SALLY N. RANDALLEvaluation of an Elective Academic Assistance Course(Under the direction of MICHELE L. SIMPSON)

This study examined an elect ive academic assistance class. Four goals guided the

study. The first goal was to investigate differences in the academic performance of

students who had completed an academic assistance course and matched students who

had never enrolled in the course. The second goal was to examine the difference in self-

regulated learning behaviors between the same two groups of students. The third goal

was to examine the perceptions held about the course by students who had completed the

course. The fourth goal was to investigate how students transferred the strategies learned

in the course to their subsequent reading-intensive courses.

Two groups of participants were studied: students who took the elective course

the first semester of their freshman year and students who were on academic probation

the semester they took the course. Data collection included academic performance

indicators accessed through the university � s student record system and two surveys and

one inventory completed by students.

Findings were analyzed by goals. First, multiple indicators of academic

performance resulted in inconclusive findings about the performance of students who

completed the course compared to students who did not enroll. Second, there seemed to

be no difference in strategic learning behaviors between the two groups on a delayed

measure. Third, students � responses indicated that they found more value in the

instructional components that focused on specific study strategies than the affective

components. Fourth, students indicated that, after a year or more, they were continuing to

use many of the strategies when they studied for their subsequent courses. An analysis of

students � strategy use and grades in their targeted reading courses indicated that students

who were able to analyze the academic task and students who implemented the strategies

on a daily or weekly basis made the highest grades.

INDEX WORDS: Course evaluation, Higher education, Developmental studies

programs, Transfer of training, Educational strategies, College

assessment outcomes, Self-regulated learning, Cognitive strategy

instruction

EVALUATION OF AN ELECTIVE

ACADEMIC ASSISTANCE COURSE

by

SALLY N. RANDALL

A.B., Emory University, 1967

M. Ed., Georgia State University, 1974

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF EDUCATION

ATHENS, GEORGIA

2002

© 2002

Sally N. Randall

All Rights Reserved

EVALUATION OF AN ELECTIVE

ACADEMIC ASSISTANCE COURSE

by

SALLY N. RANDALL

Approved:

Major Professor: Michele Simpson

Committee: Donna AlvermannMichelle CommeyrasSherrie NistSteve OlejnikDavid Reinking

Electronic Version Approved:

Gordhan L. PatelDean of the Graduate SchoolThe University of GeorgiaMay, 2002

iv

DEDICATION

To Don and the next phase of our life together.

v

ACKNOWLEDGMENTS

This dissertation was both a community and family effort and could not have been

accomplished without the talents, love, and support of many people.

Many members of my work family offered technical help as well as moral support.

Julie Segrest helped me navigate the complexit ies of the on-line student record system.

Barry Biddlecomb offered his expertise to run and sort my data. Julian Smit taught me to

use SPSS. Jodi Holschuh asked me lots of � what if � questions and helped me consider

alternative interpretations. Pat McAlexander offered her editing expertise in the final

stages of writing. Clare Connell kept me sane by reminding me to laugh and to keep the

dissertation in perspective in the broader scope of my life.

My committee members provided both challenges and support. David Reinking

challenged me to clarify my focus early in the research process, a challenge that helped me

set realistic limitations for my dissertation. Donna Alvermann and Michelle Commeyras

offered perspect ives outside of the world of Academic Assistance that helped me clarify

my thinking. They also gave me support and encouragement as family health crises

resulted in several delays. Sherrie Nist supported me through some difficult years of

balancing my job, family, and academic responsibilities. As friend, boss, and committee

member, she honored my priorities and often popped her head in my office just to offer

her encouragement. Steve Olejnik provided invaluable expertise and guidance with my

statistics so that I actually understood most of what I was doing. My work would have

vi

been impossible without the talent and loving support of Michele Simpson, my major

professor. Under her guidance, I learned a great deal about the organizat ion of research,

the technicalities of academic writing, the importance of clarity of expression, and my own

personal strengths and limitations. I cannot thank her enough for her devotion and time in

helping me write a dissertation that I believe is important to the field of Academic

Assistance.

My family was my cheering section throughout. My three children, Sarah Hall,

Michelle, Clark, and Jeanette Wilson, bolstered me with their messages of love and

support through their phone calls, visits, emails, and hugs. My three grandchildren, all

born during the dissertation process, gave me a healthy focus outside of myself, a place

where I could become lost in a world of love, laughter, and discovery. My mother, Jean

Nohlgren, helped me keep things in perspective with her constant good humor and

encouragement. Most important was the encouragement of my husband Don. He set me

on this path many years ago by challenging me to expand my career options and my

personal growth. He picked up the pieces of our home life that I let fall, and he picked me

up the many times when I was sure I could not continue.

As I write this, I realize once again how fortunate I am to be surrounded by such

caring people who have helped me reach such a milestone.

vii

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

CHAPTER

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Statement of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Purpose of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Significance of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Definitions of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Summary of Chapter One . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2 REVIEW OF THE LITERATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Theoretical Perspectives in Self-Regulated Learning . . . . . . . . . . . . . . 23

Measures of Self-Regulated Learning . . . . . . . . . . . . . . . . . . . . . . . . . 27

Post-Secondary Literacy Demands . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Transfer of Learning Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

viii

Characteristics of Effective Course Evaluation . . . . . . . . . . . . . . . . . . . 57

Recent Course Evaluation Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Summary of Chapter Two . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3 METHOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Data Collection Sources and Procedures . . . . . . . . . . . . . . . . . . . . . . . 73

Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Summary of Chapter Three . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4 RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Summary of Chapter Four . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

5 DISCUSSION, CONCLUSIONS, IMPLICATIONS, AND

RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Summary of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Discussion of the Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Implications for Educators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

Recommendations for Future Research . . . . . . . . . . . . . . . . . . . . . . . 198

Summary of Chapter Five . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

ix

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

APPENDIX A: EMAIL MESSAGE TO POTENTIAL PARTICIPANTS . . . 221

APPENDIX B: LETTER TO PARENTS OF POTENTIAL PARTICIPANTS 218

APPENDIX C: CONSENT FORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

APPENDIX D: SELF-REGULATED LEARNING INVENTORY . . . . . . . . . . 224

APPENDIX E: STUDENTS � PERCEPTIONS OF LEARNING TO LEARN 228

APPENDIX F: MOTIVATION QUESTIONS A . . . . . . . . . . . . . . . . . . . . . . 232

APPENDIX G: MOTIVATION QUESTIONS B . . . . . . . . . . . . . . . . . . . . . . 233

APPENDIX H: TRANSFER OF LEARNING TO LEARN STRATEGIES . . 234

x

LIST OF TABLES

Table 1: Demographics for Matching Participants for Fall 1998 and 1999 . . . . . . . . . . 70

Table 2: Differences Between Subjects and Controls . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Table 3: Self-Regulated Learning Inventory Scores for 1998 and 1999 . . . . . . . . . . . 111

Table 4: Ratings of Course Components as Measured by the SPLL for 1998 & 1999 . 115

Table 5: Open-Ended Responses to Annotation Instruction . . . . . . . . . . . . . . . . . . . . 116

Table 6: Open-Ended Responses to Note-Taking Instruction . . . . . . . . . . . . . . . . . . . 117

Table 7: Open-Ended Responses to Rehearsal and Test Preparation Instruction . . . . . 118

Table 8: Open-Ended Responses to Time Management Instruction . . . . . . . . . . . . . . 119

Table 9: Open-Ended Responses to Motivation Instruction . . . . . . . . . . . . . . . . . . . . 120

Table 10: Open-Ended Responses to Beliefs Instruction . . . . . . . . . . . . . . . . . . . . . . . 121

Table 11: Summary of Target Courses on which Students Reported . . . . . . . . . . . . . 126

Table 12: Transfer of Annotation Strategy to Subsequent Course . . . . . . . . . . . . . . . 128

Table 13: Transfer of Note-Taking Strategies to Subsequent Courses . . . . . . . . . . . . 132

Table 14: Transfer of Rehearsal Strategies to Subsequent Courses . . . . . . . . . . . . . . . 136

Table 15: Semester Grades of Probationary Students . . . . . . . . . . . . . . . . . . . . . . . . . 143

Table 16: Self-Regulated Learning Inventory Scores for Probationary Students . . . . 145

1

CHAPTER 1

INTRODUCTION

Literacy tasks become increasingly complex for students as they make the

transition from the academic requirements in high school to the requirements in the post-

secondary environment. Many students are at risk for failure if they do not receive

assistance. Therefore, most post-secondary institutions offer academic assistance courses

in order to support these students as they attempt to rethink their beliefs about knowledge

and learning and develop effective study strategies to meet the requirements of new

literacy tasks. In order to ensure that these courses continue to accomplish the goal of

helping students make this literacy transition and become self-regulated learners,

institutions must evaluate the impact of academic assistance courses on students � study

practices and academic performance. The evaluation of Learning to Learn, an elect ive

academic assistance course at the University of Georgia, is the subject of this study.

A search of the literature uncovers almost as many definitions for literacy as there

are experts writing on the topic of literacy. One definition that reflects the complex

literacy demands faced by post-secondary students today was proposed by Pugh, Pawman,

and Antommarchi (2000). They assert that literacy is the � ability to understand and make

use of information provided in a variety of forms � (p.25) and contend that � literacy

involves the ability to navigate purposively and critically through a network of information

connections that become denser by the day � (p. 27). This definition acknowledges the

2

multiple information formats and sources faced by post-secondary students and the need

for students to actively, independently, and critically construct knowledge and

understandings as they read, listen, synthesize multiple sources, apply what they have

learned, and make judgements. Further, these literacy demands at the post-secondary level

are a unique challenge for many students because academic literacy � cannot be fully

realized apart from both the knowledge and pedagogic demonstrations of how that

knowledge comes to be � (Anderson, Best, Black, Hurst, Miller, & Miller, 1990, p.28). In

other words, post-secondary students must adjust their understanding of the literacy task

every time they enter a new course and discover that there are differences in pedagogical

assumptions, ways of viewing knowledge, discourse practices, and assessment procedures.

They must develop situation-specific understandings of literacy tasks as they regulate

their own learning.

Despite the fact that students must always consider the context-specificity of the

literacy tasks they face, in general, the literacy demands at the post-secondary level are

more demanding and complex than they are in most high school settings (Thomas, Bol, &

Warkentin, 1991; Trigwell, Prosser, & Waterhouse, 1999). First, post-secondary courses

require a greater amount of text reading (i.e., approximately 800 pages in a ten week

quarter compared to 800 pages during an ent ire school year in high school) (Carson,

Chase, Gibson, & Hargrove, 1992; Chase, Gibson, & Carson, 1994; Murden & Gillespie,

1997). Second, post-secondary instructors assign these readings to be accomplished

independently (Orlando, Caverly, Swetnam, & Flippo, 1989) with � long retention

intervals � and few instructor checks on intermediate progress of reading and

3

comprehension (Thomas et al., p. 285). In contrast, high school teachers assign a few

pages at a time in preparation for a weekly quiz (Applebee, Langer, & Mullis, 1987;

Ravitch & Finn, 1987) and often discuss the material thoroughly in class in case students

did not read or understand (Alvermann & Moore, 1991; Sturtevant, 1996). Third, teachers

at the post-secondary level offer fewer compensations in the way of study sheets, sample

test questions, and review sessions than high school teachers do (Chase, Gibson, &

Carson, 1993). Fourth, in post-secondary courses, grades often depend almost entirely on

a few major exams (Carson et al.; Simpson & Nist, 1997a), whereas in high school,

teachers often average homework completion, project grades, many small quizzes, and

possible retests when they compute grades ( Hinchman & Zalewski, 1996; Thomas et al.).

Finally, in post-secondary classes, a high level of critical thinking is required in contrast to

the typical memorization tasks found in high schools. For example, in post-secondary

institut ions, exams are likely to measure an understanding of scientific processes, the

integration of material from multiple sources, the application of concepts to new contexts,

and an ability to make sound judgements (Carson et al.). These post-secondary exams

often appear in a recall format such as essay or short answer, requiring what Thomas and

his colleagues refer to as � extended production � (p. 282). In contrast, high school tests

often require only memorization of information (Applebee et al.), usually in the form of a

� low-level reproductive response � (Thomas et al., p. 283). Strage, Tyler, Rohwer, and

Thomas (1987) found a � dramatic increase � in the demand for integration and a decrease

in the amount of verbatim memorization at the post-secondary level (p. 294).

4

From these comparisons, it is apparent why so many high school students have

serious difficulty making the transition to the literacy demands of the post-secondary

academic environment. Students must learn new methods for managing the increased level

of literacy demands and must be able to do so independently because of the decreased

level of teacher supports. This transition to post-secondary academic work can be

especially difficult for some populations. For example, within any entering class at every

post-secondary institution, students fall on a continuum in terms of prior academic

achievement, ability, and predicted success. Students at the low end of that range at any

particular university are predicted to perform less well academically than their peers at the

higher end of those continua and are at risk for academic difficulty and possible failure.

Additionally, an increased open enrollment, the increased availability of scholarships based

on high school performance, and an increased pool of need-based loans have opened post-

secondary institutions to many first-generation college students. Research has shown that

these first-generation college-bound students often struggle with college literacy demands

(Commander & Smith, 1995; Martin & Arendale, 1994).

In response to the need to help these students make a successful and smooth

transition from high school to post-secondary work, most post-secondary institutions have

established academic assistance programs. The major purpose of these programs is to

enable students to become self-regulated learners ( Zimmerman, 2000) in order for them

to independently and successfully manage the academic demands in this new educational

environment. For the purposes of this study, such programs will be called academic

assistance programs, although they are also referred to in a variety of ways such as

5

learning support, developmental studies, and remedial reading. Academic assistance

programs usually offer a range of services such as tutoring, workshops, and courses;

however, this study will focus only on one course. Academic assistance courses, such as

Learning to Learn, serve a wide variety of students: (a) those with low college entrance

scores, (b) those in good academic standing who are having difficulty in a particular

course, (c) those in good standing who do not feel adequately prepared for the transition

to the more difficult literacy tasks, and (d) those with chronic academic problems, such as

those on probation (Casazza & Silverman, 1996).

In order to ensure that their course offerings are meeting students � transition

needs, academic assistance professionals must carry out ongoing and systematic research

that will evaluate the impact of their courses (Boylan, 1997; Casazza & Silverman, 1996;

O �Hear & McDonald, 1995). These research findings can provide academic assistance

professionals with the knowledge they need to implement course modifications and

improvements in order to increase the likelihood that academic assistance courses will

address the changing needs of students.

Statement of the Problem

It has only been in the last two decades that systematic research evaluating the

impact of specific courses has occurred within academic assistance programs. Boylan,

Bliss, and Bonham (1997) reported that even in the late 1990s, only 25% of four-year

institut ions completed ongoing and systematic evaluation of academic programs, including

course evaluation. Hence, there are only a few reports in the literature that might provide

suggestions for institutions as they develop their own course evaluation research efforts.

6

Additionally, much of the course evaluat ion research that does exist has serious limitations

in design and usefulness (Mealey, 1991; Nist & Simpson, 2000) and is often � cursory �

(Maxwell, 1997). According to these researchers, there are five limitations to the existing

literature.

The first limitation is that research reports found in the literature usually fail to

make explicit the theoret ical grounding of their course offerings and, therefore, of their

research efforts. O �Hear and McDonald (1995) suggest that the limited theoret ical base in

the field of academic assistance education is one reason there are few quality reports in the

literature. Without a theoretical grounding to guide decisions about curriculum, course

components may be piecemeal, rather than having a logical coherence, and may differ with

individual instructor �s personal interests. Therefore, the quality and value of evaluation

research for such academic assistance courses would be questionable. The theoretical

assumptions should not only drive the development of course goals and curriculum but

also the quest ions, methodology, and data analysis for course evaluation research.

Second, reports of research on the impact of academic assistance courses on

students � academic performance rarely include a discussion of predicted performance of

individual students as a baseline for evaluation. This is important because measures of

predicted performance such as individual student �s predicted freshman average take into

account multiple variables that include the level of challenge at different high schools, the

difficulty of high school courses taken by the student, the student � s high school grade

point average, and the student �s Scholastic Aptitude Test (SAT) scores (M. DeMaria,

Assistant Director of Admissions, the University of Georgia, personal communication,

7

June 22, 2000). These indicators form a composite picture of how a student is likely to

perform at a particular university that is more accurate than just SAT scores alone.

Research designs that examine average performance across groups of students, rather than

measuring academic performance of individual students in relation to their potential to

excel, are less likely to uncover the possible impact of an academic assistance course.

A third limitation in course evaluat ion research is the failure to ask student

stakeholders for their perceptions. Students may have insight into their academic problems

that academic assistance professionals cannot understand without hearing from the

students themselves. Neither can academic assistance professionals, especially in a large

university setting, be aware of all of the disparate literacy demands found in hundreds of

courses, compounded by the teaching philosophies and methods of many different

instructors. Consequently, it is crucial to assess students � perceptions of the value of an

academic assistance course in relation to the constantly changing literacy tasks they face in

subsequent courses (Maxwell, 1997; Simpson, Hynd, Nist, & Burrell, 1997). It is also

important to know which components of a particular academic assistance course have the

most impact on students and how effect ively students perceive that they can transfer the

strategies to their other courses. This student information would add to ongoing needs

assessment, a necessary part of course evaluation and modification (Elifson, Pounds, &

Stone, 1995).

Fourth, many of the existing research efforts in course evaluation do not employ

comprehensive and sensitive indicators of students � academic success. Experts in the field

recommend that researchers use a constellation of dependent variables that demonstrates

8

the impact of academic assistance courses on students � continuing academic performance

(Gebelt, Parilis, Kramer, & Wilson, 1996; Simpson, et al., 1997). These measures often

include the following: (a) grades in subsequent courses (Keimig, 1983), (b) overall

retention rate (Boylan, 1997; Casazza & Silverman, 1996; Dembo & Jakubowski, 1999;

Dubois, Dennison, & Staley, 1998; Gebelt, et al.), (c) cumulative grade point average

(Casazza & Silverman; Dembo & Jakubowski; Dubois et al.), (d) institutional academic

standing after completing the academic assistance course (Hennessey, 1990), and (e)

credits attempted and credits earned in regular content areas (Wilcox, del Mas, Stewart,

Johnson, & Ghere, 1997). However, studies are usually limited to the examination of only

one or two of these variables, rather than an analysis of a more comprehensive

constellation of variables that might provide a more complete picture of academic

performance. Further, Keimig argues that designs that use only one measure, such as

GPA, are misleading because there are so many confounding variables that enter into GPA

besides one or two academic assistance courses. An exception to this t rend of examining

just one quantitative variable can be found in the work of Weinstein and her colleagues

who compared their academic assistance students to non-academic assistance students

using retention, graduation rate, cumulative GPA, course hours failed, and course hours

passed (Weinstein, Hanson, Powdrill, Roska, Dierking, Husman, & McCann, 1997).

A final limitation is the fact that most course evaluation studies do not seek

qualitative data that might explain many of the quantitative findings. For example, there

are so many reasons for student retention or attrition that a mere percentage rate, as found

in many reports, does not reveal the multiple variables that lead to a student �s decision to

9

leave or stay at their institution (Bean, 1985). Open-ended responses from students might

help researchers learn about the intensity and interaction of these variables. Another

example of this limitation is found with instruments designed to yield a level of course

satisfaction. Unless the instrument queries students about the reasons behind their ratings,

the results cannot be used to modify and improve programming. Qualitative measures are

one way to get at this information, yet such measures are rarely found in the literature.

Purpose of the Study

The University of Georgia has become increasingly competitive since the

inauguration of the HOPE scholarship program in 1993 when large numbers of capable

students who could not afford a college education were provided with the financial

resources to attend. Consequently, there is a need to offer support to all students,

especially entering freshmen making the transition from high school and continuing

students who are at-risk academically, such as those on probation and those who request

assistance. Therefore, the purpose of this study was to evaluate the effectiveness of

Learning to Learn, an elective academic assistance course within the Division of

Academic Assistance at the University of Georgia. In order to accomplish this purpose,

data were collected on the academic achievement and self-regulated learning behaviors of

students who completed Learning to Learn and their matched controls who never elected

to enroll in the course. In addition, for students who completed Learning to Learn, self-

report data were collected to assess students � perceptions of the course and their transfer

of strategy usage.

10

Learning to Learn is one component of the Division �s overall attempt to meet its

teaching mission to enhance the success of entering and continuing students at the

University of Georgia. This elective course is designed to enable students to effectively

meet the literacy demands faced in their college courses by teaching act ive reading and

study strategies and the underlying cognitive and metacognitive processes. Students are

taught the importance of careful planning and deliberation as they read and gather

information, isolate main ideas and make judgements about which supporting information

is key, reorganize information in their own words, reduce the amount of material to be

learned, and monitor their own learning and apply fix-up strategies as needed. They are

taught how to analyze the reliability of sources, how to synthesize material from several

sources, how to make generalizations, and how to apply their prior knowledge to add to

existing schemas or create new schemas. Based on Jenkins �s Tetrahedral Model of

Learning (1979), students are taught how to analyze the academic task. To do so they

must understand the requirements of the assessment task, the nature of the required

materials, and their own characteristics as a learner. They must then use this understanding

to select and modify the appropriate study strategies necessary to accomplish the task.

Ultimately, Learning to Learn is designed to help students learn to regulate independently

these processes of active reading and studying in their other university classes.

To be reasonably confident that the instructional emphases in Learning to Learn

are satisfying the mission of the Division and meeting student needs, ongoing,

comprehensive, and systematic course evaluation research is necessary. Thus, the general

goals of this study were to investigate differences in academic performance of students

11

who had completed Learning to Learn and their controls, to examine differences in the

self-regulated learning practices of the same two groups, to assess students � perceptions of

the usefulness of the skills learned in Learning to Learn, and to investigate how and to

what extent Learning to Learn students transfer those strategies to subsequent courses.

Significance of the Study

Evaluation research, including course evaluation, is no longer a luxury but a

necessity for academic assistance programs at the post-secondary level (Breneman &

Haarlow, 1999; Boylan, 1997; Elifson, et al., 1995). Hence, the findings of this study

should be useful to the Division of Academic Assistance at the University of Georgia;

moreover, the findings should offer practical suggest ions to institut ions that are designing

or refining their own course evaluation efforts.

This study is significant for four reasons. First, the results from this study provide

institution-specific data to faculty within the Division of Academic Assistance, data that

may guide them as they modify Learning to Learn or design and implement new courses.

Programs such as those offered by the Division of Academic Assistance should be

accountable to students who pay tuition and spend valuable study time reading, studying,

and trying out strategies while in the course. Such courses advert ise instruction and

assistance in learning effective and efficient study skills, so the academic assistance

instructors have a responsibility to students to evaluate whether or not those goals are

accomplished as the literacy demands on campus change.

Second, there is a current emphasis on fiscal accountability in public higher

education (Boylan, 1997; Breneman & Haarlow, 1999; Elifson, et al., 1995; Rossi,

12

Freeman, & Lispsey, 1999). The findings of this study may provide the basis for funding

decisions at the University of Georgia, an institution that has many demands for limited

funds. This is especially important for academic assistance programs because, as Keimig

reports (1983), there are some instructors and administrators who believe that academic

assistance programs are merely a crutch for weak students and are not appropriate at the

university level. Additionally, according to Breneman and Haarlow, other instructors and

administrators believe academic assistance programs are too costly. Because

administrative decisions about continuation, limitation, or expansion of educational

programs reflect power positions within a university community (Greene, 1994; Mealey,

1991), positive results from systematic course evaluation research can provide

administrators of academic assistance programs greater credibility and authority as they

justify the existence of their courses by demonstrating their effectiveness. Therefore, it has

become more essential at the University of Georgia, as well as at other public institutions,

to find the research designs that most accurately reflect the impact of the academic

assistance courses.

Third, this study includes the following qualitative and quantitative elements that

are rarely found in the existing literature: (a) multiple indicators of students � academic

performance, (b) a consideration of individual predicted performance, (c) students �

perceptions of their academic assistance course, and (d) data on the transfer and

modification of active reading and studying strategies. Unlike many course evaluation

research efforts, this study used multiple indicators of student academic success that

reflected performance over the course of three semesters. Additionally, this study took

13

into consideration the predicted performance of students, resulting in a more accurate

picture of the impact of the Learning to Learn course in relation to the expectations for

success for individual students. This study also assessed the perceptions of students who

have taken the course, because students comprise the group with the most at stake.

Feedback and suggestions are often solicited from students at the end of a term as a part

of a course evaluation. However, academic assistance programs rarely query students

about the continued usefulness of the course content several semesters later as students

continue using the strategies in other courses. This study also examined the issue of

strategy transfer. Academic assistance courses are designed to enhance the success of

students in subsequent classes, but they rarely examine how students transfer the learning

and study skills they acquired.

Finally, and most importantly, this research study is significant because it is

grounded in theory. Because the Learning to Learn course at the University of Georgia is

based on self-regulation theory, the research questions and the instrumentation mirror that

theory (McCombs, 1986; Weinstein et al., 1997; Zimmerman, 2000). The goal of

Learning to Learn is to enhance the ability of students to regulate both their

cognitive/metacognitive behaviors and their � self-system � (McCombs) in order to meet the

demands of a range of academic environments toward the attainment of their personal

academic goals (Garcia, 1995; Zimmerman). Exist ing studies of the impact of academic

assistance courses have typically examined indicators of students � academic performance,

but few have conducted this research within the context of a theory of studying and

learning.

14

Research Quest ions

Based on a review and synthesis of the relevant literature on course evaluation in

academic assistance programs and self-regulated learning, the following research quest ions

about Learning to Learn at the University of Georgia directed this study. The research

questions are organized by the four goals that guided this study.

Goal One: To Examine Academic Performance

1. Is there a difference between the academic performance of regularly admitted

first-semester freshmen who completed Learning to Learn during fall semesters 1998 and

1999 and the academic performance of regularly admitted first-semester freshmen who did

not elect to take the course?

2. Did the academic performance of probationary students change after completion

of Learning to Learn during fall semesters 1998 and 1999?

Goal Two: To Examine Self-Regulated Learning

3. Is there a difference between the reported self-regulatory practices of regularly

admitted first-semester freshmen who completed Learning to Learn during fall semesters

1998 and 1999 and the reported self-regulatory practices of regularly admitted first-

semester freshmen who did not elect to take the course?

4. What are the reported self-regulatory practices of probationary students who

completed Learning to Learn during fall semesters 1998 and 1999?

5. Is there a relation between students � reported self-regulatory practices and their

academic performance?

15

Goal Three: To Examine Students � Perceptions about Learning to Learn

6. Which components of the Learning to Learn curriculum do students report

helped them successfully meet the literacy demands of their subsequent courses and

regulate their own learning processes?

7. What suggestions do students have for additions or omissions to the Learning

to Learn curriculum?

Goal Four: To Investigate the Transfer of Strategy Use

8. Do students transfer the literacy strategies taught in Learning to Learn to the

active reading, note-taking, and rehearsal/test preparation required in subsequent courses

that have a heavy reading load?

Assumptions

There were several assumptions inherent in this study. First, the researcher

assumed that the quantitative measures provided an accurate assessment of students �

academic performance. Second, the researcher assumed that students � scores on the Self-

Regulated Learning Inventory (Gordon, Lindner, & Harris, 1996) accurately represented

those self-regulatory processes that were involved in their independent learning at the

post-secondary level. Third, the researcher assumed that students could and would

accurately report their perceptions about the usefulness of the strategies taught in

Learning to Learn to their current studies and their perceptions about how they

transferred and modified those strategies in subsequent courses.

16

Limitations

There were a few limitations to this study. First, the complexity of the University

of Georgia environment makes it very difficult to determine what combination of variables

have an impact on students � study habits and academic performance. Concurrent

experiences outside of the Learning to Learn course impact the intellectual growth and

academic maturity of students. Students learn how to cope with college academics within

the context of regular classes, how to manipulate the college environment from advice of

their peers, and how to seek out multiple sources of support such as tutoring, peer study

groups, and individual help from instructors. Therefore, it is clear that changes in

performance that occur after students complete Learning to Learn are not just a result of

that course intervention. Also, the complex components that make the Learning to Learn

course a unique experience (e.g., the hidden curriculum of collaboration with peers, a

more personal student/faculty relationship within a small class, and an increased likelihood

of success in a carefully paced curriculum) add confounding variables to any assessment.

A second limitation is due to the significant variation in course difficulty among

various departments on campus. Ideally, this kind of research would match students on the

difficulty level and the assessment practices of their subsequent courses. However, within

a university of such diverse course offerings as the University of Georgia, this was

impossible to accomplish. This study part ially solved this problem by examining grades in

heavy reading courses that students took after completing Learning to Learn rather than

just global measures of GPA.

17

A third limitation was the motivation factor in student performance. It may be that

students who use any of the services of an academic assistance program, including taking

courses such as Learning to Learn, may be more mot ivated to improve their academic

performance than students who do not (House & Wohlt, 1990). However, this is not

necessarily true. Students may not be able to enroll in academic assistance courses such as

Learning to Learn because of schedule conflicts (Mart in & Arendale, 1994), because the

Learning to Learn courses are full (S. L. Nist, Director, the Division of Academic

Assistance, personal communication, August, 28, 2000), or because they need other credit

hours toward graduation. This study partially addressed this problem by querying students

about their reasons for taking or not taking Learning to Learn.

Fourth, when measuring the impact of a course such as Learning to Learn, a

course that is designed to change academic behaviors, distal measures may be necessary

because significant change is a long-term process. However, at the University of Georgia,

we are trying to set up a system of ongoing course evaluation research for the future as

well as the present. Because we converted to the semester system in fall 1998, it seemed

appropriate to begin this data collection at that point in t ime. Future data collection will

include more distal measures as the students who began in fall 1998 reach their junior and

senior years.

A fifth limitation exists because the study was dependent on students to volunteer

to complete part of the research. The researcher was able to gather archival data on all of

the Learning to Learn students, their matched controls, and the available probationary

Learning to Learn students. However, survey and inventory data were collected only for

18

the students who were willing to spend an hour of their valuable time to part icipate. It is

difficult to assess their motivation to participate and to know what, if any, relation exists

between that motivation and their responses on the surveys. The question remains, as it

does with any research that uses volunteers, was there a significant difference between

those who volunteered and those who did not that would affect the results of the research?

A final limitation is related to the well-documented problems with self-report data.

According to Garner (1988), there are three problems with such data that may have

affected the responses of the participants. First, self-reports are often incomplete, so

students � responses may not have provided an accurate record of all the strategies they

actually tried. Second, students may not be able to accurately assess the cognitive and

metacognitive processes they used in the past. Finally, students may have reported what

they perceived they should have done rather than what they actually did. The design of this

study should have reduced the possibility of this third problem because none of the

students in the study had a prior relationship with the researcher, nor was any future

relationship anticipated.

Definit ion of Terms

Academic assistance: a generic term that refers to a wide range of services (e. g.,

both elective and mandatory courses, tutoring, adjuncts, and learning centers) that are

designed to support students in their academic pursuits.

Academic probation: the status of any student who has a cumulative grade points

average of 2.0 or less at the end of any semester (Office of Undergraduate Admissions,

2000-2001).

19

Academic self-efficacy: students � perceptions of their academic ability that are

based primarily on school performance (Marsh, 1990).

Academic standing: the academic status of students that is determined at the end

of each term; these include Dean �s List, good standing, probation, and dismissal (Office of

Undergraduate Admissions, 2000-2001).

Adjusted High School Grade Points Average (AHSGPA): a weighted GPA that

includes grades in all academic subjects considered by the Office of Admissions and a

weight that adjusts for the difficulty level of different high schools.

Compensations: teacher actions (e.g., providing students with actual test

questions, conducting in depth review sessions, or giving students lists of concepts to be

learned before testing) that reduce the net academic demand required of students (Curley,

Estrin, Thomas, & Rohwer, 1987).

Epistemology: beliefs about the nature of knowledge and learning (Schommer,

1990, 1993).

Learning to Learn: an elective Academic Assistance course that is open to all

UGA students that is designed to enhance students � active reading, note-taking, and test

preparation/rehearsal skills.

Literacy demands: the quality and quantity of tasks that students must accomplish

in order to be successful academically, including the amount of information presented, the

reading level of the required material, and the amount of synthesis required (Curley, et al.,

1987).

20

Predicted freshman grade point average (PFGPA) : a measure of predicted

performance used at the University of Georgia that takes into account multiple variables

such as level of challenge at different high schools, the difficulty of specific high school

courses taken by individual students, individual high school grade point averages, and

individual Scholastic Aptitude Test (SAT) scores (M. DeMaria, Assistant Director of

Admissions, the University of Georgia, personal communication, June 22, 2000).

Self-regulated learning: in education, refers to the regulation of both

cognitive/metacognitive behaviors and the � self-system � (McCombs, 1986, p. 314) in

order to meet the demands of a range of academic environments toward the attainment of

personal goals (Zimmerman, 2000).

Stakeholders: the types of audiences or constituencies (Somers, 1987) that have a

stake in the results of the research and the resultant program implementation (Greene,

1994; Payne, 1994; Rossi, et al., 1999); includes faculty, staff, and administrators both

within and outside of the academic assistance program, state and federal officials, and

students.

Supports: teacher act ions that enable students to independently manage the

academic demands (Curley, et al., 1987), such as modeling of the thinking process,

providing directions of expectations for papers, and explaining the format of test

questions.

Tetrahedral Model of Memory: a four point interactive model of learning that

suggests that the learning process requires that students, in light of their own personal

characteristics, skillfully juggle multiple variables that change with each new learning

21

environment; these variables include the criterial task, the type of materials that must be

understood, and the selection of appropriate learning strategies (Jenkins, 1979).

Transfer: the � effect of learning on a different performance or context � (Salomon

& Perkins, 1989, p.116).

Summary of Chapter One

The purpose of this study was to evaluate the effectiveness of one elective course,

Learning to Learn, within the Division of Academic Assistance at the University of

Georgia. The four major areas of interest were students � academic achievement, their self-

regulated learning behaviors, their perceptions of the usefulness of Learning to Learn, and

their ability and inclination to transfer strategies learned in Learning to Learn as they

studied for their subsequent courses.

Organization of the Dissertation

Chapter 2 presents a review of the literature relevant to the study. Chapter 3

describes the participants of the study, the instrumentation used, and the data collection

and analysis procedures. Chapter 4 presents the results of the data analyses. Chapter 5

concludes the dissertation with a summary of the findings, the conclusions, implications

for educators, and recommendations for future research.

22

CHAPTER 2

REVIEW OF THE LITERATURE

The purpose of this chapter is to review the literature relevant to this study. This

discussion is divided into six sections: (a) theoretical perspectives on self-regulated

learning, (b) measures of self-regulated learning, (c) post-secondary literacy demands, (d)

transfer of learning strategies, (e) characterist ics of effective course evaluation, and (f)

recent course evaluation efforts.

This literature review made use of several search procedures. First, the following

data bases were searched: ERIC, Current Contents, and Educational Abstracts. The

following descriptors were used to locate articles: course evaluation, higher education,

developmental studies programs, transfer of training, educational strategies, college

outcomes assessment, self-regulated learning, self-directed learning, and cognitive strategy

instruction.

Second, hand searches were conducted for the last ten years in journals with a

focus relevant to the research. The following journals that deal with academic assistance

programs were searched: Journal of Developmental Education, Research in

Developmental Education, Journal of Higher Education, Research in Higher Education,

College Student Journal, and Journal of College Student Development. The following

journals that deal with literacy and the psychology of learning were also searched:

Educational Psychology Review, Journal of Adolescent and Adult Literacy, Reading

Research Quarterly, Journal of Educational Psychology, American Educational

23

Research Journal, Educational Research Quarterly, Contemporary Educational

Psychology, and Journal of Literacy Research.

Two final steps were taken. The references in the most recent articles and books

were used as a guide to the location of other seminal pieces in edited books and scholarly

journals. Finally, a professor of educational measurement was consulted for the most

current readings in evaluation.

Theoretical Perspectives on Self-Regulated Learning

Self-regulated learning theories provide the theoretical foundation for the Learning

to Learn curriculum at the University of Georgia, as well as for the theoretical perspective

for this research. In the introduction to their new handbook on self-regulation, Boekaerts,

Pintrich, and Zeidner (2000) maintain that � self-regulation is a very difficult construct to

define theoretically as well as operationalize empirically � (p.4). These authors explain that

the concept is relatively new in the educational literature, appearing first in the 1980s and

1990s. Self-regulation is a concept that is now used in many different fields, including

health psychology, social psychology, clinical psychology, and education.

The concept of self-regulation is most easily operationalized with descriptors of

the characteristics and behaviors of self-regulated learners. In general, self-regulated

learners are those learners who are � purposive and goal oriented (proactive rather than

simply reactive), incorporating and applying a variety of strategic behaviors designed to

optimize their academic performance � (Lindner & Harris, 1992). Across theoretical

perspectives, self-regulated learners are described as sharing the following characteristics:

1. They are flexible in their approaches to learning, using a variety of strategies

(Lindner & Harris, 1998; Zimmerman & Martinez Pons, 1986).

24

2. They are metacognitively aware (Garcia, 1995; Pintrich, Smith, Garcia, &

McKeachie, 1993), constantly monitoring and evaluating their learning and the underlying

cognitive processes ( Lindner & Harris, 1998; Pintrich et al.; Weinstein et al., 1997;

Zimmerman, 1998a).

3. They demonstrate self-control (Lindner & Harris, 1998) and personal agency

(Zimmerman, 1998a).

4. They respond to contextual demands (Lindner & Harris, 1992; Weinstein et al.,

1997; Zimmerman, 1998a) and are � finely tuned to situational demands � (Gordon,

Lindner, & Harris, 1996, p. 63).

5. They are proactive (Lindner & Harris, 1998; Zimmerman, 1998a).

6. They are goal-directed (Carver & Scheier, 2000; Lindner & Harris, 1998;

Pintrich et al., 1993; Weinstein et al., 1997; Zimmerman, 1998a).

8. They are internally motivated (Gordon, et al., 1996; Lindner & Harris, 1998;

Weinstein et al., 1997; Zimmerman, 1998a).

Three authors have proposed models of self-regulated learning that have specific

relevance to secondary and post -secondary learning: McCombs (1986), Weinstein et al.

(1997), and Zimmerman ( 1998b, 2000). McCombs suggested a preliminary recursive

model that focused on the � self-system in self-regulated learning � (p. 314). She proposed

that self-concept, self-esteem, and self-efficacy are the parts of the self-system that

underlie self-regulation. She suggested that students cannot be self-regulatory unless they

perceive themselves as having personal control over their learning and the competency to

accomplish the learning tasks. McCombs maintained that developmental factors and prior

experiences have a significant impact on this � positive affect and motivation to assume

personal responsibility for learning and to engage in self-regulated learning activities �

25

(p. 324). She speculated that this self-system is very adaptive and in the business of self-

preservation. However, she also proposed that the development of the self-system and the

consequent ability to self-regulate is recursive and reciprocal so that there can be continual

changes in self-perceptions as a person experiences success and control. This is the

encouraging news for academic assistance instructors who attempt to structure successful

experiences by using a small and supportive classroom environment for students as they

begin to meet more difficult literacy tasks.

The Model of Strategic Learning was proposed by Weinstein et al., 1997).The

authors conceived of self-regulation as involving the skill, the will, and the self-regulatory

behaviors of students as they navigate through the mediating environmental factors such

as teacher beliefs/expectations, the nature of the learning task, the social context, and the

available resources. The skill factor actually encompasses both knowledge (e.g., prior

knowledge and knowledge of the self as a learner) as well as skill (e.g., finding main idea

and problem solving skills). Will includes attributes such as intentions, beliefs, and goal

sett ing. Self-regulation includes factors such as monitoring, time management, and

motivation management. This model reflects the complexity and interactive nature of

strategic learning and forms the theoretical basis for a learning skills course taught at the

University of Texas at Austin.

Zimmerman (1998b, 2000) proposed a cyclical model of self-regulated learning in

which self-regulation emerges from the social, environmental, and personal conditions that

determine a student �s behaviors. He asserted that self-regulation is not a mental ability but

that it is a multifaceted, self-directive process involving cognitive, emot ional, social,

behavioral, and contextual factors. This social-cognitive perspective proposes a recursive

model consisting of the three phases of forethought , performance, and self-reflection.

26

The first phase, forethought, includes three self-motivational beliefs that affect

students � behaviors and two sub-processes. First, students who have high self-efficacy

beliefs that lead them to believe they will accomplish the outcome they desire are likely to

set higher goals and choose effective strategies. Second, students whose beliefs focus

them on learning goals and individual progress rather than competitive outcomes are more

likely to persevere at the learning task. Third, students who believe in and maintain

intrinsic interest in the academic task are more likely to accomplish their learning goal.

Based on these three beliefs, self-regulated students conduct a task analysis that employs

the two sub-processes of goal setting and strategy selection in order to meet those goals

(Zimmerman, 1998b).

The second phase in the cycle is performance or volitional control (Zimmerman,

1998b, 2000). At this stage in the cycle, students focus attention on the task, protecting

themselves from distractions. They self-instruct by telling themselves how to approach and

accomplish a learning task, using self-verbalization and imagery. Finally, they provide

themselves feedback by experimenting and then observing and recording their

performance.

The last phase of Zimmerman �s recursive model of self-regulated learning ( 1998b)

is the self-reflection stage that involves making self-judgements and reacting to those

judgements. Self-regulated students evaluate their performance based on some external

criteria or their own personal goal. Self-regulated learners also attribute their performance

to � correctable causes � (p. 5), ones over which they have control and can change. For

example, attributions to strategy usage encourage flexibility and a renewed effort. Finally,

students measure their self-satisfaction and continue the cycle. At this point students must

remain adaptive and flexible because constant recycling is usually necessary to reach

27

success, and self-regulated students are continuously reaching for a greater challenge. If

students become defensive at this point because of external attributions, the processes of

goal setting and strategic planning shut down.

In sum, the theories of McCombs (1986), Weinstein et al. (1997), and Zimmerman

(1998b, 2000) hold promise for educators because they propose that these self-regulatory

skills can be developed in the classroom through modeling and carefully sequenced

academic successes. Students can be guided through self-evaluation and deliberate self-

reflection at incremental stages and be provided with a succession of experiences that have

the potential to enhance the development of the self-regulatory behaviors. Zimmerman

suggested that students first develop a measure of independent self-control within a

carefully structured setting and eventually master the � adaptive use of skills across

changing personal and environmental conditions � (Zimmerman, 2000, p. 29).

Measures of Self-Regulated Learning

Only a few measures of self-regulated learning can be found in the literature

(Gordon, et al., 1996; Pintrich et al., 1993; Weinstein, Schulte, & Palmer, 1987;

Zimmerman & Martinez Pons, 1986). Although they use different terminology, the

instruments discussed herein measure the following three components of self-regulated

learning: cognitive processing, executive processing, and environmental control. Several

of them also measure the motivation component of self-regulated learning. The major

limitation of these four instruments is the self-report format. None of the measurements

actually observes students as they work through the process of self-regulation in relation

to academic performance.

An early instrument for measuring self-regulated learning is The Self-Regulated

Interview Schedule (SRIS) developed by Zimmerman and Martinez Pons in 1986. This

28

was a structured interview with an open-response format aimed at assessing the self-

regulated learning strategies of high school students. The authors defined self-regulated

learning strategies as � actions directed at acquiring information or skill that involve

agency, purpose (goals), and instrumentality self-perceptions by a learner � (p. 615). This

early measure identified only strategic behaviors and omitted the affective components of

self-regulation such as motivation, beliefs, and attributions.

The administration of the SRIS involved the presentation of a scenario of an

academic task to a student and a query about how the student would complete the task

presented in the scenario (Zimmerman & Martinez Pons, 1986). The researchers used the

existing literature as a guide to isolate fourteen categories of strategies, such as self-

evaluation, monitoring and keeping records, seeking social assistance, goal sett ing and

planning, and environmental structuring. These fourteen categories were used for scoring

students � responses.

Three scores were obtained on the SRIS : (a) strategy use, a dichotomous score

indicating if the strategy had or had not been used; (b) strategy frequency, the number of

contexts in which a strategy was mentioned; and (c) strategy consistency, a numerically

weighted score based on the frequency of usage. The three behaviors most highly

correlated with academic achievement and their correlation coefficients were as follows:

seeking information (.37), keeping records and monitoring (.33), and organizing and

transforming (.31).

Another widely used measure of self-regulated learning for college students is the

Learning and Study Strategies Inventory (LASSI) developed at the University of Texas at

Austin (Weinstein, et al., 1987 ). On the LASSI, students respond to items that describe

the following ten learning and studying components: attitude, motivation, anxiety, time

29

management, concentrat ion, information processing, finding main idea, use of study aids,

self-testing, and test taking. The LASSI uses a 5-point Likert-type scale for which students

respond on a range from a (not at all like me) to e (very much like me). For each of the

ten components, students receive a score that compares their responses to the responses

of the successful college students on whom the instrument was normed. No total score is

computed. Statistical information is provided in the LASSI manual. Coefficients for

internal consistency ranged from .85 for time management to .72 for information

processing; however, no information is provided for validity of the LASSI.

A recent examination of the LASSI by Olejnik and Nist (1992) confirmed that the

instrument is multi-dimensional, supporting the authors � decision to include only individual

sub-scale scores and no total learning strategy score. Olejnik and Nist � s factor analysis

determined that the ten components actually group into three constructs: (a) effort-related

activities, (b) goal orientation, and (c) cognitive activities. They found close overlap

between effort-related and cognitive activities but little association of goal orientation with

the other two.

The Motivated Strategies for Learning Questionnaire was developed over a ten

year period at the University of Michigan for use with college students (Pintrich et al.,

1993). As its t itle suggests, it assesses the use of learning strategies and the underlying

motivational factors. The motivat ional section assesses students � value beliefs, goals, and

test anxiety. The strategies section measures students � use of cognitive and metacognitive

learning strategies such as rehearsal, elaborat ion, and paraphrasing. The design is a 7-point

Likert-type scale with responses that range from 1 (not at all true of me) to 7 (very true of

me). The authors report the internal consistency as � robust � (p.808) with alpha scores

ranging from .52 to .93. However, they reported that they were not surprised to find only

30

� modest � (p.812) positive correlations of sub-scales with academic performance � given

the many other factors that are related to college course grades that are not measured by

the MSLQ � (p. 812). Correlations with the grade in one course ranged from -.27 to.41.

One more recent measure of self-regulated learning is the Self-Regulated Inventory

by Gordon et al. (1996). This self-report instrument was specifically designed for college

students, both undergraduate and graduate. It is based on a model of self-regulated

learning that includes the following four factors: (a) cognitive processing, (b) executive

processing, (c) motivation, and (d) environmental utilization and control. During cognitive

processing, students are engaged in an automatic or habitual processing that includes

focusing attention, storing and retrieving information, and elaborating. During executive

processing, students are involved in more conscious and deliberate metacognitive

processes such as task analysis, strategy construction, and evaluation of learning. The

motivation factor is influenced by students � self-efficacy beliefs, attributional style,

epistemological beliefs, and learning and goal orientation. During environmental

utilization/control, students engage in help-seeking behaviors, time and setting

management, and the use of resources and available supports in the academic

environment. Because this measure was used in this study, it is discussed in more detail in

Chapter 3.

Self-regulated learning is a difficult construct to define. Researchers can observe

student behaviors but it is more difficult to access the cognitive and metacognitive thought

processes behind the behaviors. The use of self-report instruments is a limitation within

this field of research (Garner, 1988). To more thoroughly understand self-regulatory

behaviors and the underlying thought processes would require a procedure such as a

think-aloud. Think-aloud procedures are very time-consuming and the very act of

31

verbalizing may alter a person �s thinking and, therefore, responses (Payne, 1994).

However, researchers must continue to seek clarification about the characteristics of self-

regulated learning because such an awareness is critical to understanding the behaviors

post-secondary students must master in order to meet the literacy demands of the college

environment.

In sum, there are only a few existing instruments that measure self-regulated

learning, and they are all self-report instruments. These instruments attempt to measure

four basic elements of self-regulated learning: cognitive processing, executive processing,

environmental control, and motivation. An understanding of the difficult literacy demands

of the post-secondary learning environment demonstrates the necessity for post-secondary

students to become self-regulated learners.

Post-Secondary Literacy Demands

Many beginning post-secondary students quickly realize that the literacy demands

they encounter in college are far different from the demands they had experienced in high

school. In contrast to high school, college students find that not only must they

accomplish tasks that require a more complex level of cognitive processing, but they must

do so independently as they monitor and regulate their own performance. In fact, they

must become self-regulated learners. This section presents a review of the literature that

outlines some of the important differences in literacy demands that students face during

their transition to college. First, a theoret ical discussion of academic task provides a

framework for evaluating the significance of those differences. At the same time, the

relationship between the level of task demand and students � adaptive behaviors is

explored. Second, an explanation of the specific differences in the use of texts, the reading

32

load, and the assessment tasks illustrates the academic challenges that beginning college

students encounter.

Theories on Academic Task

In their seminal work, several researchers have explored the concept of academic

task (Christopoulos, Rohwer, & Thomas, 1987; Curley, et al., 1987; Doyle, 1983; Strage,

et al., 1987; Thomas, Iventosch, & Rohwer, 1987). Doyle � s theory of academic work and

Thomas and Rohwer �s model of student achievement have both contributed to our

understanding of how academic tasks shape the behaviors and beliefs of students.

In 1983, Doyle introduced the concept of � academic work � (p.159). He suggested

that there are three elements of academic work: (a) the product, meaning the task demand,

such as an essay or a test; (b) the � operations that are to be used to generate the product, �

such as memorizing or integrating; and (c) the � givens, � that is, the resources available to

accomplish the task (p. 161). To be successful, students must match their � operations � and

the underlying cognitive processes to the task demand or required � products, � using the

available resources.

Doyle further maintained that there are four levels of academic tasks or � products �

that require different levels of cognitive processing. They are the following: (a) memory

tasks, either reproduction or recognition; (b) procedural tasks, such as problem solving;

(c) comprehension tasks, such as drawing inferences; and (d) opinion tasks, such as

making a judgement based on supporting evidence. He also asserted that each level of task

is associated with varying degrees of ambiguity and, therefore, different degrees of risk of

error for students. The first two levels, memory and procedural tasks, carry a low degree

of ambiguity because prescribed steps offer students support as they accomplish the tasks.

33

However, the third and fourth levels, comprehension and opinion, have no inherent

structure so the ambiguity and risk are greater.

Doyle (1983) also posited that tasks that are open-ended and involve

understanding, integration, and opinion, like those at levels three and four, are associated

with a higher level of risk not only for students but also for teachers. Doyle maintained

that tasks are embedded in the specific school environment and explained why high school

teachers make some of their instructional decisions. Teachers are held accountable for

content coverage and year-end standardized test scores that are generated annually and

student behaviors as well (Chase, et al., 1993; Doyle). According to Doyle, teachers find

that during the more open-ended tasks from levels three and four, they have greater

difficulty managing student participation, behaviors, and learning outcomes. These higher

level tasks are also time-consuming and difficult to grade. Therefore, many teachers tend

to avoid these tasks and adhere to tasks at levels one and two.

Doyle further hypothesized that the nature of the literacy task will direct students �

attention to specific parts of the content, and they will be able to chose specific cognitive

processes and operations to meet the demands. The result is that high school students

quickly master the operations that are needed to accomplish the lower level tasks that are

required of them.

Therefore, beginning college students report that they are still most comfortable

with the lower level memory and procedural tasks that they experienced in high school

(Carson et al., 1992; Randall, 1999). Because memory for information learned at levels

three and four is most � durable � (Doyle, 1983, p. 164), much of the content that high

school students learn at levels one and two is not in their long term memory when they

reach college. Additionally, if they transfer the � operations � that were successful in high

34

school to the college environment where the � product � demand and the � givens � are

different, they may no longer be as successful academically (Doyle, p. 161).

Booth (1997) explained the problem aptly when he said, � Researchers have

demonstrated that whatever we as teachers try to convey and students � own percept ions

are the filter through which what we communicate passes, and those perceptions

powerfully influence students � approach to learning � (p. 205). Because the concept of task

is embedded in the school environment and, therefore, a specific school culture (Doyle,

1983), students develop beliefs about what it means to learn and to know from high

school activities, and these beliefs determine how they study when they first arrive at

college. Students � beliefs about studying and learning apparently override the reality of

college demands (Van Etten, Frelbern, & Pressley, 1997), and many students resort to a

surface learning by memorizing facts as a way of coping with the bewildering array of

facts, a strategy that Garner and Alexander (1989) term � well-established mal-adaptive

routines � ( p. 145),

Doyle � s (1983) theory provides insight into why high school students perfect the

operations associated with lower level cognitive processing that are no longer effective

when they reach college. The research of Thomas and Rohwer and their colleagues

complements Doyle �s theory and provides a useful model for analyzing the level of task

demands. (Christopoulos, et al., 1987; Curley, et al., 1987; Strage, et al., 1987; Thomas,

et al., 1987). These researchers elaborate on the role of the teacher and the classroom

environment, providing more insight into the academic transition students must make as

they leave high school and begin college. They focus on the academic task demands, the

supports and compensations provided by teachers, and the resulting net demand required

of students.

35

The research on autonomous learning by Thomas et al. (1987) introduced the

important concept of net demand that results from the interaction of task demand,

supports, and compensations. The concept of demand includes the quality and quantity of

course requirements. For example, at one extreme, students might be asked to complete a

memory level matching task with material taken from one chapter of material. At the other

extreme, students might be required to write an essay that requires synthesis of

information from multiple sources and an analysis of the reliability of those sources.

Supports are defined as instructors � actions that help students succeed at meeting the level

of demand. Supports would include help such as providing models of good essays and

offering instruction and practice using study strategies. By contrast, compensations are

instructors � actions that provide so much assistance that the net demand of the academic

task is significantly reduced. For example, a teacher who reiterates all of the text content

and provides a list of all terms that will be on a test would enable many students to be

successful with a minimum of effort. The model proposed by Thomas et al. makes it clear

that teacher behaviors can reduce the net task demands and, therefore, the level of

ambiguity and risk discussed by Doyle (1983).

Through in-depth classroom observations and document analysis, Curley, et al.

(1987) identified course features that can provide a framework for the discussion of the

net task demand found in college as compared to high school. The following general

course demands were isolated: (a) amount of information presented, (b) amount of

verbatim information expected, (c) reading level of the material, (d) level of

comprehension required, (e) amount of integration required, and (f) need to retrieve

information. In general, the research indicates that in high school, the literacy demands in

36

relation to these features are severely reduced by the compensations provided by teachers

(Alvermann & Moore, 1991; Thomas, et al., 1987).

Differences in Literacy Demands Between High School and College

This discussion of the differences between high school and college literacy

demands will be organized into the following four sections: (a) the importance and use of

textbooks, (b) the necessity to read independently, (c) note-taking requirements, and

(d) assessment tasks and their underlying cognitive processing.

Use of Textbooks

The ways that texts are used reflects the learning environment, including the beliefs

that instructors have about what it means to learn. Many college instructors perceive their

task as one of introducing students to ideas, providing them with resources, and then

leaving the actual learning up to the student. They are rarely faced with problematic

student behaviors and are not held responsible for students who fail. On the other hand,

high school teachers have the difficult task of managing student behaviors and being

accountable for student learning outcomes (Doyle, 1983). They are responsible for the

learning of students of all ability levels. Their use of text is driven by the fact that their

success as a teacher is evaluated by how well their students perform on memory level

measures. These differences in the academic environment result in different literacy

demands for students in terms of the use of textbooks.

The text is often a primary source of information in college, and instructors expect

students to read and understand the text independently prior to class and often do not

cover the concepts in class unless students raise questions. Orlando et al. (1989) found

that college instructors reported three reasons students must read texts. Instructors

reported that text material (a) introduces concepts to be covered later in lecture,

37

(b) covers concepts never covered in lecture, and (c) provides a different point of view

from the lecture. Instructors believe the literacy task requires that students select and

combine information from multiple textual sources, a � manipulation � similar to working a

jigsaw puzzle (Chase et al., 1994, p. 12).

In contrast, independent reading, comprehension, and synthesis are almost

unnecessary for most high school students, and, in fact, is not often done. Many

researchers have reported that the independent reading of textbooks at the high school

level is almost non-existent despite the fact that about 90% of high school teachers

(Applebee, et al., 1987) reported frequent use of textbooks. From the students �

perspective, only 22% of students in one survey indicated completing the readings and

82% said that they made Cs or better without reading their text at all (Murden & Gillespie,

1997). Randall (1999) found that more than half of the students she surveyed said that

they read less than half or almost none of their history text on their own. The authors of

the 1990 National Assessment of Educational Progress (NAEP) Science Report Card

(Jones, Mullis, Raizen, Weiss, & Weston, 1990) found that only half of 12th grade science

students nationwide read their science text several times a week. A fourth of these

students reported never reading their science text. The NAEP report for 1987 discovered

the same trends and concluded that such practices were consistent across ability levels

despite tracking that could allow for greater responsibility for the upper level, college-

bound students (Applebee et al.).

Consequently, high school teachers spend most of class time compensating for the

fact that most of their students don �t read or cannot understand the text on their own. In

fact, teachers believe repetition or the � redundancy of content coverage � (Thomas et al.,

1987, p.345) on their part is necessary because they know students aren � t reading. Most

38

teachers lead recitation sessions in which they pose questions and call on students to

answer, highlighting the key points of the assigned reading (Alvermann & Moore, 1991;

Chase et al., 1994; Sturtevant, 1996). Often high school students are assigned worksheets

with questions to answer based on the reading. The answers require one or two word

responses. Students have reported that they either skim the reading to find the answers or

they just listen in class to get the answers during discussions (Chase et al., 1993).

Alvermann and Moore assert that this emphasis on facts makes for a disjointed listing of

discrete pieces of information rather than a focus on underlying concepts.

The research on college students indicates that the high school practices discussed

above have a serious impact on students � use of texts in college. Although college

freshmen believe reading will be necessary to pass their classes, their behaviors don � t

reflect that belief, according to Chase et al. (1994). Chase and her colleagues found that

less than 20% of students in the college history classes they observed reported actually

reading before class (Chase et al., 1994) and only 50% reported reading all of the material

(Chase et al., 1993). Additionally, Warkentin, Stallworth-Clark and Nolen (1999) found

that there was a significant decrease in the number of students who read before class as

the term progressed. Students � reports indicated that this decrease may have been due to

the difficulty of integrating multiple texts and lecture rather than the complexity of the

readings themselves (Chase et al.,1994). This integration, that instructors perceive as so

necessary, requires independent � comprehension enhancing study activities � (Thomas et

al, 1987, p. 279) such as annotat ing, outlining, note-taking, mapping, and time-lines.

The literacy demands are also shaped by the types and uses of supplemental texts

students use in high school and college. College history and political science students are

likely to read original documents, biographies, professional journals, or news magazines

39

that require a high level of background knowledge. In anthropology and sociology, college

students are often assigned readings about various cultures or social groups and are

expected to relate the reading to the concepts and terminology of the discipline.

Moreover, inst ructors often require that students analyze the arguments presented in

relation to the social and political time in which they were published. This type of reading

is rarely required of most high school students. Rather, supplementary materials reportedly

used in high schools are popular magazines, newspapers and filmstrips (Randall, 1999).

In sum, when students enter college, they face radically different cognitive

demands in relation to the use of the text. They must read independently, isolate the key

information that might appear on a test , monitor their own comprehension, pose questions

to the instructor if they do not understand, and synthesize material from different sources.

According to Doyle (1983), these new demands require the development and

implementation of new operations in order to be successful at the independent reading of

test.

Reading Load

As students encounter a more complex reading task that must be accomplished

independently, they also face a significant increase in the reading load, one of the

important course features outlined by Curley et al. (1987). This major change exacerbates

their difficulty in meeting the task demands they face. For example, at the college level,

history students are likely to be responsible for as many as 80 to 100 pages of independent

reading a week (Chase et al., 1994; Simpson & Nist, 1997). Estimated totals in history for

any one term average 750 to 800 pages (Carson et al., 1992; Chase et al., 1994). In

contrast, high school history students are accustomed to short readings in history of as

little as seven to twenty pages per week (Chase, et al., 1994; Murden & Gillespie, 1997).

40

This averages about 800-900 pages for an entire year �s study. Both students and teachers

have reported that even this minimal high school reading load is a major problem for some

students (Chase et al., 1993).

This greater information load for beginning college students increases the difficulty

of the literacy demand because college students must selectively process more

information, distinguish the important from the unimportant, store and retrieve more

information from long-term memory, all in a shorter period of time (Curley et al., 1987;

Thomas, et al., 1991).

Taking Lecture Notes

A few researchers have addressed the task of taking lecture notes, the most

common writing task required of college students. College history instructors devote close

to 95% of class time to lecture, whereas high school teachers spend less than half of the

class period lecturing and students do not really need to take lecture notes to do well on

tests (Carson et al., 1992; Chase, et al., 1993; Simpson & Nist, 1997). The most

significant difference, however, is that college instructors use about half of the lecture to

introduce new concepts, whereas high school teachers use lectures to reinforce and

explain what the students were assigned to read for homework (Chase et al., 1993). In

fact, high school students reported spending about three-fourths of their time listening to

their teacher explain the assigned readings, rather listening to lectures on new information

(Chase et al., 1994; Murden & Gillespie, 1997). High school teachers often post an outline

and students are expected to take notes using that guide (Sturtevant, 1996), a

compensation that is not always available in college. Note-taking in college is often

completed with very few visual aids so that students must organize their notes and

41

integrate them with the text � from both direct and indirect discourse cues � (Carson et al.,

p. 30).

College students have reported that the difficulty in note-taking is the pressure of

writing quickly and not knowing what is the important information to record (Chase et al.,

1994). Indeed, they do not seem to know how to approach the note-taking task.

Researchers have observed students while they were taking history lecture notes, and

many students only recorded the facts, such as dates and names, rather than any analysis

presented by the instructor (Simpson & Nist, 1997). College students also tend to focus

exclusively on ideas cued by their instructors, omitting the uncued but important

information (Chase, et al., 1993; Simpson & Nist). For example, Simpson and Nist

observed history students taking notes at the beginning of each new topic but noticed that

most of them actually stopped taking notes before the instructor finished his explanation of

each topic. This may occur because some instructors do not provide a model for what

good notes would look like (Warkentin et al., 1999).

These findings indicate that students have difficulty when some of the course

features become increasingly complex. College lectures present a great deal of

information in a condensed period of time, and students must comprehend the information

as the lecture progresses in order to select what is most important to record and eventually

study. For most beginning college students, this is a new skill to be learned, one that is

never explicitly taught.

The Assessment Tasks

College students face still another challenge when they first encounter a typical

college test or exam. Two characteristics of assessment tasks significantly increase the

42

cognitive demands for these beginning college students: (a) the level of thinking required

on the tasks and (b) the frequency of testing.

Level of thinking required on the assessment task. The ultimate task demand is

taking a test or completing some other criterial measure. As discussed in a previous

section, Doyle (1983) suggested that there are four main kinds of tasks that are required in

testing situations: memory tasks, procedural tasks, comprehension tasks, and opinion

tasks. Memory and procedural level tasks require only a surface knowledge and minimal

understanding of overall structure, while comprehension and opinion tasks focus more on

the conceptual structures of the knowledge. The level of thinking required of students

determines, to a degree, how they perceive the discipline and what kind of studying they

will do. Most students finish high school very comfortable with the memory level

assessments that were the norm (Randall, 1999). However, in college, students suddenly

encounter assessment tasks at the comprehension and opinion levels, requiring them to

draw inferences, apply concepts to new contexts, and relate new learning to previous

learning. They quickly discover that their old techniques no longer work. This discovery

explains why students report that one of their greatest difficulties with the transition from

high school to college is caused by a lack of sufficient study tools for test preparation

(Thomas et al., 1991).

In general, the typical assessment task in college is one of integration. It involves

the synthesis of lecture and multiple source readings in both writing and exam situations

(Carson et al., 1992). These tasks require the highest levels of cognitive functioning,

including analysis, synthesis, and evaluation (Bloom, 1984). Strage et al. (1987) found a

dramatic increase in the demand for integration in college and a decrease in the amount of

verbatim memorization required. That is, students must subordinate, organize, synthesize,

43

and categorize ideas, both across texts and across textbook chapters (Carson et al.), as

well as relate original writings, novels, and essays to key issues (Hynd, 1999). These tasks

must be accomplished independently, without the compensatory reiteration students were

used to in high school because college instructors report that 50 to 60 percent of their

tests cover material found only in the text (Orlando et al., 1989).

Essay exams are an especially difficult assessment format for beginning college

students to master. For essays, students must engage in what Thomas et al. term

� extended production � (1991, p. 282). In history, students might be required to place their

ideas in a specific time context (Carson et al., 1992), or they might be asked to interpret

an event from the standpoint of various participants or viewpoints (Hynd, 1999). Because

students must often write one major essay and answer five to ten short answer items

within a 50 minute class period (Simpson & Nist, 1997), it is not surprising that they

reported that the pressure to write an organized essay under the time constraints of college

testing is a serious source of stress (Chase et al., 1994).

In contrast, even though many high school teachers explicit ly state that their

instructional goals include understanding and critical thinking, their emphasis on testing of

factual information gives a very different implicit message to students (Hinchman &

Zalewski, 1996; Sturtevant, 1996). High school students typically take memory level tests

that require memorizat ion of facts to answer multiple choice, fill in the blank, or matching

items with no analysis, judgment, or synthesis required (Chase et al., 1994). Such tests tap

into only the lowest levels of Bloom � s taxonomy by requiring knowledge, comprehension,

and minimal application (Bloom, 1984). Thomas, et al. (1991) found these � low-level

reproductive responses � ( p. 283) were required in about 69% of the high school testing

44

they observed. Additionally, they found that only 14% of high school assessment tasks

required any integration, whereas 99% of college tests did.

In summary, beginning college students must make a leap from memory level tests,

for which there are often significant teacher compensations, to tests that require greater

independent comprehension and integration. Students must make this transition essentially

on their own because neither high school teachers nor college instructors provide explicit

instruction in how to read, organize, and study for such tasks.

Frequency of testing. The frequency of testing also changes the cognitive challenge

for students as they make the transition from high school to college. Post-secondary

students often face long retention intervals with few instructor checks on intermediate

progress of reading and comprehending (Strage et al., 1987; Thomas, et al., 1991), so the

retrieval demand is sizeable. This delayed assessment requires students to do the

following: (a) understand the importance of regular attendance and actually attend class

regularly, (b) recognize key information in order to take good lecture notes, (c) under-

stand the importance of regular review and integration of lecture notes with text, and

(d) practice good time management in order to distribute practice and review over time

(Warkentin et al., 1999).

In contrast, 75% of high school teachers report testing weekly (Applebee, et al.,

1987; Ravitch & Finn, 1987) in addition to the daily comprehension checks that are made

by many teachers. This means that there is a limited amount of knowledge that must be

retained at one time, and last minute memorization of facts works quite well.

In summation, the following changes from high school to college significantly

change the net demands in many courses: (a) a decrease in the amount of

teacher/instructor support for the reading, interpreting, and learning of material; (b) a

45

change from the memorization of material for a test of factual recall to the integration and

synthesis required on tests such as essay exams; (c) an increased information load and

longer intervals between tests; (d) increased ambiguity and risk associated with each test;

and (e) the importance of a few exams in the overall grade. To further complicate the

problem, beginning college students have well-developed ideas of what it means to study

that were appropriate for the tasks in high school, but are ineffective for most college level

tasks. As they struggle to cope with college demands, they strive to reduce their risk of

error by using tried and true methods from their high school years.

In light of these findings about the dramatic increase in the complexity of literacy

demands faced by students as they make the transition to post-secondary work, it is clear

that academic assistance programs provide a necessary service by offering a supportive

classroom environment in which to teach students the strategies needed for active and

independent reading and studying in college. Ultimately, the goal of academic assistance

programs is to help students transfer these new strategic behaviors to the demanding

literacy environment they face in college.

Transfer of Learning Strategies

The transfer of learning from a structured educational environment to one of future

independent learning is an issue that has interested researchers and educators for most of

the century (Marini & Genereux, 1995). It is an especially important issue for academic

assistance educators whose primary instructional goal is that students who complete an

academic assistance course will have the declarative, procedural, and conditional

knowledge (McCombs, 1986; Paris, Lipson, & Wixson, 1983), the strategic and

theoretical knowledge (Haskell, 2001), and the motivation (McCombs, 1986) necessary to

apply that knowledge to their subsequent university courses. Therefore, a significant

46

component of course evaluation/research should be an assessment of the extent to which

students have transferred the strategies to future courses and the effect of this transfer on

their future academic performance (Simpson et al., 1997).

This part of the literature review is divided into two sections on the transfer of

learning strategies: (a) theoret ical perspectives on transfer and (b) research findings in

regard to transfer.

Theoretical Perspectives on Transfer

� Transfer of learning is universally accepted as the ultimate aim of teaching.

However, achieving this goal is one of teaching � s most formidable problems � (McKeough,

Lupart, & Marini, 1995, p. vii). Everyday we can see evidence of unconscious transfer of

learning in the behaviors of those around us, for example, when a toddler calls every four-

legged animal a dog. However, it appears that transfer of learning strategies to new

learning situations is not as automatic in formal educational settings such as college.

According to Salomon and Perkins (1989), it is important to understand that transfer is the

� effect of learning on a different performance or context, � not on a similar performance or

in a similar context (p. 116). Examples of the kinds of transfer that are the concern of

academic assistance professionals include the following: (a) using a learning principle or

theory that was learned in one context in a new context, (b) using a learning strategy

appropriately in a different discipline from the one in which it was initially learned, (c)

knowing how to approach new learning problems because of earlier problem solving

experiences, and (d) using factual information learned in one context to interpret new

learning.

According to Marini and Genereux (1995), the study of the phenomenon of

transfer involves the understanding of the relationship among five elements: (a) the

47

instructional or training task, including the materials and practice; (b) the instructional

context, including the expectations, setting, teacher support, and peer behaviors; (c) the

transfer task; (d) the transfer context; and (e) the abilities and dispositions of the learner.

Theorists in the field seem to fall into two camps: (a) those who stress the importance of a

match between the training task and context with the transfer task and context and

(b) those who emphasize the importance of the critical thinking abilities and dispositions

necessary for transfer.

The first group of theorists emphasizes the relation among the four elements of the

task: instructional task, instructional context or domain, transfer task, and transfer context.

These theorists believe that the difficulty of the transfer is determined by the degree of

difference between the training task or training context and the transfer tasks or transfer

context (Marini & Genereux, 1995). � Near � transfer is assumed to be more likely to occur

than � far � transfer (Detterman, 1993, p.5). McPeck �s views (1992) are representative of

this approach, and he explains the problem by saying, �The transfer question is about

whether learning a particular task helps or hinders the learning of another different kind of

task � (p. 201). This view usually focuses on the transfer of specific tasks such as solving

math problems (Sternberg & Frensch, 1993), learning techniques for memorization

(Detterman, 1993), or working physics problems (Bassok & Holyoak, 1993). This first

approach to the issue of transfer is complicated by the inconsistency in the terminology

used to label concepts of transfer. For example, researchers do not agree on what

constitutes � near � versus � far � transfer in terms of task or context. Researchers also

disagree on what constitutes a domain, so discussions of � within-domain � versus � cross-

domain � transfer (Marini & Genereux, 1995, p. 5) or same versus different contexts

(Salomon & Perkins, 1989) are problematic.

48

The second approach to the issue of transfer seems more applicable to the transfer

of self-regulated and strategic learning behaviors that are the goals of academic assistance

courses such as Learning to Learn. This view emphasizes the importance of guiding

students to understand the critical thinking processes that underpin their learning so they

can make the generalizations necessary to transfer those critical thinking dispositions from

the training context to a future context. Theorists refer to these critical thinking

dispositions as � self-regulatory thinking strategies � (Phillips, 1992, p.138), an � inquiring

disposition � (Brell, 1990, p. 66), or � mindful abstraction � (Salomon & Perkins, 1989,

p. 124). These theorists agree that there are dispositions that can be fostered in students

and that challenge their � default � assumptions (Phillips, p.149) about the learning and

study problems they face.

According to Phillips (1992), these critical thinking dispositions occur at the level

of self-regulation and include � strategies that are used across various boundaries � (p.153).

Other theorists agree and believe that the following abilities or dispositions comprise the

underlying critical thinking strategies that can be generalized and may allow students to

transfer their problem solving skills to new situations.

1. The disposition to question automat ic assumptions and interpretations (Brell,

1990; Phillips, 1992) and to pause before making an initial response (Salomon & Perkins,

1989).

2. The ability to move from a focus on the solution of a problem to the properties

of the problem (Phillips, 1992), or a more theoretical understanding of the problem

(Haskell, 2001), or � some generic or basic qualities or attributes or patterns � of the

problem (Salomon & Perkins, 1989, p. 125).

49

3. The disposition to search for and analyze alternative solutions or strategies

(Phillips, 1992; Salomon & Perkins, 1989; Zimmerman, 1998b).

4. The ability to change a focus or an approach when goals are not successfully

met (Phillips, 1992).

5. The ability to weigh evidence, including the relevant contextual information

(Phillips, 1992; Salomon & Perkins, 1989).

6. The disposition to monitor and evaluate choices and decisions (Phillips, 1992;

Zimmerman, 1998b).

This second view of transfer would suggest that these dispositions can be and must

be explicitly taught and encouraged in academic assistance classes as students are taught

various strategies. In fact, Mentkowski (2000) suggests that � learning that lasts � must be

experiential and situated in a study context (p.230). In a class like Learning to Learn,

students encounter problems within specific disciplines and for specific assessment tasks;

then they are guided to make strategic decisions about the appropriate study strategies to

use. Yet, the role of the teacher must be to help students look beyond the specifics of the

presenting problem, to abstract the properties of the problem, and consider the application

to future problems they may encounter in other courses. This requires the ability to

recognize similarities and differences between the learning contexts and tasks and transfer

contexts and tasks and the ability to use the knowledge of the basic principles or patterns

to carry out effective transfer.

Instead of focusing on the distinction between near and far transfer or low-road

and high road transfer, Salomon and Perkins (1989) provide a helpful discussion of the

two types of high-road transfer, � forward-reaching � and � backward-reaching, � explaining

that both kinds of transfer are necessary for students and both require mindful abst ractions

50

(p. 122). To be able to practice forward-reaching transfer, students must be taught to

purposefully consider the basic elements of a problem they are considering in anticipation

of future situations in which some of the same elements might be found. To be able to

practice backward-reaching transfer, students must be taught that in new situations, they

must deliberately think back to past experiences and learning for possible solutions to

current problems. These abstractions, according to Salomon and Perkins, � provide the

bridge from one context to another � (p. 126). These two kinds of high road transfer

certainly reflect the goals of Learning to Learn because they involve the � volit ional,

metacognitively guided employment of nonautomatic process � (Salomon & Perkins, p.

126). In other words, transfer does not occur automatically. Rather, it requires an

intentional teaching of the steps of critical thinking that can help students develop the

dispositions that allow for the critical evaluation of new contexts and problems, from both

anticipatory and retrospective perspectives.

For an academic assistance professional, the concern is the transfer of study

strategies from the training setting of the academic assistance course to the diverse

contexts and tasks students will face in the years to follow. Students who are trying to

transfer effective learning strategies, especially in the social sciences and the humanities,

are faced with ill-structured problems, problems that cannot be definitively described,

problems for which a solution is not clearly available (King, Wood, & Mines, 1990). Not

only are there no definitive interpretations of the content in many college disciplines, but

there is no single answer about the best strategic approach for any one student in any

particular class.

The kind of transfer that is the goal of courses such as Learning to Learn requires

a complex interaction of many different kinds of knowledge and dispositions. It requires

51

not only declarative, procedural, and conditional knowledge about strategies (Paris, et al.,

1983) but also theoretical knowledge about the importance of abstracting principles and

attributes of particular study situations so they can be applied flexibly in future contexts

(Haskell, 2001).

This kind of transfer also requires several affective dispositions that form the

foundat ion for a student � s motivat ion to attempt the transfer task. Some of these affective

dispositions include a willingness to take risks in the highly charged academic climate, a

high level of self-confidence, significant perseverance, and a degree of flexibility and

openness to new experiences (Bereiter, 1995; Marini & Genereux, 1995). Three other

affective dispositions required for effective transfer are academic self-efficacy (Bandura,

1993), internal attributions for success or failure (Graham, 1994) and mature

epistemological beliefs (Schommer, 1993).

Research has found that positive feelings of self-efficacy are highly correlated with

other factors related to academic success, including greater task persistence (Bandura,

1993), more motivation to self-regulate and set higher goals (Zimmerman, 1998a), more

effective monitoring of study time (Bouffard-Bouchard, Parent , & Larivee, 1991), and

greater use of cognitive and metacognitive strategies among college students (Pintrich &

Garcia, 1991). All of these student characteristics and dispositions are necessary for

effective transfer of strategies. According to Bandura (1993), these behaviors are not

common in students with low self-efficacy because they � shy away from difficult tasks �

(p.144). �They have low aspirations and weak commitment to the goals they choose to

pursue � (Bandura, p.144). Transfer of strategies to new academic settings is a risky and

cognitively challenging task that students with low academic self-efficacy are not likely to

have the confidence, persistence, or cognitive monitoring skills to effectively implement.

52

Internal and effort attributions are a second affective variable that impacts transfer.

Graham (1994) posited three dimensions of � attributional judgements � (p.33) including

loci (external or internal), stability (constant or varying over time), and controllability

(subject to volitional control or not). Two of the most common attributions for academic

success and failure are effort and ability. Effort attributions for failure are focused

internally, suggest the possibility for change and improvement over time and situations,

and imply that academic success is within the volitional control of the student. Effort

attributions leave open the possibility for future success if students decide that they will try

harder, that they will devote more time to studying, or that they will use another type of

strategy. These students with effort attributions are likely to consider alternatives for new

approaches to studying and engage in backward-reaching t ransfer as discussed by

Salomon and Perkins (1989). However, ability att ributions are viewed as internal,

constant, and not subject to influence by the student. These self-defeating ability

attributions have been associated with low self-efficacy (Bandura, 1993; Graham;

Zimmerman, 1998a). As Graham says, this � self-ascription for failure to low ability tends

to lower one �s expectations for future success � (p. 40). As a result, students with ability

attributions may put forth less effort because they do not feel in control of academic

outcomes; they believe their efforts can not offset the fact that they do not have the

academic ability to succeed in college. In addition, external failure attributions (e.g., unfair

teacher practices, tr icky tests, or difficult textbooks) have been linked to less effective

cognitive processing (Graham), less effective strategy use, and less task persistence

(Garner, 1990; Graham). Consequently, students with these self-defeating attributions also

lack the disposition to search for more effective alternative strategies, so they are less

likely to transfer strategies to new contexts.

53

Third, mature epistemological beliefs are necessary for effective transfer of

elaborative cognitive/metacognitive strategies. Epistemological beliefs have been studied

extensively by Schommer (1990, 1993) who proposed five dimensions of beliefs about the

nature of knowledge and learning that � seem to affect students � processing of information

and monitoring of their comprehension � (1990, p. 503). Schommer suggests that each

factor can be viewed as a continuum from � self-defeating � beliefs to more � sophisticated �

beliefs (1990, p. 50). The five dimensions are as follows: (a) the degree to which

knowledge is simple or complex, (b) the degree to which knowledge is certain or evolving,

(c) the degree to which authority is omniscient, (d) the degree to which learning depends

on innate ability or effort, and (e) the degree to which learning is quick and accomplished

on the first try or never accomplished. Schommer � s research has found that students who

believe in � quick, all-or-nothing learning � tend to have poor comprehension, possibly

because they draw inappropriately simplified conclusions (1993, p.503). Such students fail

to integrate concepts and have inadequate comprehension-monitoring skills, resulting in

overconfidence in their understanding of what they have read or heard. Students with

these immature epistemological beliefs do not perceive the complexity of most learning

tasks, so they are unlikely to have the critical thinking dispositions to carefully analyze

each task and then search for the most appropriate study solution.

In sum, the issue of the transfer of learning from a structured learning environment

to independent learning is an issue that has interested researchers for years. Most current

research into the transfer of learning focuses on the critical thinking dispositions that

students must possess to be able to abstract the properties or basic qualities of the

problems that they face in order to apply an effective solution.

54

Research Findings on Transfer

The researchers who study the issue of transfer have approached the subject from

three different perspectives: (a) a strategic perspective (Pressley, El-Dinary, Brown,

Schuder, Bergman, York, & Gaskins, 1995), (b) a cognitive perspective (Haskell, 2001;

Phillips, 1992; Salomon & Perkins, 1989), or (c) a social perspective (Campione, Shapiro,

& Brown, 1995). According to Campione and his colleagues (1995), most early research

on transfer was on short-term transfer and was conducted in laboratory settings. However,

these early studies have little relevance for transfer issues within the complex educational

environments that are the concern of academic assistance instructors. Three exemplary

studies of transfer that were conducted in a naturalistic setting, one from each of the three

perspectives, provide useful information for educators who are interested in the issue of

transfer.

The strategic perspective is the one discussed by Pressley (1995) and his

colleagues in their report of two exemplary elementary programs, one in the Benchmark

School in Pennsylvania and the other in the SAIL/SAI Program in Maryland. Both

programs serve elementary aged students who have significant reading problems. For both

studies, the authors combined the three methodologies of teacher interviews, traditional

case studies, and discourse analyses. They concluded that transfer of strategy use requires

six elements: (a) long-term instruction, (b) the introduction of only a few strategies a year,

(c) extensive explanations and teacher modeling, (d) scaffolding, (e) feedback and re-

instruction as needed, and (f) the encouragement of flexible thinking. Although their work

was primarily from a strategic perspective, it certainly overlaps with the cognitive

theorists.

55

The cognitive theorists often focus on the critical thinking skills that can transfer to

new learning situations. Norris and Phillips (1987) studied the reading strategies of middle

school students using verbal protocol analysis (Payne, 1994). They found that the major

difference between good and poor readers was the good readers � critical thinking

dispositions that they were able to t ransfer to the task of reading unfamiliar text. The

good readers employed some of the following productive strategies of a critical thinker:

(a) They questioned their previous default interpretations when evidence to the contrary

was presented. (b) They shifted their focus to related questions when they could not

resolve an immediate interpretation. And (c) They analyzed alternative interpretations. On

the other hand, poor readers usually employed unproductive strategies, such as

maintaining a default interpretation despite evidence that would contradict it. For both

good and poor students, the critical thinking ability and disposition had a greater impact

on their success with a new reading task than prior knowledge of the content did.

However, it is difficult to determine from the authors � discussion if the results may have

been related to differences in general intelligence or verbal abilities as much as differences

in strategic behaviors.

In contrast, the social theorists have found that the social aspect of learning seems

to enhance students � transfer of learning (Campione, et al., 1995). These researchers agree

that learning is most likely to result in transfer when students learn through cooperative

social interaction. Campione and his colleagues developed the educational model called

Fostering Communities of Learners for their work with elementary and middle school

students. They found that the use of reciprocal teaching and other cooperat ive learning

activities helped students develop an understanding of the underlying principles of the

learning task. These authors identified the following five elements of the learning

56

environment that they believe are essential if transfer is to occur: (a) explicit teaching of

transfer as a major goal of learning, (b) continuous student talk that encourages multiple

perspectives, (c) students in the role of teaching, (d) content that � supports extended

analysis � (p.42), and (e) activities practiced in context (Campione, et al.). It is difficult to

determine whether the authors � findings of a positive impact on students � transfer reflect

the motivating factor of the social interaction or if the cognitive processing was

substantially altered by the constant verbalization.

For college students, the transfer of newly learned strategies to new educational

contexts probably involves all three of the aspects discussed above. It certainly requires

both active and flexible cognitive processing and significant strategic knowledge, both

procedural and conditional. It is also likely to be enhanced through interaction and

verbalization with peers and instructors. However, the limited research that exists on

college research is problematic because it only examines the indirect indicators of the

transfer of skills or dispositions from students � learning in an academic assistance course

to their use of the strategies in subsequent courses. For example, Weinstein and her

colleagues used the global measure of student retention without examining the actual

strategies that students used that might have contributed to their retent ion (Weinstein,

Dierking, Husman, Roska, & Powdrill, 1998). Researchers also encounter problems when

they examine transfer in the natural setting of a college campus because of the multiple

confounding variables. The level of student motivation, the reasons students have for

taking an academic assistance class, the goodness of fit between the strategies taught in an

academic assistance course with the strategies required to be successful in subsequent

courses all affect the level of strategy t ransfer. Additionally, as outlined in transfer theory,

much of what students transfer is a way of thinking about problems and abstracting

57

patterns and principles, a way of viewing a discipline, or a way of mentally examining

information and considering alternative solutions; these cognitive processes are difficult

and time-consuming to access as well as assess.

Academic assistance instructors are concerned about these vital issues of students �

transfer of learning from an academic assistance course to subsequent college courses. In

fact, academic assistance instructors have a mandate to help students develop both the

abilities and the dispositions necessary to make the leap to independent learning. However,

the complexity and constant change of literacy task demands require ongoing course

evaluation to ensure that the academic assistance course offerings actually reach the goal

of preparing students for the literacy demands of college.

Characteristics of Effective Course Evaluation

Standards for effective course evaluat ion have been clearly out lined in the

literature. Evaluation efforts should be utilitarian, feasible, ethical, and accurate, allowing

for continual program improvement (The Joint Committee on Standards for Educational

Evaluation, 1994). Course evaluation that is characterized by these qualities is useful not

only to the institution itself, but also makes a contribution to the general program

evaluation literature.

Many theorists make a clear distinction between research and evaluation. A major

purpose of research is to inform others in the field; therefore, it must be replicable and

generalizable (Payne, 1994). This is not often possible for course evaluation because it is

difficult to compare courses across institutions due to the interaction of a myriad of

complex variables (Keimig, 1983). Institutions reflect unique composites of different

student demographic variables and entry qualifications, different degree programs, and

different institutional types (i.e., residential or commuter, private or public, technical

58

institutes, junior colleges, colleges, or universities) (Boylan, 1997). Payne distinguishes

between the two because evaluation, unlike research, always includes a value judgement

that forms the basis for decision-making. Indeed, the goal of course evaluation is to

provide information to pract itioners and stakeholders so that value judgements can be

made about existing courses in regard to their continuation, modification, or termination

(Boylan, George, & Bonham, 1991; Payne, 1994). Other researchers insist that research

and evaluation go hand in hand (Casazza & Silverman, 1996; Commander & Smith,

1995). This research study reports on the evaluation for one academic assistance course,

but it can certainly be considered a contribution to research in the field in the sense that it

may provide a source of ideas for similar institutions, not for replication, but as a stimulus

for their own inst itution-specific course evaluat ion.

Ongoing, comprehensive, and systematic evaluation of post-secondary academic

assistance courses has become more important in the last few decades for several reasons.

First, in this age of fiscal accountability in public higher education, courses are expected to

demonstrate their cost effectiveness to justify the expenditure of public funds (Boylan,

1997; Elifson, et al., 1995; Rossi, et al., 1999). Second, in 1999, Breneman and Haarlow

reported on the continuing debate about the appropriateness of academic assistance

courses at the college level, which are viewed by some as remedial. Third, open enrollment

at some institut ions and increased financial aid for students who attend public institut ions

have forced colleges and universities to analyze the population of student they can best

serve (Breneman & Haarlow; Elifson, et al.).

Effective evaluation/research efforts share several important characteristics that

have been clearly outlined by The Joint Committee on Standards for Educational

Evaluation (1994), a committee sponsored by over a dozen national professional

59

educational organizations involved in program evaluation. Four basic characteristics or

standards that have been delineated as a guide for educators involved in evaluation are

certainly relevant to course evaluations as reported in this study. First, effective evaluation

is utilitarian, providing practical and useful information for the intended audiences.

Second, effective course evaluation is feasible, practical for conducting in a naturalistic

setting and cost-efficient in terms of the use of resources. Third, such evaluation is guided

by ethical and legal processes that guard the rights of participants. Finally, effective course

evaluation is based on accurate information and is used to make logical value and merit

judgements. Evaluation/research efforts of academic assistance courses should strive to

conform to these best pract ices.

Utilitarian

The first characteristic of effect ive evaluation, ut ility, is two pronged. Effective

educational evaluation efforts address the concerns and needs of all groups or audiences

that have an investment in the course (Payne, 1994) and provide practical and useful

information for each of these audiences. In the academic assistance literature, there are

several major categories of audiences, often termed stakeholders (Greene, 1994; Payne;

Rossi, et al., 1999). One category of stakeholders consists of faculty, staff, and

administrators responsible for the academic assistance courses. Another includes others in

the university who have an interest in the courses, such as faculty in the various disciplines

who might teach high-risk courses or, as Payne suggests, o ther program directors who

might compete for funds. A third audience includes � institut ional decision makers � in the

university-wide administrat ion who make budgetary decisions and are ultimately held

accountable for overall student performance at the institution (Boylan, et al., 1991, p.104).

Finally, student beneficiaries form a significant group of stakeholders, a group that is often

60

overlooked in the literature. Quality program evaluation focuses on students � perceptions

of the usefulness of an academic assistance course as they transfer the strategies to other

classes so there can be a close match between student needs and services (Maxwell, 1997;

Simpson, et al., 1997).

Effective course evaluation/research is also utilitarian in the sense that it provides

practical and useful information for each of these audiences. Effective program evaluation

is comprehensive, systematic, and ongoing, completed at least annually (Boylan, 1997;

The Joint Committee on Standards for Educational Evaluation, 1994). The information is

� timely and influential � (The Joint Standards for Educational Evaluation, p.5), taking into

account changes in society, in student demographics and in the institution �s mission

(Boylan; Boylan et al., 1991; Casazza & Silverman, 1996; Maxwell, 1997). Effective

program evaluation is part of a pract ical, cyclical change process that includes research,

development, diffusion, adoption, and then more research, an integral part of an

institution �s overall strategic plan (Elifson, et al., 1995).

Three other characteristics of utilitarian educational evaluation are described in the

literature. First, effective evaluation utilizes multiple criteria (Boylan, et al., 1991; Casazza

& Silverman, 1996); at a minimum, data should be gathered on demographics, academic

performance, student satisfaction, faculty perception and satisfaction (Boylan, 1997;

Maxwell, 1997), persistence data, stop-out data, and notations of whether students �

course enrollment was mandatory or elective (Casazza & Silverman). Data should report

on both short term impact such as grades the next semester, as well as long term measures

such as how students transfer the skills learned in academic assistance programs to other

course work (Boylan et al., 1997; Simpson, et al., 1997). Second, effective course

evaluation begins with a thorough review of the literature for a knowledge of what has

61

worked well in other institutions with common problems (Simpson et al.). Third, Boylan

et al. (1991) suggest that effective evaluation efforts are open to the unexpected outcome,

the serendipitous finding.

Feasible

The second characteristic of effective course evaluation, feasibility, is found in

evaluation efforts that are � realistic, prudent, diplomatic, and frugal � (The Joint

Committee on Standards for Educational Evaluation, 1994, p.6). There is a � clear

statement of realist ic, attainable objectives � (Boylan, et al. 1991, p.103) that are possible

within the naturalistic educational setting without disrupting classes. It follows that such

educational evaluation works within the availability and limitations of several kinds of

resources, including funding, time, personnel, technical expert ise, cooperation among

stakeholders, and access to records (The Joint Committee on Standards for Educational

Evaluation; Rossi, et al., 1999).

The characteristic of feasibility limits the designs that are appropriate in an

educational setting such as an academic assistance course. Experimental models that use

random assignment to matched control and treatment groups or models that use large

enough samples to perform statistical analysis on the data are not often used. Educators

often do not have large samples and certainly cannot withhold educational interventions in

order to have a control group (Boylan, et al., 1991). Another reason experimental designs

are not often used is because effective course evaluation should measure extended learning

that is always complicated by prior knowledge and other confounding variables, what

Payne calls �unpredictable contingencies � (1994, p. 11).

62

Ethical

The third characteristic of effective course evaluation is the attempt � to facilitate

protection of the rights of individuals affected by the evaluation � (The Joint Committee on

Standards for Educat ional Evaluation, 1994, p.6). Such evaluation efforts establish

guidelines regarding confidentiality, freedom of information, protection of human subjects

and fiscal responsibility. Designers of course evaluation carefully consider whether or not

any activity will harm students � self-esteem, whether or not the activities are sensitive to

cultural diversity, whether or not researchers have been instructed in how to behave in a

non-judgmental way, and whether or not there is inappropriate pressure to participate

(Clark-Thayer, 1995).

Accurate

The final characteristic of effective course evaluation is accuracy. Course

evaluation efforts that strive for accuracy share several common characteristics. For

example, they control for historical effect with concurrent groups, they control for novelty

effect by studying a particular program after its first year, and they look at cumulative

versus single semester GPA (Kulik, Kulik, & Shwalb, 1983). Accurate conclusions and

judgements require that course evaluation efforts consider assumptions educators make

about the needs of the students they teach in academic assistance courses. For example,

Simpson, et al. (1997) argue that assumptions, such as the belief in funct ional reading skill

instruction versus the belief in strategy instruction, must form the foundation for

evaluation efforts.

In sum, effective course evaluation research reflects four basic characteristics.

Such program evaluation is ut ilitarian, feasible, ethical, and accurate. For academic

63

assistance courses, evaluation efforts that attempt to meet these standards are most likely

to provide information that will enhance the quality and success of future courses.

Recent Course Evaluation Efforts

Experts have agreed for decades that effective program evaluation is necessary for

the operation of any social program; however, prior to the 1980s and 1990s, very little

formal evaluation for academic assistance courses was reported in the literature. A review

of several resource texts for professionals published in the early 1980s found either no

mention of evaluation (Algier & Algier, 1982) or only a paragraph or two with no details

about design, methodology, or results (Lauridsen, 1980). Only one text published prior to

1990 that was devoted to issues of program evaluation was located (Walkevar, 1981). As

late as 1997, Boylan, et al. reported that only 14% of two year institutions and 25% of

four year institutions had an established plan of systematic and regular program evaluation

for all of the components of their academic assistance programs, including courses.

O �Hear and McDonald (1995) speculate that there are several reasons why so few quality

evaluation reports can be found in the literature. For one, most academic assistance

educators are trained as teachers rather than researchers; consequently, research and

publication are not part of their performance expectations or evaluations. Second, there is

a limited theoretical base in the field on which to base program evaluation research.

Finally, there is a scarcity of graduate programs that encourage scholarship and research in

academic assistance education.

Although the course evaluation research is limited, a variety of quantitative

academic outcomes have been used as dependent measures to evaluate academic

assistance courses. They have included the following: (a) internal measures, (a) global and

distal measures of academic success; and (c) specific near measures of academic success.

64

Internal measures have included grades in academic assistance courses, course completion

rates (Boylan, 1997; Hennessey, 1990), and the number of attempts in developmental

courses before completion (Keimig, 1983). However, these internal measures of success

within the academic assistance program itself did not examine the transfer of skills to

mainstream classes as suggested by many educators (Gebelt, et al., 1996; Keimig).

Global external measures that at tempted to measure transfer of skills from the

academic assistance course to the regular college curriculum have included the following:

(a) retention by individual terms and overall retention in college (Boylan, 1997; Casazza &

Silverman, 1996; Dembo & Jakubowski, 1999; Dubois et al., 1998; Gebelt, et al., 1996;

Keimig, 1983), (b) degree completion (Casazza & Silverman; Keimig), and (c) cumulative

GPA (Weinstein, et al., 1998). These global measures must be interpreted carefully

because, for different majors, there is such a wide variation in the difficulty level of the

courses that are used to arrive at the measure. As a way to equalize the difference in

difficulty level between majors, Mealey (1991) has suggested that looking at students �

GPA for core classes only.

Near indicators of academic success include the following measures: (a) institu-

tional academic status following completion of an academic assistance course (e.g,

satisfactory, on probation, or dismissal) (Hennessey, 1990), (b) grades in subsequent

courses (Keimig, 1983), and (c) credits attempted and credits earned in the regular content

areas (Dubois et al.,1998; Hennessey; Wilcox, et al., 1997).

Very few studies in the literature have looked beyond these typical academic

indicators. One example of such research is Mealey �s (1991) work that measured the

satisfact ion of instructors in several disciplines. She asked instructors to compare the

quality of work turned in by students enrolled in an academic assistance course to the

65

work of the students who were not taking the course. She queried the instructors in the

areas of test-taking, critical thinking, and synthesis of informat ion. Other researchers have

looked at affective change as a result of course enrollment, such as students � attitudes

about studying (Dubois et al., 1998), their motivation (Dubois et. al.), and their

knowledge and utilization of campus resources (Wilcox et al., 1997).

A problem faced by researchers as they design evaluat ion for courses is the

assignment of students to cohort groups. However, some researchers have been creative in

identifying nearly matched groups in order to evaluate the success of academic assistance

courses. For example, Napoli & Hiltner (1993) compared three groups: (a) those placed in

required academic assistance reading classes, (b) those who were placed but avoided

attending academic assistance reading classes, and (c) those who exempted the program

by their earned GPA. Maring, Shea, and Warner. (1987) used similar cohort groups:

(a) students who had low scores on the state pre-college test and followed advice to take

the reading and study strategies course, (b) students who were deficient at mid-semester

and followed the advice to take the course, and (c) students who took the course as an

elective despite no serious academic problems.

A variety of statistical analyses have been used in course evaluation. These include

correlations, such as assessing the relationship between academic assistance course

contact hours and GPA or university hours completed (Abrams & Jernigan, 1984). Other

researchers have compared students enrolled in an academic assistance class and cohort

groups who were not enrolled on measures such as mean GPA (Hennessey, 1990), mean

retention rates (Gebelt, et al.,1996; Hennessey), and mean grades in content area classes

(Wilcox, et al., 1997). ANOVAs and post hoc comparisons have also been cited in the

literature (Dembo & Jakubowski, 1999; Napoli & Hiltner, 1993; Wilcox et al.).

66

In sum, quantitative measures predominate the current course evaluation literature.

Some researchers have used global measure of academic success that are difficult to

interpret because of the complexity of the factors involved. Other researchers have

employed more specific measures of academic success, such as grades in courses

subsequent to the academic assistance course. A few researchers have attempted to

uncover some of the affective changes that may have resulted from academic assistance

courses.

Summary of Chapter Two

This review has included a discussion of six areas of the current literature that are

relevant to the study: (a) theoretical perspectives on self-regulated learning, (b) measures

of self-regulated learning, (c) post-secondary literacy demands, (d) t ransfer of learning

strategies, (e) characteristics of effective course evaluation, and (f) recent course

evaluation efforts. The literature describes self-regulated learners as autonomous,

metacognitive, proactive, goal-directed, and internally motivated. The literature also

suggests that beginning college students will have to become self-regulated learners in

order to deal with the increasingly complex task demands at the post-secondary level.

Addit ionally, the literature mandates effective evaluation to ensure that academic

assistance courses are providing the instruction needed for students to develop into self-

regulated learners, learners who can transfer the knowledge, strategies, and critical

thinking dispositions to their subsequent college-level courses.

67

CHAPTER 3

METHOD

This study had four major goals. The first goal was to investigate differences in

the academic performance of students who had completed Learning to Learn and matched

controls who had never enrolled in the course. The second goal was to examine the

differences in self-regulated learning behaviors between these same two groups of

students. The third goal was to examine the perceptions held about Learning to Learn by

students who had completed the course. Finally, the fourth goal was to investigate how

and to what extent Learning to Learn students transferred strategies learned in the class to

subsequent college courses. This chapter includes a description of the study participants,

the data collection sources and procedures, and the data analysis procedures.

Participants

Two populations of participants who might especially benefit from Learning to

Learn instruction were selected for the study: (a) regularly admitted first-semester

freshmen and (b) students on academic probation. Although any students at UGA can

enroll in Learning to Learn, freshmen were selected as participants because they

experience a major t ransition from a high school environment in which there were constant

teacher direction, support, and compensation to a post-secondary environment in which

students are expected to manage more difficult academic tasks independently (Nist &

Simpson, 2000). Freshmen were also selected because the researcher hypothesized that

there would be fewer confounding variables if the participants were first-semester

freshmen who had minimal to no experiences in the UGA environment. Probationary

68

students were selected as participants because they have demonstrated their deficiency in

academics and are at risk of being dismissed from school. Because the study examined

academic performance for three semesters after enrollment in Learning to Learn, only

students who remained at the University for that time period were included in the study.

Regularly Admitted First-Semester Freshmen

The first category of participants included students drawn from the total freshmen

population enrolled at the University of Georgia during fall 1998 and fall 1999 who were

still enrolled three semesters later. A total of 64 of these 1998 and 1999 freshmen

completed Learning to Learn during the fall semester of their freshman year and were

selected as participants. Twenty-eight were freshmen during the fall of 1998 and 36 were

freshmen during the fall of 1999. For each Learning to Learn student , a freshman who did

not elect to take the course was selected as a matched control, 28 from 1998 and 36 from

1999. These controls were selected on the basis of the verbal score of the Scholastic

Aptitude Test (SAT-V), ethnicity, gender, and prior joint enrollment experience, in that

order. These variables were used in the matching procedure because of their possible

impact on college performance.

The matching procedure took place in four stages. First, for each Learning to

Learn participant in each year, all students with the same SAT-V score formed a pool of

possible matches. These pools ranged in size from about 50 students for the extreme upper

and lower ends of the SAT-V scale (e.g., 710 and 470) to as many as 225 for the middle

range scores (e.g., 560). Out of this first pool, all students of the same ethnic group

remained in the pool at the second stage. For European American students, the pool sizes

remained large at this point; however, for African American, Hispanic, or mixed race

students, the pool was drastically reduced. At the third stage, all students of the same

69

gender remained in pool. After matching on these three variables, a match was randomly

chosen from the pool that remained. If the first random match did not match on joint

enrollment experience, another random match was selected until the control student

matched the Learning to Learn student on this last variable as well.

After the matched controls were selected, the researcher made several attempts to

contact both the Learning to Learn and control students through email, U.S. mail, and

telephone. Only 23 of the 64 original controls agreed to participate in the second phase of

the study. However, because 30 Learning to Learn students had already agreed to

participate, it was also important to find 30 controls for this second phase of the study.

Therefore, alternate controls were selected as described until a total of 30 matched

controls agreed to participate. In the end, seven of the controls were not the original

students drawn from the pools but were randomly drawn during the second attempt to find

controls who would participate in the second phase of the study. The other 57 control

students came from the original attempt to locate matches.

Table 1 summarizes the demographic characteristics that were used to match the

Learning to Learn and control students. When available, the information is also presented

for the entire freshman class for each year. Some data are presented both as a raw count

and as a percentage.

70

Table 1

Demographics for Matching Freshmen Participants for Fall 1998 and 1999________________________________________________________________________1998 UNIV (n= 28) Control (n=28) All freshman (n=5986)________________________________________________________________________Mean SAT-V 566.79 566.79 596 Students with joint 8 (29%) 7 (25%)enrollment credits

Ethnicity American Indian 0 0 11 (<1%)African-American 1 (4%) 1 (4%) 397 (7%)Asian-American 1 (4%) 1 (4%) 200 (3%)Hispanic 0 0 73 (1%)European-American 26 (93%) 26 (93%) 5170 (86%)Unknown/Multiracial 0 0 97 (2%)Non-Resident Alien 0 0 38 (<1%)

GenderMale 10 (36%) 10 (36%) 2546 (42%)Female 18 (64%) 18 (64%) 3440 (57%)

________________________________________________________________________1999 UNIV (n=36) Control (n=36) All freshmen

(n=6054)________________________________________________________________________Mean SAT-V 574.72 574.72 598

Students with joint 10 (28%) 10 (28%)enrollment credits

EthnicityAmerican Indian 0 0 9 (<1%)African-American 2 (7%) 2 (7%) 361 (6%)Asian-American 0 0 232 (4%)Hispanic 0 0 94 (2%)European-American 33 (92%) 33 (92%) 5221 (86%)Unknown/Multiracial 1 (3%) 1 (3%) 108 (2%)Non-Resident Alien 0 0 29 (<1%)

GenderMale 14 (39%) 14 (39%) 2,584 (43%)Female 22 (61%) 22 (61%) 3,470 (57%)

________________________________________________________________________

71

An analysis of the information in Table 1 reveals one major difference between the

study participants and freshman as a whole. T-tests indicate that the SAT-V of the study

participants was significantly lower than the SAT-V score of the entire freshman class for

both 1998 {t(27)=-3.010, p=.006} and 1999 {t(35)=-2.790, p=.008}. These findings

indicate that the Learning to Learn students, as a group, have significantly weaker verbal

skills than the average freshman, at least as measured by the SAT. Figures were not

available for a comparison of the study participants with the freshman class as a whole for

joint enrollment experience. An examination of gender and ethnic group indicates that the

proportion of each group for the study participants was similar to the entire freshman

class.

Probationary Students

The second category of participants included students who were drawn from 68

students who were on academic probation at the University of Georgia during fall 1998

and 1999 and completed Learning to Learn during that same fall semester. According to

the Undergraduate Bulletin (Office of Undergraduate Admissions, 2000-2001), students

are placed on academic probation � at the end of any term in which their UGA cumulative

average is below 2.0 � (p. 47). Because of the high attrition rate among probationary

students, only 25 of the 68 were still enrolled at UGA three semesters later, 10 from fall

1998 and 15 from fall 1999. The other students had either dropped out of UGA or had

transferred to another institution.

I had planned to match these probationary students with similar students who had

not completed Learning to Learn. However, there were serious problems finding control

72

students. Data were requested from the Institute of Research and Planning on all students

who were on academic probation at the beginning of fall 1998 and fall 1999. These data

were requested in August of 2000, but did not arrive in the Division of Academic

Assistance until January, 2001. An examination of the data at that time made it apparent

that it would not be useful for finding matches because of problems with the data. First,

many of the Learning to Learn students who had been on probation were not included in

the data received. Second, many of the students included in the data had not been on

probation during the fall of 1998 and 1999.

Another problem I had was a difficulty contacting the probationary students who

had completed Learning to Learn. I accessed the University �s computer-based students

record system, the Bell-South phone book, the UGA print directory, and the UGA on-line

directory for email addresses, phone numbers, and mailing addresses. I attempted to

contact students by email, U.S. mail, and phone on multiple occasions; however, often the

information was out-of date or students did not respond to the contacts. In an attempt to

locate more students, I talked with Bill Marshall, Associate Registrar (personal

communication, April, 2001). He explained that even his office does not have current

information on students unless students have voluntarily provided it. My multiple attempts

to locate probationary students who had completed Learning to Learn resulted in locating

only eight students; of these eight, six agreed to participate. As a result of these problems,

I decided to focus this part of the study on just the six probationary students who were

willing to volunteer because it proved to be so difficult to get matched controls.

73

The six probationary students were male. Four students were European-

American, one was African-American, and one was Asian-American. SAT-V scores

ranged from 440 to 670 with a mean of 558. One student did not have an SAT score

posted. None of the probationary students had prior high school joint enrollment

experience.

Data Collection Sources and Procedures

Data were collected from both archival sources and from two surveys and one

inventory. Data collection was accomplished in two phases.

Phase One

During the first phase, archival data were collected from The Office of

Institutional Research and Planning and from the University �s computer-based student

record system on the 64 Learning to Learn freshmen, their 64 controls, and the six

Learning to Learn probationary students who were still enrolled three semesters following

Learning to Learn. Two kinds of data were collected: (a) baseline admissions data and (b)

data reflecting academic performance at UGA.

Baseline admissions data included gender, ethnicity, SAT-V scores, joint

enrollment credits, high school grade point average (HSGPA), adjusted high school grade

point average (AHSGPA), and predicted freshman grade point average (PFGPA).

AHSGPA is a weighted GPA that includes grades in all academic subjects considered by

the UGA Office of Admissions and a weight factor that adjusts for the difficulty level of

different high schools. Students � predicted freshman grade point average (PFGPA) is

derived from a formula that factors in both verbal and math SAT scores, high school grade

74

point average in core academic courses, the difficulty level of specific courses taken by

individual students, and the difficulty level of a student �s high school (M. DeMaria,

Assistant Director of Admissions, the University of Georgia, personal communication,

June 22, 2000). In this formula, SAT verbal scores are weighted more heavily than SAT

math scores.

Academic performance data differed slightly for the two categories of participants.

For all students, semester GPAs and grades in reading-intensive courses were collected for

several semesters. In addition, for the 128 freshmen, the number of course withdrawals

were recorded. For the students on academic probation, an additional indicator of

academic performance was academic status for each semester following Learning to

Learn.

Phase Two

During Phase Two, all of the 134 students were contacted by email (see Appendix

A), phone, and U.S. mail (see Appendix B) and asked to meet with me during March and

April of 2001. I made multiple contacts with students until 30 Learning to Learn

freshmen, 30 controls, and six probationary students, a total of 66 students, agreed to

participate in the Phase Two of the study. At the initial contact, I explained to the students

that there would be a monetary incentive for their participation. I told them that all

participants who took part in this phase of the study would be paid ten dollars for their

participation. I also told them that there would be a drawing for four large cash prizes of

$50.00, $75.00, $100.00, and $200.00 and that all students who part icipated in the Phase

Two would be eligible for the drawing. The $10.00 checks were mailed to students in

75

May. The awarding of the large cash prizes took place at the end of the data collection

during a pizza party that was held to thank the students for their participation.

I explained the purposes of the study to all of the 66 students who the participated

in the second phase. This was accomplished in separate sessions for controls and Learning

to Learn students with no more than four students per session. I told all of the students

that the purpose of the study was to investigate the self-regulated learning behaviors of

college students. In separate sessions, I told the Learning to Learn students that three

additional purposes were related to their Learning to Learn course. I explained that I

hoped to learn how useful the course had been to students, how they would recommend

the course be improved, and how they were using the strategies they had learned in

Learning to Learn in their other reading-intensive courses. Before administering the

instruments, all students signed a consent form (see Appendix C). I then directed the

students to read the instructions for each instrument and emphasized the need for honest

answers. I asked the students if they had questions before they began, and then remained

in the room as students completed the instruments in case they had questions as they

worked.

Motivation as a Possible Confounding Variable

Motivation has been a major confounding variable in many studies that have

examined college students � academic performance (Pintrich & Garcia, 1991; Winne,

1995). I took several steps in an attempt to discover if motivation was a critical

confounding variable that might influence indicators of academic performance in this

study; the concern was that Learning to Learn students, as a group, might be more highly

76

motivated. One step was the calculation of ANCOVAs using AHSGPA as a covariate. A

second step was the examination of the motivation sub-scale on the Self-Regulated

Learning Inventory (Gordon, et. al., 1996) (see Appendix D). In addition, the role of

motivation was directly addressed with all students in Phase Two of the study. The

purpose of these questions was to discover any obvious difference in the level of

motivation for academic success that existed at the beginning of the freshman year

between students who enrolled in Learning to Learn and the control students.

Motivational question asked of Learning to Learn students. The last question on

the Students � Perceptions of Learning to Learn (SPLL) (Randall, 2000a) asked freshmen

and probationary Learning to Learn students to report the primary reason they decided to

enroll in Learning to Learn (see Appendix E). The forced choices were as follows: (a) I

needed an A to boost my GPA. (b) I wanted to learn new ideas for improving my study

techniques. And (c) My parent or advisor pressured me to take it. Fourteen students

(47%) responded that their primary reason was to learn new strategies. The primary

reason for twelve students (40%) was the pressure from parents or advisors. The primary

reason for three students (10%) was the need for an A to boost their GPA. One student

did not respond to the question. Therefore, the motivation for taking the course for about

half of the students did not match the course goals. That is, about half of the Learning to

Learn students took the course because they felt some external pressure or because they

saw some advantage other than learning new study strategies. Only half of the students

took the course primarily because they thought their high school strategies would not be

77

effective in college and that the new strategies presented in Learning to Learn would be

useful to them in their subsequent courses.

Motivational questions asked of controls. Motivation was addressed on two

separate questionnaires completed by the control students, one for controls who had been

informed about Learning to Learn at the time of their freshman registration (see Appendix

F) and one for controls who had not been informed about Learning to Learn (see

Appendix G). Control students were asked two questions. First, they were queried about

their interest in a study strategies course and asked to explain why they did not choose to

enroll in Learning to Learn when they were freshmen. Of the 30 control students who met

with the researcher, only three had heard about Learning to Learn when they registered as

incoming freshmen. When asked why they did not enroll, one of these three students

responded that he did not believe he needed extra help, one student said he wanted to take

the course but did not have room in his schedule, and a third student reported that all

sect ions of Learning to Learn were full when he went to register.

The other 27 controls reported that they had not heard of Learning to Learn when

they registered for their freshman fall semester so they could not consider it as an optional

course. After I explained the goals of Learning to Learn, these students were asked if, in

retrospect, they might have enrolled in Learning to Learn if they had known about the

course. Of those 27, 15 said that they would probably have enrolled in the course if it had

fit their schedule and 12 said they would not have. Therefore, 56% of the controls

reported that they probably would have been motivated to take a study strategies course.

Of the 12 who said they would not have taken the course, 10 explained that they did not

78

think they needed instruction in study strategies. Two others indicated that they did not

know at the beginning of their freshman year that they would have benefitted from such

instruction. Therefore, over half of the controls appeared to have been motivated to learn

effective study strategies as they began their freshman year.

In an additional effort to look at motivation for academic success, the control

students were asked what kinds of help they sought independently when they had difficulty

with a course (see Appendixes F and G). Two of the 27 reported that they did not need

any outside help with their studying. The other 25 students reported a variety of help-

seeking behaviors that might be considered indicators of motivation for academic success.

Nineteen students reported attending regular study groups, fourteen used their teaching

assistants for tutorial help, six found private tutors, five used departmental tutors, four

used academic assistance tutors at Milledge Hall, two took adjunct classes, one used the

tutors available to athletes, and three studied informally with friends. These numbers

represent a total of 54 attempts to seek academic help from a variety of resources on

campus for an average of about two per student . Each of these efforts, except possibly

studying with friends, took a deliberate act of seeking help and an additional expenditure

of time beyond routine studying.

In sum, on the surface, it appears that the Learning to Learn students were not

more motivated than the controls. In fact, many of the Learning to Learn students took

the course for reasons other than to become more effective and strategic learners. The

risks inherent in using self-report data were certainly present (Garner, 1988) because I was

asking students to remember and report accurately about their motivations during a time

79

period more than a year in the past. There was also a chance that students responded in a

way that they thought would please the researcher. However, the researcher had no prior

or subsequent relat ionship with any of the students, so that may have been unlikely.

Instruments Completed by All Participants

All 66 Phase Two participants completed two instruments: The Self-Regulated

Learning Inventory (SRLI) (Gordon, et al., 1996) (see Appendix D) and a questionnaire

related to their motivation to learn new learning and study strategies, as previously

discussed (see Appendices E, F, and G).

The SRLI is a self-report inventory that measures self-regulatory practices of

undergraduate students. It includes the following four sub-scales of self-regulated

learning: (a) executive processing, which includes students � deliberate and conscious

metacognitive task analysis, cognitive monitoring, and strategy construction and

evaluation; (b) cognitive processing, which includes students � automatic focusing of

attention, information storage, information retrieval, and elaboration; (c) motivational

set/beliefs, which includes students � attributions and goal orientations; and (d)

environmental utilization/control, which includes students � help seeking behaviors, time

and sett ing management, and resource allocation. The inventory also provides a total self-

regulated learning composite score. This 80-item inventory uses a 5-point Likert-type

scale with responses on each item ranging from to 1 (not at all typical of me) to 5 (almost

always typical of me). Each sub-scale has a total of 20 items with a score range of 20 to

100, resulting in a range of 80 to 400 for the total inventory. Means were derived for each

sub-scale of the SRLI and for the composite score of self-regulation.

80

This version of the SRLI (Gordon, et al., 1996) was designed for use with

undergraduate students. Internal consistency for the SRLI is good, with Cronbach �s alpha

coefficients ranging from .78 to .82 on the four sub-scales and .93 on the composite

(Gordon, et al., p.8). Validity of the SRLI was determined by a correlation of the SRLI to

GPA, an indicator of academic success. Although they were not all strong, the correlations

were all significant at the .001 level. The correlations were as follows: (a) executive

processing (.16), (b) cognitive processing (.24), (c) motivation (.46), (d) environmental

utilization (.19), and (e) the composite score ( .30). For this study, the two strongest

correlations, the composite score (.30) and the sub-scale score for motivation (.46), were

the most important because they were used in ANCOVAs and correlations.

Instruments Completed by Learning to Learn Students Only

All Learning to Learn students (30 freshmen and 6 probationary students)

completed two surveys. The first was the Students � Perceptions of Learning to Learn

(SPLL) (Randall, 2000a), a survey that measures students � perceptions of the usefulness of

the strategies learned in Learning to Learn and students � suggestions for course

improvements. The second survey was Transfer of Learning to Learn Strategies (TLLS)

(Randall, 2000b), a survey that examines how and to what extent students transferred the

strategies they learned in Learning to Learn to subsequent university courses. Finally, the

six probationary students were informally interviewed individually by the researcher. They

responded to questions about their high school and early college experiences and their

perceptions of why they had had so much academic difficulty at UGA.

81

The SPLL (Randall, 2000a) has six sections which allow for both quantitative and

qualitat ive analysis (see Appendix E). In the first section, students were asked to rate the

overall usefulness of each of the six instructional components of Learning to Learn with a

range from 1 (not at all useful) to 3 (very useful). There were additional response choices

for students to check if they were unsure of the usefulness of the instruction or if they

believed they knew how to use the strategy before they took Learning to Learn. The first

three instructional components focused on improving cognitive and metacognitive

processing with active reading strategies (i.e., annotations, note-taking strategies, and

rehearsal/test preparation strategies). The other three components focused on self-

management areas (i.e., time management, motivation, and beliefs about knowledge and

learning). In the second section of the SPLL, students were asked to explain their response

in detail whenever they rated an instructional component as not at all useful or very

useful.

The third, fourth, fifth, and sixth sections of the SPLL (Randall, 2000a) consist of

open-ended questions. In section three, students were asked to explain any other

components of the course that were helpful to them but were not queried within the six

components they rated in the first section. Then in section four, students were asked to

explain any of the six instructional components that they thought should be expanded and

taught in more depth. For sections five and six, students were asked to explain in detail

any other curricular components they thought should be added to the course or

components they would recommend be omitted.

82

The TLLS (Randall, 2000b) focuses in depth on students � transfer and modification

of the three strategies of active reading using annotations, note-taking during lectures, and

rehearsal/test preparation to subsequent university courses (see Appendix H). Students

were asked to think about a course they had completed the prior semester that required a

significant amount of independent reading. They then answered detailed questions about

how they approached the literacy tasks in this target course and how they used or did not

use strategies learned in Learning to Learn. The items in this survey are generally forced-

choice with an open-ended other option that requires an explanation. For example,

students were given five choices for how they might have annotated in their target course.

If none of the choices was appropriate, students were asked to check other and then

explain their usual format in detail. Several of the questions were open-ended why

questions that asked students for their explanation of their previous responses. For

example, if students reported that they did not annotate at all in their target class, they

were asked to explain why not. Finally, the TLLS also asked students to explain their

personal modifications of strategies that were taught in Learning to Learn, any changes

they made in strategy use as the semester progressed, and their rationale for such changes.

Both the SPLL (Randall, 2000a) and the TLLS (Randall, 2000b) were piloted in a

Learning to Learn course of twenty students taught by the researcher during the summer

of 2000. Students completed the surveys as directed and then part icipated in a debriefing

session with the researcher. The discussion helped the researcher clarify items that were

unclear, identify choices that should be added, and find inconsistencies in phrasing. In

addition, both instruments were reviewed by three academic assistance professionals

83

whose Learning to Learn courses are based on self-regulation theory. These instructors

offered suggestions for improvement and clarity.

Data Analysis

The research questions and the method of data analysis are outlined herein. The

data were analyzed separately for 1998 and 1999 in order to have a built-in replication

over two years. Eight research questions, organized by the four goals of the study, guided

the analyses of the data.

Goal One: To Examine Academic Performance

1. Is there a difference between the academic performance of regularly admitted

first-semester freshmen who completed Learning to Learn during fall semesters 1998 and

1999 and the academic performance of regularly admitted first-semester freshmen who

did not elect to take the course? To answer this question, the following indicators of

academic performance were analyzed using analyses of covariance (ANCOVA): (a)

semester grade point average (GPA) for the fall semester of the freshman year (1998 and

1999), (b) GPA for the subsequent spring semester (1999 and 2000), (c) freshman grade

point average (FGPA) after 30 earned credit hours, (d) the difference between predicted

freshman grade point average (PFGPA) and actual FGPA, (e) grades in reading-intensive

courses taken during the spring, summer, or fall semester after the fall of the freshman

year, (i.e., Chemistry 1211 and 1212, Biology 1103 and 1104, History 2111 and 2112,

Political Science 1101, Sociology 1101, and Anthropology 1102). The final indicator, the

number of course withdrawals subsequent to the first fall semester, was analyzed using t-

tests.

84

ANCOVAs were used to test the hypotheses comparing the academic performance

of the Learning to Learn students and their controls for indicators a, b, c, d, and e.

ANCOVAs statistically � adjust the treatment effects for any differences between the

treatment groups that existed before the experimental treatments were administered �

(Keppel, 1991, p. 302) and reduce the error variance, creating a more sensitive test of the

hypothesis. The three covariates used were adjusted high school grade point average

(AHSGPA), number of credit hours earned during the semester of note or during the

freshman year, and motivation, as measured by the motivation sub-scale of the SRLI

(Gordon et al., 1996).

I chose AHSGPA as a covariate because it allowed me to compare the college

performance of the two groups while adjusting for previous high school performance.

AHSGPA was also used as a covariate because of its potential as an indicator of

motivation (Zimmerman, Bandura, & Martinez Pons, 1992). Because the students were

matched on SAT-V, one indicator of academic ability, Learning to Learn students and

controls would have been expected to have had similar academic abilities needed for

success in high school. Therefore, any differences in AHSGPA might reflect some

difference in motivation between the two groups.

The second covariate used was earned credit hours. When a comparison was made

between Learning to Learn students and controls on GPA for any particular semester or

for the freshman year, the covariate was the credit hours earned for that particular time

period. It was assumed that there might be a relation between the number of hours earned

and GPA; that is, the higher the number of credit hours attempted and earned, the greater

85

the work load, and the lower the GPA might be. Earned credit hours for any semester

included all hours earned for completion of courses at the 1000 level or above in which

letter grades were awarded, excluding Pass/Fail and Satisfactory/Unsatisfactory grades.

This adjustment was particularly important for freshman earned credit hours.

Freshman credit hours were computed for each student at the end of the semester in which

30 hours were earned, the point at which the FGPA was calculated. For some students,

this occurred at the end of spring or summer semester of the first year. However, many

students earned just under 30 hours their first year; therefore, the computation for their

FGPA did not occur until the fall of their second year and many had earned well over 30

hours by the end of that semester. For example, it was important to adjust for the

difference between students whose FGPA was computed at the end of 30 hours after two

semesters and those whose FGPA may have been computed after as many as 44 hours

taken over four semesters.

The third covariate was motivation as measured by the motivation sub-scale of the

SRLI (Gordon, et al., 1996). Because motivation has been found to be a variable that

impacts college academic performance (Pintrich & Garcia, 1991; Winne, 1995), it was

used as a covariate to adjust for any differences that might exist between the Learning to

Learn students and the controls.

The procedures that were used for each of the ANCOVAs were as follows. First,

the data were examined for outliers using Cook �s D (Pedhazur, 1997, p. 51). Then the

data were examined for possible interactions between the covariate and the treatment. The

criterion used for testing the interaction was �± =.10. The relatively high Type I error rate

86

was chosen to reduce the risk of a Type II error. For comparisons for which there was no

interaction, the differences between the two groups were examined for statistical

significance with �± =.05. For comparisons for which there was a significant interaction,

regression lines were plotted to better understand the relation between the covariate and

the dependent measure for the two groups. Finally, the Johnson-Neyman procedure

(Pedhazur, 1997, p. 592) was employed to determine where along the covariate measure

were differences between the groups statist ically significant at the .05 level.

2. Did the academic performance of probationary students change after

completion of Learning to Learn during fall semesters 1998 and 1999? The following

four indicators of academic performance were analyzed by descriptive statistics: (a)

semester GPAs for fall semester of Learning to Learn enrollment, (b) semester GPAs for

each semester after completion of Learning to Learn through fall 2000, (c) any change in

academic status following Learning to Learn, and (d) grades earned in reading-intensive

courses that were taken during or after the semester of Learning to Learn enrollment

through fall 2000.

Goal Two: To Examine Self-Regulated Learning

3. Is there a difference between the reported self-regulatory practices of regularly

admitted first-semester freshmen who completed Learning to Learn during fall semesters

1998 and 1999 and the reported self-regulatory practices of regularly admitted first-

semester freshmen who did not elect to take the course? The composite score of the SRLI

(Gordon et al., 1996) was used to obtain a measure of self-regulated learning that was

analyzed with t-tests.

87

4. What are the reported self-regulatory practices of probationary students who

completed Learning to Learn during fall semesters 1998 and 1999? The SRLI (Gordon,

et al., 1996) was used to obtain a measure of self-regulated learning that was analyzed

with descriptive statistics.

5. Is there a relation between students � reported self-regulatory practices and

their academic performance? For freshmen participants, these data were analyzed using

Pearson �s Product Moment Correlation Coefficients. Because of the small number of

probationary participants, the researcher examined the data for patterns..

Goal Three: To Examine Students � Perceptions about Learning to Learn

6. Which components of the Learning to Learn curriculum do students report

helped them successfully meet the literacy demands of their subsequent courses and

regulate their own learning processes? These data were obtained from the SPLL (Randall,

2000a) and were analyzed by tallying, grouping, and descriptive statistics for both

freshmen and probationary students.

7. What suggestions do students have for additions or omissions to the Learning

to Learn curriculum? These data were collected from responses on the SPLL (Randall,

2000a) and were analyzed by tallying, grouping, and descriptive statistics for both

freshmen and probationary students.

Goal Four: To Examine the Transfer of Strategy Use

8. Do students transfer the literacy strategies taught in Learning to Learn to the

active reading, note-taking, and rehearsal/test preparation required in subsequent

courses that have a heavy reading load? These data were obtained from the TLLS

88

(Randall, 2000b) and were analyzed through tallying, grouping, and descriptive statistics

for both freshmen and probationary students.

Summary of Chapter Three

This study employed a variety of measures in order to assess the effectiveness of

Learning to Learn, a course taught within the Division of Academic Assistance at the

University of Georgia. Institutional archival data were used to assess students � academic

performance. Two surveys and one inventory were used to measure students � self-

regulated learning behaviors, students � perceptions about Learning to Learn, and their

transfer and modification of learning strategies to a subsequent reading-intensive course.

The data were analyzed through descriptive statistics, t-tests, ANCOVAs, and

correlations.

89

CHAPTER 4

RESULTS

This study had four major goals. The first goal was to investigate differences in the

academic performance of students who had completed Learning to Learn and matched

controls who had never enrolled in the course. The second goal was to examine the

differences in self-regulated learning behaviors between these same two groups of

students. The third goal was to examine the perceptions held about Learning to Learn by

students who had completed the course. Finally, the fourth goal was to investigate how

and to what extent Learning to Learn students transferred strategies learned in the class to

subsequent college courses. Two populations of participants were studied: (a) 64 regularly

admitted first-semester freshmen from fall 1998 and fall 1999 and their 64 matched

controls and (b) six students on academic probation during fall 1998 and fall 1999.

Research Quest ions

The following eight research quest ions that guided this study are organized around

the four basic goals of the research.

Goal One: To Examine Academic Performance

1. Is there a difference between the academic performance of regularly admitted

first-semester freshmen who completed Learning to Learn during fall semesters 1998 and

1999 and the academic performance of regularly admitted first-semester freshmen who did

not elect to take the course?

2. Did the academic performance of probationary students change after

completion of Learning to Learn during fall semesters 1998 and 1999?

90

Goal Two: To Examine Self-Regulated Learning

3. Is there a difference between the reported self-regulatory practices of regularly

admitted first-semester freshmen who completed Learning to Learn during fall semesters

1998 and 1999 and the reported self-regulatory practices of regularly admitted first-

semester freshmen who did not elect to take the course?

4. What are the reported self-regulatory practices of probationary students who

completed Learning to Learn during fall semesters 1998 and 1999?

5. Is there a relation between students � reported self-regulatory practices and their

academic performance?

Goal Three: To Examine Students � Perceptions about Learning to Learn

6. Which components of the Learning to Learn curriculum do students report

helped them successfully meet the literacy demands of their subsequent courses and

regulate their own learning processes?

7. What suggestions do students have for additions or omissions to the Learning

to Learn curriculum?

Goal Four: To Examine the Transfer of Strategy Use

8. Do students transfer and modify the literacy strategies taught in Learning to

Learn to the active reading, note-taking, and rehearsal/test preparation required in

subsequent courses that have a heavy reading load?

Data used to answer these research questions were collected from several sources.

First, quantitative data measuring academic performance were accessed on all 134

participants through the University of Georgia �s Institute of Research and Planning and

the University �s computer-based student record system. Second, as explained in Chapter

91

3, The Self-Regulated Learning Inventory (Gordon et al., 1996), a self-report inventory

about strategic study behaviors, was completed by the 66 of the total 134 students who

agreed to meet with the researcher. Third, of these 66 who met with the researcher, the 36

Learning to Learn students completed two surveys. The first survey, Students �

Perceptions of Learning to Learn (SPLL) (Randall, 2000a), was designed to measure

students � perceptions of the usefulness of the instruction provided in Learning to Learn.

The second survey, Transfer of Learning to Learn Strategies (TLLS) (Randall, 2000b),

was designed to examine how and to what extent students transferred the strategies

learned in Learning to Learn to subsequent university classes with heavy reading loads.

Chapter 4 presents the results of this study. The findings for each research question

are presented, first for the 128 freshmen and second for the six probationary students,

rather than in the numerical order as written.

Findings

Freshmen

This section presents the findings for each of the research questions for freshmen

who enrolled in their first semester at the University of Georgia either fall 1998 or fall

1999.

The quantitative analyses for this study was based on five basic assumptions. First,

the researcher assumed that the two groups of students in the study, the Learning to

Learn participants and the controls, were independent to the degree that they were

mutually exclusive (i.e., the students who had never enrolled in Learning to Learn were a

totally different sample than students who had completed the course). Second, the

researcher assumed that the population from which the study was drawn, all college

freshmen, was likely to be normally distributed in terms of academic performance. Third,

92

equality of within group variance was assumed for each comparison between the Learning

to Learn students and controls unless Levene �s �s test (Huck & Cormier, 1996) indicated a

lack of equality of variance. In the few comparisons where equality of variance could not

be assumed, adjusted t-scores, F-scores, and significance levels were used to interpret the

results.

In addition, two other assumptions were examined for the ANCOVAs. First, it

was assumed that there was a linear relation between the covariate and the dependent

variable. Chapter 3 provides a detailed discussion of the justifications for this assumption

and the rationale for the choice of covariates. Second, homogeneity of the regression

slopes for the control group and the Learning to Learn students was assumed. As

explained in Chapter 3, when this assumption was inappropriate, regression slopes were

plotted and interpreted using the Johnson-Neyman procedure (Pedhazur, 1997).

Research Question 1:

Is there a difference between the academic performance of regularly admitted first-

semester freshmen who completed Learning to Learn during fall semesters 1998 and 1999

and the academic performance of regularly admitted first-semester freshmen who did not

elect to take the course?

As discussed in Chapter 3, six quant itative indicators were examined for all

participants (N=128) in order to answer this first research question. In brief, these

indicators were as follows: (a) semester GPA for the fall semester of the freshman year

(1998 and 1999), (b) GPA for the subsequent spring semester (1999 and 2000), (c) FGPA

after 30 earned credit hours, (d) the difference between PFGPA and the actual FGPA, (e)

grades in reading-intensive courses taken during the spring, summer, or fall semester after

the fall of the freshman year, and (f) the number of course withdrawals subsequent to the

93

first fall semester. The data from the first five indicators were analyzed with ANCOVAs.

The data from the last indicator, course withdrawals, were analyzed with t-tests.

Although the subjects were matched on SAT-V, ethnicity, gender, and joint

enrollment experience, t-tests were conducted to determine if there were significant

differences between the Learning to Learn students and the controls on other variables. T-

tests were conducted on the following data: (a) high school grade point average

(HSGPA), (b) adjusted high school grade point average (AHSGPA), (c) predicted

freshman grade point average (PFGPA), (d) credit hours earned during fall semester, (e)

credit hours earned during spring semester, (f) freshman credit hours computed at the

point at which the University determines the freshman average, and (g) motivation, as

measured by a sub-scale on the SRLI (Gordon, et. al., 1996). All hypotheses were tested

at the .05 level for statistical significance.

As revealed in Table 2, significant differences existed for some variables between

the Learning to Learn students and the controls, and the patterns were consistent from

1998 to 1999. First, the control students had higher HSGPAs than the Learning to Learn

students and the differences were statistically significant for both 1998 {t(27)=-2.252,

p=.028} and 1999 {t(35)= -2.228, p=.029}. In fact, for both years, the Learning to

Learn students earned HSGPAs lower that the freshman class as a whole, and that

difference was statistically significant for both 1998 {t(27)=-3.912, p=.001} and 1999

{t(35)=-4.311, p=.000}. Second, the control students had higher AHSGPAs than the

Learning to Learn students; this difference was not statistically significant for 1998

{t(27)=-1.7, p=.095} but was statistically significant for 1999 {t(35) =-2.7, p=.009}.

Third, the controls had statistically significant higher PFGPAs than the Learning to Learn

students as computed by the UGA Office of Admissions for both 1998 {t(27)=-2.4,

94

p=.01} and 1999 {t(35)=-2.3, p=.023). Fourth, controls earned more credit hours during

the spring semester of their freshman year than the Learning to Learn students did and the

difference was statistically significant for 1998 {t(27)=-2.6, p=.01} and for 1999 {t(35)=-

2.3, p=.02}. There were no statist ically significant differences between the groups for fall

earned credit hours, freshman credit hours, or motivation.

95

Table 2

Differences Between Subjects and Controls________________________________________________________________________

UNIV Control __________ __________

Measure M SD M SD t p

1998 (n=28) (n=28)HSGPA 3.33 .34 3.51 .27 -2.252 .028

AHSGPA 3.48 .32 3.63 .31 -1.698 .095

PFGPA 2.81 .22 2.95 .24 -2.410 .019 Credit hours

Fall 13.04 1.97 12.39 2.32 1.119 .268

Spring 11.82 2.41 13.43 2.19 -2.618 .011

Freshman 37.61 3.93 37.04 4.34 .516 .608

Motivation* 7 3.00 8.42 70.15 8.12 .919 .366________________________________________________________________________1999 (n=36) (n=36)HSGPA 3.36 .34 3 .53 .29 -2.228 .029

AHSGPA 3.48 .31 3.66 .28 -2.670 .009

PFGPA 2.96 .24 3.09 .23 -2.319 .023

Credit hoursFall 12.64 1.85 12.92 1.36 -.725 .471

Spring 12.03 2.09 13.22 2.31 -2.302 .024

Freshman 36.08 4.03 36.44 4.35 -.365 .716

Motivation* 68.14 10.83 70.88 10.29 -.720 .477________________________________________________________________________Note. *Out of a possible 100

96

To adjust for these initial differences between the groups as summarized above,

ANCOVAs were computed using several covariates. AHSGPA was chosen as a covariate

instead of HSGPA because AHSGPA reflects the difficulty level of the different high

schools that the students attended. AHSGPA was used as the covariate for five of the six

comparisons, motivation was used for four of the comparisons, and earned credit hours

were used for the comparison of semester GPAs. Although PFGPA could not be used as a

covariate because the SAT-V part of the PFGPA formula was used to match students, a

comparison of student performance relative to their predicted performance was computed

as part of Research Question 1.

For the ANCOVAs, the data from each indicator were analyzed in two phases.

First, the data were examined for outliers Using Cook �s D (Pedhazur, 1997). Only one

outlier was discovered, but there was no sound rationale for removing this student from

the data set. Second, the data were examined for significant interactions. When there was

no interaction, the adjusted means were examined for significant differences, but when

there was a significant interaction (p<.10), two steps were taken to examine the results.

Regression lines were drawn in order to observe the nature of the interaction. Then the

Johnson-Neyman procedure was employed to determine if the observed differences were

significant at the .05 level (Pedhazur, 1997). The results for each indicator are discussed

separately.

There were five interactions for which the Johnson-Neyman procedure (Pedhazur,

1997) was used. In two cases, the equation resulted in a negative number under the radical

which made it impossible to complete the equation. However, in both cases, the number

97

under the radical approached zero (i.e., -.00019 and -.00000703). Therefore, the value

under the radical was set at zero and the equation was solved. In these two cases, only one

critical value resulted so significance was determined in relation to that value. In a third

case, the Johnson-Neyman procedure set the critical lower limit at a point well above the

actual data set so that the rejection region did not capture the point of intersection. This

calculation was computed by hand and the computer results were confirmed. The critical

lower limit did not make sense so it was ignored and only the upper limit was used to

determine significance.

Indicator 1a: Semester GPA for fall semester (1998 or 1999). The purpose of this

indicator for Research Quest ion 1 was to evaluate any differences that might exist in the

academic performance of the Learning to Learn students and the controls at the end of the

semester during which the Learning to Learn participants completed the course. Three

ANCOVAs were computed for both years, using motivation, AHSGPA, and fall earned

credit hours as covariates.

Results for 1998 indicated that for both motivation and AHSGPA, the assumption

of equal within group slopes was met; that is, there were no significant interact ions

between the covariates and the treatment variable. With motivation as the covariate, the

adjusted means for the Learning to Learn and control groups were 3.313 and 3.178

respectively. This difference was not statistically significant at the .05 level {F(1,54)=.510,

p=.482}. Using AHSGPA as a covariate, the adjusted means were 3.161 and 3.118 for

Learning to Learn students and control students respectively. This difference was not

statist ically significant at the .05 level {F(1,54)=.089, p=.767}.

98

When the covariate was fall earned credit hours, results for 1998 indicated that

there was evidence of a significant interaction {F(1,54)=3.089, p=.085}. Therefore, the

first step was to plot regression lines in order to examine the nature of the interaction.

Regression lines indicated that for control students, there was a moderate positive

correlation between the number of hours they earned and their semester GPA (r=.493).

That is, the greater the number of hours these students took, the higher their semester

GPA. However, for Learning to Learn students, there was no relationship between GPA

and the number of hours (r=.015). Regardless of the number of credit hours they earned,

their fall GPA was a little above 3.0. For the second step, the Johnson-Neyman procedure

was employed to determine where along the covariate continuum the differences between

groups on the outcome was statistically significant. As explained earlier in this chapter,

this calculation was one that resulted in only one crit ical value. The crit ical number of fall

earned credit hours calculated by the Johnson-Neyman procedure was 14.97. Fourteen

(25%) of the students earned fall credit hours greater than 14.97. Therefore, it was

concluded that the control students who earned 15 hours or more for the semester earned

significantly higher GPAs than the Learning to Learn students did for the same number of

semester hours. However, for the students (75%) who earned fewer that 15 credit hours,

there was no statistically significant difference between the groups for fall GPA.

Results for 1999 indicated that the assumption of equal within group slopes was

met with all three covariates; that is, there were no significant interact ions between the

covariates and the treatment variable. With motivation as the covariate, the adjusted

means for Learning to Learn and control groups for fall GPA were 3.133 and 2.953

99

respectively. The difference was not statistically significant at the .05 level {F(1,70)=.963,

p=.335}. Using AHSGPA as the covariate, the adjusted means for fall GPA were 3.159

and 2.916 for Learning to Learn and control students respectively. This difference was

not statist ically significant at the .05 level (F(1,70)=3.339, p=.070}. With fall credit hours

as the covariate, the adjusted means for Learning to Learn and control students were

3.089 and 2.977 respectively. This difference was not statistically significant at the .05

level {F(1,70)=.636, p=.428}.

Indicator 1b: Semester GPA at the end of the subsequent spring semester (1999

and 2000). The purpose of this part of Research Question 1 was to examine any difference

in academic performance between the two groups the semester after Learning to Learn,

the semester when students who had completed the course could be expected to apply the

strategies they had learned. Three ANCOVAs were computed using motivation,

AHSGPA, and earned spring credit hours as covariates.

The results for 1998 indicated that the assumption of equal within group slopes

was met when the covariate was motivation; that is, there was no significant interaction

between the covariate and the treatment variable. The adjusted means for the Learning to

Learn and control students for spring GPA were 3.101 and 3.277 respectively. This

difference was not statist ically significant at the .05 level {F(1,54)=1.302, p=.264}.

However, there were significant interactions when adjusted for AHSGPA

{F(1,54)=9.985, p=.003} and spring earned credit hours {F(1,54)= 2.989, p=.090}.

Therefore, regression lines were plotted and examined for both AHSGPA and spring

earned credit hours. First, regression lines for AHSGPA indicated that for controls, there

100

was a moderate positive correlation (r=.617) between AHSGPA and spring GPA; that is,

the greater the AHSGPA earned by the control students, the higher their spring semester

GPA. However, there was a very small negat ive correlation (r=-.207) between AHSGPA

and spring GPA for Learning to Learn students; that is, the higher their AHSGPA, the

lower their spring semester GPA.

Next, the Johnson-Neyman procedure was employed to determine where along the

covariate continuum the difference between groups on the outcome was statistically

significant. The Johnson-Neyman procedure identified the critical lower and upper limits

for AHSGPA as 2.92 and 3.57. Two students had AHSGPAs below 2.92, and 27 students

(48%) had AHSGPAs above 3.57. Therefore, when adjusted for AHSGPA, for students

with AHSGPAs of 3.57 or higher, control students had significantly higher spring GPAs

than Learning to Learn students did. For students (48%) with AHSGPAs lower than 3.57

but higher than 2.92, there was no significant difference between the two groups on spring

GPA. However, for students with AHSGPAs below 2.92 (3%), the Learning to Learn

students had significantly higher spring GPAs.

Second, the interaction for spring earned credit hours was examined by plotting

regression lines. As was found to be true for fall semester, there seemed to be no

correlation between the number of course hours taken and semester GPAs for the

Learning to Learn students (r=.027); their semester averages hovered at 3.0 regardless of

the number of earned credit hours. However, for the controls there was a moderate

positive correlation (r=.507) between earned credit hours and spring GPA; the control

students seemed to do better when they took more hours.

101

Next the Johnson-Neyman procedure was used. The lower and upper limits were

ident ified as 22.89 and 13.84 respectively. This is the calculation for which the lower limit

was identified at a point above the upper end of the actual range of the data, as discussed

earlier in this chapter. Therefore, only the upper limit of 13.84 was used to determine

significance of the findings. Twenty-two students (39%) earned 14 hours or more credit

for spring semester. Therefore, I concluded that, when adjusted for spring earned credit

hours, control students who took 14 hours or more earned significantly higher spring

GPAs than Learning to Learn students did for the same number of semester hours.

However, for students (61%) who earned less than 14 credit hours, there was no

significant difference between the Learning to Learn and control students for spring GPA.

Results for 1999 indicated that the assumption of equal within group slopes was

met with all three covariates; that is, there were no significant interact ions between the

covariates and the treatment variable. With motivation as the covariate, the adjusted

means for Learning to Learn and control students for spring GPA were 2.776 and 2.941

respectively. The difference was not statistically significant at the .05 level {F(1,70)=.969,

p=.333}. Using AHSGPA as the covariate, the adjusted means for spring GPA were 2.902

and 3.014 for Learning to Learn and control students respectively. This difference was

not statist ically significant at the .05 level {F(1,70)=.670, p=.416}. With spring credit

hours as the covariate, the adjusted means for Learning to Learn and control students

were 2.844 and 3.037 respect ively. This difference was not stat istically significant at the

.05 level {F(1,70)=1.934, p=.169}.

102

Indicator 1c: Freshman grade point average (FGPA) calculated at the completion

of 30 credit hours. The purpose of this part of Research Question 1 was to examine the

difference between the two groups after the completion of 30 hours, a benchmark at UGA

because it marks the point at which grades are evaluated for the retention of the HOPE

scholarship for the first time.

The results for 1998 indicated that the assumption of equal within group slopes

was met when the covariate was either motivation or freshman earned credit hours; that is,

there were no significant interactions between the covariates and the treatment variable.

With motivation as the covariate, the adjusted means for Learning to Learn and control

students for FGPA were 3.205 and 3.258 respectively. The difference was not statistically

significant at the .05 level {F(1,54)=.186, p=.669}. With freshman earned credit hours as

the covariate, the adjusted means for FGPA were 3.047 and 3.257 for Learning to Learn

and control students respectively. This difference was not statistically significant at the .05

level {F(1,54)=3.542, p=.065}.

However, there was a significant interaction when FGPA was controlled for

AHSGPA {F(1,54)=6.255, p=.016}. Therefore regression lines were plotted in order to

examine the interaction. These regression lines indicated that for Learning to Learn

students, there was no correlation (r=-.049) between AHSGPA and FGPA; that is,

Learning to Learn students earned freshmen averages at about 3.0 regardless of their

AHSGPA. However, for controls, there was a moderate positive correlation (r=.589)

between AHSGPA and freshman GPA; that is, for controls, the higher their AHSGPA, the

higher their freshman GPA.

103

The Johnson-Neyman procedure identified the critical lower and upper limits of

AHSGPA as 2.46 and 3.64. None of the students had AHSGPAs below 2.46. However,

twenty-two of the 56 students (39%) earned AHSGPAs of 3.64 or higher. Therefore,

when adjusted for AHSGPA, for students who earned a AHSGPA of 3.64 or higher, the

controls earned a significantly higher FGPA than did the Learning to Learn students.

However, for students (61%) who had AHSGPAs less than 3.64, there was no significant

difference between the two groups for FGPA.

Results for 1999 indicated that the assumption of equal within group slopes was

met with all three covariates; that is, there were no significant interact ions between the

covariates and the treatment variable. With motivation as the covariate, the adjusted

means for Learning to Learn and control groups for FGPA were 2.967 and 2.947

respectively. The difference was not statistically significant at the .05 level {F(1,70) =.027,

p=.871}. Using AHSGPA as the covariate, the adjusted means for FGPA were 3.031 and

3.011 for Learning to Learn and control students respectively. This difference was not

statist ically significant at the .05 level {F(1,70)=.052, p=.820}. With freshman credit

hours as the covariate, the adjusted means for Learning to Learn and control students

were 2.973 and 3.049 respect ively. This difference was not stat istically significant at the

.05 level {F(1,70)=.573, p=.452}.

Indicator 1d: The difference between predicted freshman average (PFGPA) and

actual FGPA. The purpose of this part of Research Question 1 was to examine academic

performance in relation to predicted performance. The dependent measure used was a

difference score between PFGPA and actual FGPA because this difference can provide a

104

good indicator of performance in relation to potential as measured by past performance. As

explained in Chapter 3, the PFGPA was determined by a formula that computes a weighted

composite of SAT scores, high school grades, difficulty level of high school courses, and a

difficulty rating of different high schools. Therefore, I concluded that the difference

between PFGPA and actual FGPA would seem to be one of the most important indicators

of academic performance. To determine this difference score, a numerical difference

between PFGPA and the actual FGPA was calculated as the dependent measure. For

example, one student �s PFGPA was 2.91, and her actual freshman GPA was 2.44.

Therefore, the difference score was -.47. Such negative difference scores indicate that

students did not perform as well as they were predicted to do during their freshman year.

Positive difference scores indicate that students performed better than expected during their

freshman year at UGA. On average, this prediction is as accurate for students with low

AHSGPAs as for students with high AHSGPAs. Two ANCOVAs were computed to

compare the difference scores, using AHSGPA and motivation as the covariates for 1998

and 1999.

The results for 1998 indicated that the assumption of equal within group slopes was

met when controlled for motivation; that is, there was no significant interaction between

the covariate and the treatment variable in terms of their difference scores. The adjusted

means were +.387 and +.353 for Learning to Learn students and controls respectively.

This difference was not statistically significant at the .05 level {F(1,54)=.070, p=.793}.

However, when AHSGPA was the covariate, there was a statistically significant

interaction {F(1,54)=5.371, p=.024}. Therefore, regression lines were plotted to examine

105

the interaction. Regression lines indicated that for controls, there was no noticeable

correlation between PFGPA and FGPA (r=.113) when adjusted for AHSGPA. However,

for Learning to Learn students, there was a moderate negative correlation (r=-.448),

indicating that the higher the AHSGPA, the greater the negative difference between the

actual FGPA and the predicted FGPA.

The Johnson-Neyman calculation identified the critical lower and upper limits at

2.39 and 3.77. No student had an AHSGPA below 2.39, but 14 of the 56 students (25%)

earned AHSGPAs of 3.77 or higher. Therefore, for students with AHSGPAs of 3.77 or

higher, control students had statistically significant higher positive difference scores than

Learning to Learn students did. However, for students (75%) with AHSGPAs lower than

3.77, there was not a significant difference between the groups in terms of their difference

scores.

Results for 1999 indicated that the assumption of equal within group slopes was

met when the covariate was AHSGPA; that is, there was not a significant interaction

between the covariate and the treatment variable. The adjusted means of the Learning to

Learn and control students in terms of the difference between their PFGPA and their actual

FGPA were +.01602 and +.01599 respectively. This difference was not statistically

significant at the .05 level {F(1,70)=.001, p=.981}.

However, there was a statistically significant interaction when the covariate was

motivation {F(1,70)=3.365, p=.078}.The first step was to plot regression lines in order to

examine the nature of the interaction. As explained earlier in this chapter, this was the

second calculation that resulted in only one critical value because there was a negat ive

106

number under the radical. The critical number for motivation level was 56.18, a point

below the intersection. Only three students (10%) had motivation scores at 56 or below.

Therefore, I concluded that there was no significant difference between the control students

and Learning to Learn students in terms of the actual FGPA in relation to their PFGPA

when controlled for motivation.

Indicator 1e: Grades earned in courses that required significant amounts of

independent reading and studying that were taken during the spring, summer, or fall

semester after the fall of the freshman year. The purpose of this part of Research Question

1 was to look at the specific indicators of grades earned in reading-intensive courses

because reading and study strategies form the most important part of the curriculum of

Learning to Learn. Grades were examined in Chemistry 1211 and 1212, Biology 1103 and

1104, History 2111 and 2112, Political Science 1101, Sociology 1101, and Anthropology

1102. ANCOVAs were employed using AHSGPA as the covariate to determine if there

were significant differences in grades earned by the two groups.

Results for 1998 indicated that the assumption of equal within group slopes was

met for each discipline when AHSGPA was the covariate; that is, there was not a

significant interaction between the covariates and the treatment variable. For science

courses, the adjusted means were 2.847 and 3.293 for the Learning to Learn and control

students respectively. This difference was not statistically significant at the .05 level

{F(1,24)=2.922, p=.101}. For history courses, the adjusted means for Learning to Learn

and control students were 2.982 and 2.981 respectively. The difference was not statistically

significant at the .05 level {F(1,25)=.000, p=.997}. For courses in sociology/anthropology,

107

the adjusted means were 3.047 and 3.520 for Learning to Learn and control students

respectively. This difference was not statistically significant at the .05 level

{F(1,25)=3.510, p=.073}. For courses in political science, the adjusted means for Learning

to Learn and control students were 2.849 and 3.049 respectively. The difference was not

statist ically significant at the .05 level {F(1,35)=.796, p=.379}.

Results for 1999 indicated that the assumption of equal within group slopes was

met for all disciplines when the covariate was AHSGPA; that is, there were no significant

interactions between the covariate and the treatment variables. For science courses, the

adjusted means were 2.938 and 2.922 for Learning to Learn and control students

respectively. The difference was not statist ically significant at the .05 level {F(1,33)=.004,

p=.953}. For history courses, the adjusted means for Learning to Learn and control

students were 2.568 and 2.794 respectively. This difference was not stat istically significant

at the .05 level {F(1,22)=.686, p=.417}. In sociology/anthropology courses, the adjusted

means for the two groups were 2.858 and 2.889. The difference was not statistically

significant at the .05 level.{F(1,34)=.048, p=.828}. For political science courses, the

adjusted means were 3.050 and 2.536 for Learning to Learn and control students

respectively. This difference was not statistically significant at the .05 level

{F(1,36)=3.953, p=.055}.

Indicator 1f: Number of course withdrawals subsequent to the first fall semester.

The purpose of the question was to examine how students were managing their course

loads. The number of withdrawals is one indication of how prepared students believe they

are for difficult courses after they receive the course syllabi and understand the

108

requirements of each course. Data were collected from spring 1999 through fall 2000 for

1998 freshman and from spring 2000 through fall 2000 for 1999 students. A course

withdrawal was counted for any course for which a student earned a W, indicating the

course was dropped after drop-add but before the mid-point of the semester. These data

were analyzed using t-tests because there was not an appropriate measure to use as a

covariate.

For 1998, the Learning to Learn students withdrew from an average of 2.68 classes

with a standard deviation of 2.86, while the control group withdrew from an average of

1.43 classes with a standard deviation of 1.14. These standard deviations indicated that the

Learning to Learn students had a greater within group variance than the controls did.

Levene �s test confirmed this lack of equality of variance so the t-statistic reflects an

adjustment for this lack of equality of variance (Huck & Cromier, 1996, p.285). The

number of course withdrawals was significantly higher for the Learning to Learn students

than the controls {t(35.342)=2.152, p=.038}.

For 1999, both groups withdrew from fewer courses than the students did in 1998

because the data were collected for fewer semesters. Levene �s test indicated that equal

variances could be assumed, and no statistically significant difference between the two

groups was found {t(70)=1.146, p=.36}. The mean for course withdrawals for Learning to

Learn students was 1.08 with a standard deviation of 1.20. The mean for control students

was .72 course withdrawals with a standard deviation of .94.

In summary, Research Question 1 compared Learning to Learn students and their

controls using multiple indicators of students � academic performance. ANCOVAs and t-

109

tests were used for these comparisons. As discussed, several statistically significant

differences were found that indicated the controls performed at a higher level academically

than the Learning to Learn students did in 1998 for students with high AHSGPAs and for

students who earned the highest number of credit hours. Conversely, for the students with

the lower AHSGPAs and who earned fewer credit hours, there was no statist ically

significant difference between the Learning to Learn students and the controls. In 1999,

there were no statistically significant differences between the two groups for any of the

dependent measures.

Research Question 3:

Is there a difference between the reported self-regulatory practices of regularly admitted

first-semester freshmen who completed Learning to Learn during fall semesters 1998 and

1999 and the reported self-regulatory practices of regularly admitted first-semester

freshmen who did not elect to take the course?

The purpose of this question was to determine if there were differences in strategic

learning behaviors between the control students and the Learning to Learn students. The

Self-Regulated Learning Inventory (Gordon, et al., 1996) was administered to all of the

students who agreed to meet with the researcher (n=60). T-tests were employed to

compare the two groups across the four sub-scales and the composite of the SRLI. Possible

scores on the four sub-scales range from 20 to 100, and the composite score ranges from

80 to 400 It is important to note that these measures of self-regulated learning were

obtained during the research process during the spring of 2001 rather than when the

110

students were beginning freshman. Therefore, they do not reflect the self-regulatory

practices of the students as they were taking classes during their freshman year.

For 1998 as indicated in Table 3, there were no significant differences between

Learning to Learn students and the controls for any of the sub-scales or the composite. For

all of the measures except cognitive processing, the Learning to Learn students had higher

mean scores than the control students, but none of these differences were significant at the

.05 level. For 1999, the control students had higher means scores on every measure, but

none of these differences were significant at the .05 level.

111

Table 3

Self-Regulated Learning Inventory Scores for 1998 and 1999________________________________________________________________________

1998 UNIV(n=16) Control (n=13) t p _______________ _______________

Factor M SD M SD

Execut ive Processing 66.13 7.67 62.54 9.63 1.12 .27Cognitive Processing 68.25 8.19 69.70 7.63 -.49 .63Motivation 73.00 8.42 70.15 8.12 .92 .37Environmental Utilization 65.00 12.02 60.08 13.44 1.04 .31Composite Score 272.06 29.13 262.46 28.51 .89 .38________________________________________________________________________ 1999 UNIV (n=14) Control (n=17) t p

________________ _______________Factor M SD M SD

Execut ive Processing 63.21 15.64 63.65 11.34 -.09 .93Cognitive Processing 64.50 13.68 67.88 9.53 -.78 .44Motivation 68.14 10.83 70.88 10.29 -.72 .45Environmental Utilization 59.14 14.30 61.82 10.88 -.59 .56Composite Score 255.00 50.14 264.82 35.71 -.67 .54________________________________________________________________________

Research Question 5:

Is there a relation between students � reported self-regulatory practices and their academic

performance?

The purpose of this question was to examine the relation between students � self-

regulatory behaviors and their academic performance. Students � academic performance was

defined as the difference between PFGPA and actual FGPA. As explained in Chapter 3, the

difference score between an individual student �s PFGPA and the student �s actual FGPA

was chosen as the best indicator of academic performance because ability and past

112

academic performance are factored into PFGPA. The composite score on The Self-

Regulated Learning Inventory (Gordon et. al., 1996) was used as the measure of self-

regulatory behaviors. Data from all students who met with the researcher (n=60) were used

for this analysis.

To answer this research question, data were analyzed using a Pearson Product

Moment Correlation Coefficient (Weinberg & Goldberg, 1990) to evaluate the strength of

the relation between self-regulatory behaviors and performance for each year. For 1998

(n=29), there was a moderate and statistically significant correlation (r=.456, p.05)

between self-regulated learning behaviors and academic performance. For 1999 (n=31),

there was only a very weak correlation (r=.167) between self-regulated learning behaviors

and academic performance.

In sum, Research Questions 3 and 5 examined the reported self-regulatory

behaviors of students and the relation between these behaviors and academic performance.

As described, t-tests found no significant differences between Learning to Learn students

and controls in terms of self-regulatory behaviors. Correlations found that, for 1998, there

was a moderate significant correlation between self-regulated behaviors and academic

performance, but for 1999, there was no correlation.

Research Question 6:

Which components of the Learning to Learn curriculum do students report helped them

successfully meet the literacy demands of their subsequent courses and regulate their own

learning processes?

113

The purpose of this question was to determine which course components were most

helpful and which were not especially helpful to students. All Learning to Learn freshmen

who met with the researcher (n=30) completed Students � Perceptions of Learning to

Learn (SPLL) (Randall, 2000a). The SPLL has six sections; the first four sections addressed

this research question and the other two sections addressed Research Questions 7 and 8.

In the four sections of the SPLL (Randall, 2000a) that addressed this question,

students were asked to provide the following information. In section one, students were

asked to evaluate six different components of the Learning to Learn curriculum using a

Likert-type scale. The six instructional components were annotating texts, taking lecture

notes, creating rehearsal strategies, time management strategies, motivational strategies,

and beliefs about knowledge and learning. Students � responses were tallied and percentages

of each response were reported for each instructional component.

In the second section of the SPLL (Randall, 2000a) students were asked to explain

the reasons for their ratings if they reported that the instructional component was very

useful (an a response) or was not at all useful (a c response) to enable the researcher to

probe further. Students � responses were tallied using their phrasing as much as possible,

and then similar responses were grouped together. In a few cases, students rated a

component either as very useful or not at all useful but did not write an explanatory

comment. Other students discussed more than one aspect of the strategy component.

Therefore, the total number of tallies does not always correspond to the number of

respondents.

114

The third and fourth sections of the SPLL (Randall, 2000a) asked the 30 students to

comment further on the instruction during Learning to Learn. The third section asked the

students to explain which of the existing components they believed should receive more

instructional time. The fourth section asked students to explain what other course features

were useful to them but had not been addressed elsewhere in the survey. For the third and

fourth section, responses were tallied and similar responses were grouped together.

The discussion of these four sections of the SPLL (Randall, 2000a) is organized as

follows. First, the Likert-type rating in the first section and the students � explanations of

their responses from the second section are addressed for each instructional component. In

this way, all of the data from one instructional component is presented together. Then the

results of the third and fourth sections are addressed.

Sections One and Two of the SPLL

Annotation instructional component. The first instructional component rated by

students was annotation of texts. Typically, instruction in this strategy teaches students to

condense their reading material into the most salient information, paraphrase the content,

and bring organization to the material. As noted in Table 4, annotation instruction was

rated as very useful by 12 of the freshmen surveyed (40%), as somewhat useful by 14

students (47%), and as not at all useful by one student (3%). Only one student (3%) was

not sure of the value of annotation instruction and two students (7%) reported that they

knew how to annotate before they took Learning to Learn.

115

Table 4

Ratings of Course Components as Measured by SPLL for 1998 & 1999 ________________________________________________________________________Inst ructional a - very b - somewhat c- not at d - unsure e - I had this skillComponent useful useful useful before L to L________________________________________________________________________

Annotations 12 (40%) 14 (47%) 1 (3%) 1 (3%) 2 (7%)

Note-taking 12 (40%) 12 (40%) 1 (3%) 0 5 (17%)

Rehearsal 15 (50%) 10 (33%) 3 (10%) 1 (3%) 1 (3%)

Time Mgmt 9 (30%) 12 (40%) 3 (10%) 0 6 (20%)

Motivation 4 (13%) 15 (50%) 2 (7%) 3 (10%) 6 (20%)

Beliefs 5 (17%) 13 (43%) 2 (7%) 5 (17%) 5 (17%)________________________________________________________________________

After rating annotation instruction, students explained why this component was

very useful or not at all useful. The advantages and disadvantage of annotation instruction

and the frequency of each response category are summarized in Table 5.

116

Table 5

Open-Ended Responses to Annotation Instruction________________________________________________________________________Frequency Advantages of annotating (very useful response)

4 The writing helped me understand the reading. 4 It was better than highlighting. 4 They helped me prepare for tests and finals, especially by saving time. 3 They helped me locate key information. 2 It helped me with memorization of material. 2 It reduced or prevented the necessity to re-read. 1 It helped me organize the material.________________________________________________________________________ Frequency Disadvantage of annotating (not at all useful response)

1 It was too t ime-consuming.________________________________________________________________________

Note-taking instructional component. The second instructional component rated

was note-taking instruction. This instruction typically includes strategies for taking notes

during lecture, organizing and editing notes after lecture, and creating study aids from the

notes for test preparation. As shown in Table 4, note-taking instruction was rated as very

useful by 12 students (40%), as somewhat useful by 12 students (40%), and as not at all

useful by 1 student (3%). No student was unsure of the value of the instruction, and five

students (17%) reported that they had this skill before they took Learning to Learn.

After rating note-taking instruction, students explained why the instruction was

very useful or not at all useful. As revealed in Table 6, on the whole, students found this

instructional component to be useful for a variety of reasons.

117

Table 6

Open-Ended Responses to Note-Taking Instruction________________________________________________________________________Frequency Advantages of note-taking instruction (very useful response)

4 It helped me clarify what would be important to know. 3 My lecture notes were useful for test preparation. 1 My notes improved understanding of lecture. 1 They helped me find possible test questions.________________________________________________________________________Frequency Disadvantage of note-taking instruction (not at all useful responses)

1 It was difficult and confusing to change to a new strategy.________________________________________________________________________

Rehearsal and test preparation instructional component. The third instructional

component rated by students was rehearsal and test preparation strategies such as concept

cards, time lines, concept maps, charts, talk throughs (verbal rehearsal), and PORPE

(preparat ion for essay exams). Although not queried specifically, many students named the

strategy that they found most helpful. Two of the most frequently cited were concept

cards, which were named seven times, and verbal talk throughs, which were named five

times. As seen in Table 4, rehearsal strategy instruction was rated as very useful by 15

students (50%), as somewhat useful by 10 students (30%), and as not at all useful by three

students (10%). One student (3%) was unsure of the value of instruction, and one student

(3%) had these skills before taking Learning to Learn.

Students � responses to the open-ended questions revealed nine categories of

advantages and two categories of disadvantages. Table 7 summarizes the advantages and

disadvantages reported by students and the frequency of each response category.

118

Table 7

Open-Ended Responses to Rehearsal and Test Preparation Instruction________________________________________________________________________Frequency Advantages of test preparation instruction (very useful response)

3 The repetition in the writing process helped. 2 The variety of strategies helped in different courses. 2 The strategies helped me monitor my learning. 2 The strategies helped clarify the material so I could understand it

better. 2 The strategies helped me organize the material to be learned. 1 They helped me manage the heavy information load. 1 They provided information visually. 1 They helped me understand how the memory process works. 1 Many of the strategies are portable.________________________________________________________________________Frequency Disadvantages of test preparation instruction (not at all useful

responses)

1 They were too time-consuming to make. 1 They require too much in-depth information.________________________________________________________________________

Time management instructional component. The fourth strategy component that

students rated was instruction on time management techniques. This instructional

component typically includes ideas for setting priorities, analyzing time usage, scheduling,

balancing major responsibilities, pacing of reading and studying, and the relation between

time management and motivation. As indicated in Table 4, instruction in time management

strategies was rated as very useful by nine students (30%), as somewhat useful by 12

students(40%), and not at all useful by three students (10%). No student was unsure of

119

the value of time management instruction and six students (20%) believed they had good

time management skills before they took Learning to Learn.

Students � explanations of their ratings for time management instruction revealed

seven different reasons why the instruction was very useful and two reasons why it was not

at all useful.. Table 8 summarizes these explanations of the advantages and disadvantage of

time management instruction and provides the frequency of each response category.

Table 8

Open-Ended Responses to Time Management Instruction________________________________________________________________________Frequency Advantages of time management instruction (very useful responses)

2 It helped me set priorities, balancing academics and other activities. 2 A written schedule helped me with organization. 1 It helped me evaluate my current schedule. 1 It increased my feeling of being prepared for exam. 1 I saw the importance of managing my time carefully.________________________________________________________________________Frequency Disadvantage of time management instruction (not at all useful

responses)

2 My lack of personal motivation interfered with any improvement intime management.

________________________________________________________________________

Motivational instructional component. The fifth strategy component that students

rated was instruction in motivational strategies. This instruction typically includes short and

long term goal setting activities, the use of incentives, and a discussion of the relation

between strategy use, academic success, and increased motivation. As was illustrated in

Table 4, instruction in motivational strategies was rated as very useful by four students

(13%), as somewhat useful by 15 students (50%), and as not at all useful by two students

120

(7%). Three students (10%) were unsure of the value of instruction in motivation and six

students (20%) believed they were well motivated prior to taking Learning to Learn.

In their explanations of their responses, students offered only four reasons why

instruction in motivation was useful and two reasons why it was not. Table 9 summarizes

these responses.

Table 9

Open-ended Responses to Motivation Instruction________________________________________________________________________Frequency Advantages of time management instruction (very useful responses)

1 Incentives discussed in class helped me reach my goals. 1 Encouragement from other students was helpful. 1 Learning to pace readings prevented boredom and helped with

motivation. 1 Strategies made learning less overwhelming and increased

motivation.________________________________________________________________________Frequency Disadvantage of motivation instruction (not at all useful responses) 2 I made no effort to implement suggestions so I saw no improvement.________________________________________________________________________

Beliefs about knowledge and learning instructional component. The final

instructional component that students rated was instruction in beliefs about knowledge and

learning. Usually, this instructional component includes discussions of epistemological

beliefs, attribution theory, and the Tetrahedral Model of Learning (Jenkins, 1979). As was

indicated in Table 4, instruction in beliefs about knowledge and learning was rated as very

useful by five students (17%), as somewhat useful by 13 students (43 %), and as not at all

useful by two students (7%). Five students (17%) were unsure of the value of the

121

instructional component and five students (17%) believed they had this knowledge before

they took Learning to Learn.

Only four students explained their rating of instruction in beliefs about knowledge

and learning. Table 10 summarizes these responses.

Table 10

Open-Ended Responses to Beliefs Instruction________________________________________________________________________Frequency Advantages of beliefs instruct ion (very useful responses)

1 I learned about the relation between beliefs and actions. 1 I learned about myself as a learner. 1 I learned about how memory works.________________________________________________________________________Frequency Disadvantage of beliefs instruct ion (not at all useful responses)

1 This information did not help me with my studying.________________________________________________________________________

Section Three and Four of the SPLL

The third section of the SPLL (Randall, 2000a) asked the 30 Learning to Learn

students to explain how they would like to see existing course components expanded. Four

or more students (an average of 15%) suggested that more practice would be useful in one

or more of the following areas: (a) in-depth work with time management, (b) taking lecture

notes, (c) annotating, especially in a greater variety of courses, and (d) rehearsal strategies,

especially talk throughs. Two students (about 7%) suggested that more work in the

following areas would be useful: (a) reading rate, (b) storing information in long term

122

memory for test preparation, and (c) task analysis and strategy application in other courses

students were taking.

The fourth section of the SPLL (Randall, 2000a) asked the 30 students to explain

other features of Learning to Learn that had been especially helpful but had not been

queried on the survey. Four students (13%) reported that the reading rate component was

especially beneficial. Three students (10%) reported that instruction in how to apply

strategies in other classes was very useful. Three students (10%) explained that interaction

with a supportive Learning to Learn instructor was particularly useful. Two students

(about 7%) suggested that other useful areas of instruction were the practice on essay

writing using PORPE and the small class discussions.

Research Question 7:

What suggestions do students have for additions or omissions to the Learning to Learn

curriculum?

The last two sections of the SPLL (Randall, 2000a) addressed this research

question. The purposes of this question were twofold: (a) to determine what academic

needs students perceived that they had but were not addressed in Learning to Learn and

(b) to determine what parts of Learning to Learn did not seem relevant to students �

academic lives. All Learning to Learn students who met with the researcher (n=30) were

asked for their ideas for additions or omissions that would improve Learning to Learn. The

most frequent suggestion for a course addition, cited by nine students (almost 30%), was a

need for greater application of Learning to Learn strategies to other courses, including

math, science, English, business, and foreign languages. Each of the three areas of library

123

research, test taking skills, and group discussions were cited as other areas of need by three

students (10%). In terms of course features that should be omitted, students reported only

a few ideas. Five students (about 17%) explained that the simulated course units in history

and psychology taught in Learning to Learn were not helpful and should be omitted. Three

students (10%) suggested that the instruction in motivation was not useful and should be

omitted. Three other students suggested omitting instruction in beliefs about learning.

In summary, Research Quest ions 6 and 7 examined students � evaluation of the

Learning to Learn curriculum. In general, many students found the instructional

components of annotation, note-taking, and rehearsal strategies very useful. A smaller

number of students reported that instruction in time management, mot ivation, and beliefs

about learning were very useful. Students also explained why each instructional component

was useful or not. Finally, students made suggestions about which existing instructional

components might be expanded, which components might be added, and which ones might

be omitted from the Learning to Learn curriculum.

Research Question 8:

Do students transfer the literacy strategies taught in Learning to Learn to the active

reading, note-taking, and rehearsal/test preparation required in subsequent courses that

have a heavy reading load?

The purpose of this question was to examine the nature of the transfer of strategies

learned in Learning to Learn to other courses because successful transfer is the ultimate

goal of the course. The survey, Transfer of Learning to Learn Strategies (TLLS) (Randall,

2000b), asked students to describe how and to what extent they transferred the skills

124

learned in Learning to Learn to a difficult reading-intensive course they had completed the

semester immediately prior to meeting with the researcher. All of the Learning to Learn

students who met with the researcher (n=30) responded to the TLLS. After all of the

students � surveys were completed, the researcher grouped the target courses together into

disciplinary categories based on descriptions provided in The University of Georgia

Undergraduate Bulletin (Office of Undergraduate Admissions, 2000- 2001) and

consultation with three other instructors from different disciplines. The following four

categories of target courses resulted: (a) laboratory sciences, including biology, chemistry,

anatomy, biochemistry, and marine sciences; (b) social science courses, including

psychology, sociology, political science, and linguistics; (c) history; and (d) business related

courses, including economics, management, public relations, accounting, and legal studies.

The one linguistics course was grouped with the social sciences because the emphasis in the

particular course is on the relation between the study of language structures and other

social sciences. Legal studies was grouped with business courses because, at UGA, legal

studies is part of the business school and all courses are related to business law. Six

students responded in reference to a targeted science class, eleven in reference to a target

class in the social sciences, five in reference to a targeted history class, and eight in

reference to a targeted business class.

Before proceeding with an examination of the students � transfer activities, general

descriptive data on the four categories of courses were calculated. First, the average

difficulty level of the courses in each category, as rated by the students, was calculated on a

scale from one (easy), through two (average), to three (difficult). Second, the average final

125

course grade earned by the students participating in the study was calculated for each

course category using data available from the University computer-based record system

(i.e., F=0, D=1, C=2, B=3, and A=4). Third, based on student report, the number of

different instructors experienced by students in each category was determined.

A summary of this descriptive data is provided in Table 11. First, the average level

of difficulty for science, history, and business, as reported by students, were very similar at

approximately 2.8 on a 3-point scale. Social science classes were rated as less difficult with

an average of 2.45. Second, final course grades earned by students were highest in the

sciences (3.0), next highest in social sciences (2.91), third highest in business (2.88), and

lowest in history (2.6). For the purposes of this study, an individual grade of B or higher

was the criterion set by the researcher as an indicator of academic success. Although a C

was at one time considered an average grade, today a student who earns a majority of Cs

loses the HOPE scholarship. Finally, students reported on courses taught by 26 different

instructors, with no duplications in history or the social sciences and two duplications each

in science and business.

126

Table 11

Summary of Target Courses on which Students Reported ________________________________________________________________________

Type of information Sciences Social Sciences History Business

Number of students 6 11 5 8

Average level of 2.83 2.45 2.8 2.88course difficulty

Final grades earned 3.00 2.91 2.6 2.88by respondents

Number of instructors 4 11 5 6represented________________________________________________________________________

In order to examine students � transfer of strategies in detail, the information

requested on the TLLS (Randall, 2000b) was organized by the three instructional

components of annotating, note-taking, and rehearsal strategies. These three components

were selected from the total of six instructional components evaluated by the SPLL

(Randall, 2000a) because they are the core academic strategies taught in Learning to

Learn. For each of the three instructional components, students were asked about the

format of their strategy, how they paced themselves as they created the strategy, and how

they used the strategy when they studied for tests. The TLLS provided several choices

from which students might pick a response for questions on format, pacing, and usage of

strategies. Students were also offered an other option for format, pacing, and usage so they

might explain their personal modifications of the strategy. Students � responses were read,

127

tallied, and then organized by course category. Multiple answers were often appropriate

and some students did not respond to every sect ion; therefore, the number of responses did

not always correspond to the number of respondents. Finally, for each of the three strategy

components, students were asked to explain how and why they modified the strategy over

the course of the semester. Students � responses for each instructional component are

discussed separately.

Transfer of Annotation Strategies

Of the 30 Learning to Learn students surveyed, four out of six science students,

seven out of eleven social science students, one out of five history students, and two out of

eight business students reported annotating in their target course. Therefore, the data on

annotation in Table 12 represent the responses of 14 of the 30 students (47%) who

completed the survey.

128

Table 12

Transfer of Annotation Strategy to Subsequent Courses________________________________________________________________________Type of transfer Science Soc.Sci. History Business Totals

(n=4 of 6) (n=7 of 11) (n=1 of 5) (n=2 of 8) (n=14 of 30)________________________________________________________________________Format a. in text margins 2 7 1 2 12 (86%) b. on sticky notes 0 1 0 0 1 (7%) c. back of previous page 0 0 0 0 0 d. paper strips 0 0 0 0 0 e. other L to L method 0 1 0 0 1 (7%) f. other 2 0 0 0 2 (14%)

Pacing of annotating a. almost daily 1 2 0 0 3 (21%) b. once or twice weekly 2 2 1 0 5 (36%) c. 1 - 2 days before test 1 3 0 2 6 (43%) d. other 0 0 0 0 0

Usage of annotations for studying a. read them over 0 5 0 2 7 (50%) b. covered and tested self 0 2 1 2 5 (36%) c. talked them through 0 3 0 0 3 (21%) d. studied almost daily 1 0 0 0 1 (7%) e. studied 1 - 2 1 5 1 1 8 (57%) days before test f. other 2 0 0 0 2 (14%)

Change in format, pacing or usage a. yes 1 0 0 0 1 (7%) b. no 3 7 1 2 13 (93%)________________________________________________________________________

As shown in Table 12 in terms of format, most students who annotated (86%) did

so in the margins of their text books. Only two students used other methods taught in

Learning to Learn, including using sticky notes. Two science students explained that they

129

modified the annotation format because there was so much detailed information to learn

from the text; therefore, they took notes on notebook paper rather than trying to fit it in the

margins of the text.

In reference to the pacing of reading and annotating, the majority of the students

paced themselves by annotating one or two days before their tests (43%) or once or twice

weekly (36%). Only three students (21%) reported annotating almost daily.

As indicated in Table 12, in terms of usage of annotations for studying, seven

students (50%) reported that they read over their annotations. Only five students (36%)

covered the material in the annotations and tested themselves. Three students (21%) orally

talked through their annotations as they studied. Two students selected the other response,

indicating that they modified their use of annotations while they studied. One science

student reported that, because the test questions were so detailed, it did not pay to study

the annotations. A second science student realized that the process of annotating resulted

in learning much of the material; therefore, this student transferred only the material that

was not yet learned to note cards and studied the cards instead of the annotations. No

matter how they used their annotations, most of these students (57%) did not begin to

study their annotations until one or two days before their test.

In reference to the question about making a change as the semester progressed,

most students (93%) did not adjust their annotation strategy as the course progressed. Only

one science student changed the method of annotating by reducing the volume of

annotations because he was spending too much time on science to the detriment of his

other courses.

130

Overall, half of the students, a significant number of those surveyed, chose not to

annotate at all. In terms of the six target classes in the sciences, only two students did not

annotate. One chose not to because there was so much important information that it was

more effective to construct other strategies instead, such as study guides and flow charts.

The other student reported that the lecture notes contained all of the important information

and that detailed annotations were not necessary.

For the social science classes, four of the eleven students did not annotate at all.

One student reported that, because of a photographic memory, annotations were not

necessary. Another reported a preference for highlighting and outlining. Two students

reported that the most important information came from their instructors � lectures so

annotating was unnecessary.

For history courses, four of the five students did not annotate, and their

explanations of why they did not annotate were varied. The total number of explanations

exceeds four because many students provided more than one reason. One student gave

each of the following reasons for not annotating in history: (a) he was too lazy, (b) he

didn �t even think about the possibility of annotating, (c) he had forgotten how to annotate,

(d) he believed that everything important was discussed in class so he did not need to

annotate, (e) he believed that annotating was not necessary or effective because all of the

readings were novels, and (f) he believed that annotations were not possible because the

texts were so detailed.

For business courses, six of the eight students did not annotate at all. They

explained a variety of reasons. Three students reported that the instructor covered the text

131

in his lectures and that they filled in notes using an outline prepared by their instructor. A

fourth student reported a lack of skill in annotating, and for a fifth student, time

management was such a problem that there was not time to annotate. A final student

decided that memorization of exact phrases from the text was more effective than trying to

understand the information.

Transfer of Note-Taking Strategies

Of the thirty students who completed the TLLS (Randall, 2000b), five out of six

science students, ten out of eleven social science students, all of the history students, and

five out of eight business students took notes during lectures in their target courses.

Therefore, the data in Table 13 represent the note-taking activities of 25 of the 30 students

(83%).

132

Table 13

Transfer of Note-Taking Strategies to Subsequent Courses ________________________________________________________________________Type of transfer Science Soc. Sci. History Business Totals

(n=5 of 6) (n=10 of 11) (n=5 of 5) (n=5 of 8) (n=25 of 30)________________________________________________________________________Format a. no specific format 2 6 4 3 15 (60%) b. predicted questions on 1/3 0 0 0 0 0 c. ½ lecture and ½ text 0 0 0 0 0 d. with summary or retrieval 1 1 0 0 2 (8%)

cues at the bottom e. another �L.to L. � method 0 1 0 0 1 (4%) f. other 2 2 1 2 7 (28%)

Pacing of working with notes a. daily 1 0 1 0 2 (8%) b. weekly 1 7 2 3 13 (52%) c. night or two before test 0 2 1 2 5 (20%) d. other 3 1 1 0 5 (20%)

Usage of notes for studying a. combined text and notes 5 9 3 4 21 (84%) b. rewrote or edited 2 3 1 1 7 (28%) c. tested self on material 0 4 4 1 9 (36%) d. highlighted key points 5 4 2 2 13 (52%) e. read them over 3 9 5 2 19 (76%) f. summarized main points 2 5 2 1 10 (40%) g. outlined 0 1 1 1 3 (12%) h. other 1 0 0 1 2 (8%) Change in format, pacing, or usage a. yes 1 0 2 1 4 (16%) b. no 4 1 3 4 12 (48%)________________________________________________________________________

In terms of format, 15 students (60%) reported that they used no specific format.

In other words, they did not use a format taught in Learning to Learn, but probably used

133

whatever they had used in the past. Only three students (12%) followed a format taught in

Learning to Learn. A total of seven students (28%) selected the other option for the

format of their note-taking. These students represented all the disciplines and stated that

they used their instructors � outlines or printouts, either from a web site or from an

overhead in the classroom, and then added important details during the lecture or predicted

test questions.

For pacing, a slight majority of students (52%) worked with their notes in some

way every week instead of waiting until the night or two before a test. Only two students

(8%) reported working with their notes on a daily basis. In reference to pacing, five

students provided explanations for other responses. However, only three of these other

responses actually clarified their pacing as they took notes. Of these three, one science

student reported that he worked with his notes for the two weeks before a test, and a

second science student reported that he worked with his notes the weekend before and the

night before a test. One history student reported waiting to use notes until the instructor

provided a list of terms on a weekly basis, and then he used the notes only for finding

definitions.

In terms of usage of notes for studying, students reported a large variety of

activities while they prepared for tests. Students most often reported that they did one or

more of the following: (a) 21 students (84%) combined information from their notes and

text, (b) 19 students (76%) read over their notes, (c) 13 students (52%) highlighted key

points, (d) 10 students (40%) summarized main points, (e) nine students (36%) tested

themselves on key information, and (f) seven students (28%) rewrote and edited their

134

notes. Only two students recorded other responses indicating that they modified their

method of studying with their notes. A business student made note cards from lecture notes

and a science student used the notes only to answer study questions provided by the

instructor.

Four students changed their method of note taking as the semester progressed. A

science student realized that lectures did not provide enough background information and

began to combine information from the text with lecture notes. Two history students did

very poorly on the first test so they began to take more detailed notes. One business

student began to record more details after finding out what kind of information would be

on tests.

Of the 30 students surveyed, only six did not take lecture notes. One science

student believed that commercial notes were superior and chose not to take notes in

lecture. One social science student chose not to take notes during lecture because the

instructor provided a complete set of notes on the web. All history students took notes.

Three business students reported that they did not take notes during lectures. One student

said that the instructor provided copies of notes before class and so it was only necessary

to make additions to them during class. A second business student reported that the

instructor worked homework problems during class and did not lecture. The third business

student said that by listening, there was less risk of missing key information than there was

when time was spent writing notes.

135

Transfer of Rehearsal Strategies

Of the 30 Learning to Learn students surveyed, all six science students, ten of the

eleven social science students, all five history students, and six of the eight business

students used one or more rehearsal strategy in their target course. Therefore, the data

summarized in Table 14 represent the responses of 27 of the 30 students (90%) who

completed the survey.

136

Table 14

Transfer of Rehearsal Strategies to Subsequent Courses________________________________________________________________________ Type of transfer Science Soc.Sci. History Business Total

(n=6 of 6) (n=10 of 11) (n=5 of 5) (n=6 of 8) (n=27 of 30)________________________________________________________________________ Format a. constructing concept cards 4 8 3 3 18 (67%) b. studying old tests 4 2 0 2 8 (30%) c. constructing concept maps 3 1 0 0 4 (15%) d. predicting/answering questions 1 6 4 5 16 (59%) e. constructing charts 2 1 1 0 4 (15%) f. PORPE for essay writing 0 3 3 0 6 (22%) g. time lines 0 1 0 0 1 (4%) h. completing practice tests 3 4 2 0 9 (33%) i. using study groups 3 6 3 4 16 (59%) j. using study schedule 3 4 2 2 11 (41%) k. using talk throughs 2 7 0 2 11 (41%) l. practice solving problems 2 2 0 2 6 (22%) m. other 0 0 0 1 1 (4%)

Pacing of strategy construction a. continuously 1 1 0 0 2 (7%) b. week before test 4 7 2 6 19 (70%) c. night before test 1 2 3 0 6 (22%) d. other 1 0 0 0 1 (4%)

Usage of strategy for studying a. read over information 5 11 5 6 27 (100%) b. testing oneself 3 4 1 1 9 (33%) c. sorting and reducing info. 3 4 1 2 10 (37%) d. talking aloud 4 7 3 2 16 (59%) e. studied with peers 3 5 3 1 12 (44%) f. other 0 0 0 0 0

Change in format, pacing, or usage a. yes 3 3 1 1 8 (30%) b. no 3 7 4 5 19 (70%)________________________________________________________________________

137

In terms of format as shown in Table 14, the 27 students reported using a wide

variety of rehearsal strategies. The most frequently used were the following: (a) 18

students (67%) constructed concept cards, (b) 16 students (59%) predicted questions and

answered them, (c) 16 students (59%) attended study groups, (d) 11 students (41%) made

a study schedule, (e) 11 students (41%) conducted verbal talk throughs, (f) 9 students

(33%) completed practice tests, and (g) 8 students (30%) studied old tests. Other strategies

(i.e., maps, charts, PORPE, time lines, and practice problems) were used by six or fewer

students. Only one business student explained that he used the additional format of

studying notes from previous semesters.

In reference to pacing, a large majority of students (70%) began making strategies a

full week before a test. Only two students reported working on constructing strategies on a

regular basis. One science student selected the other response and reported creating

strategies constantly.

In terms of usage of strategies for studying, students reported a variety of activities:

(a) all 27 students read over the information on the strategy, (b) 16 students (59%) talked

about the information aloud, (c) 12 students (44%) studied the information on the strategy

with a peer, (d) 10 students (37%) sorted and reduced the information on the strategy, and

(e) nine students (33%) tested themselves on the material contained in the strategy. No

student recorded an other response for usage of rehearsal strategies for studying.

Eight students (30%) modified their rehearsal strategies as the semester progressed.

One science student began to use practice tests located on the instructor � s web site, another

science student began to study with a classmate to increase comprehension, and a third

138

science student learned to omit details from strategies that would not be covered on the

test . Three social science students also made changes in their rehearsal strategies. One

student began constructing strategies more in advance and rehearsing them more

frequent ly. Another student began to include only information provided by the instructor in

the form of lecture notes, outlines, and study guides. A third social science student was

able to stop reviewing concept cards from the beginning of the semester because that

material had been mastered. One history student explained that not using any strategies

resulted in an F on the first exam so he used four different rehearsal strategies on

subsequent exams and eventually began to earn Cs on exams. One business student found

out that, although notes and text were straight-forward and factual, the tests required

concept application; therefore, this student began predicting questions that would result in

thinking through how to apply concepts.

Of the 30 Learning to Learn students surveyed, only two reported using no

rehearsal strategies. One social science student said that the tests were essays that required

analysis of theories and that further rehearsal of the information was unnecessary. A

business student reported reading the chapters as refresher for the material discussed in

class.

In sum, the 30 Learning to Learn freshmen from 1998 and 1999 reported using a

wide variety of strategies in their target courses. For annotations, a majority of students

chose to annotate in the text margins, to annotate a few nights before a test or once or

twice a week, and to read over their annotations as they studied the night or two before a

test. In terms of note-taking, students did not transfer any particular method taught in

139

Learning to Learn. They worked with their notes about once a week rather than waiting

until the night before the test, and they used their notes in a variety of ways to study for

tests. In reference to rehearsal strategies, students used many strategies that were taught in

Learning to Learn. A majority of students waited until the week before a test to construct

the strategies and usually just read over the information contained in the strategy.

Probationary Students

The second category of participants were students on academic probat ion during

fall semesters 1998 and 1999 and who completed Learning to Learn during the respective

fall. At the end of each semester, all students who are experiencing serious academic

difficulty at UGA are assigned one of the following levels of academic standing: (a)

probation, (b) continued probation, (c) first dismissal, (d) second dismissal, or (e) cleared

probation. All of the participants in this category were on probation the semester they took

Learning to Learn.

Of the 68 probationary students who completed Learning to Learn during these

two fall semesters, twenty-five were still enrolled at the time of this study. As discussed in

Chapter 3, because of the high attrition rate of probationary students and the serious

difficulty the researcher encountered in accessing accurate data on the probationary

students st ill at the University, the researcher was successful in contacting only eight

students. Only six of these eight were willing to participate in the study. Four of the

students completed Learning to Learn during the fall of 1998 and two completed the

course during fall 1999.

140

Baseline admissions data reveals the following student characteristics. The average

SAT-V scores were 558 with a range from 440 to 670. One student did not have an SAT-

V score posted. Their average adjusted high school grade point average (AHSGPA) was a

2.86 with a range from 2.34 to 3.17. Two students did not have an AHSGPA posted. The

average PFGPA for the probationary students was a 2.33 with a range from 2.33 to 2.49.

Three students had no predicted freshman grade points average (PFGPA) posted.

This section presents the research findings for each of the six research questions

that were relevant to probationary students. The data were analyzed using descriptive

statistics, and the findings for both 1998 and 1999 were combined for the purposes of

reporting results because of the small number of participants. As discussed in Chapter 3,

students at UGA are placed on academic probation whenever their cumulative GPA drops

below a 2.0. All of the six participants were placed on academic probation at the end of

their first semester at UGA, with the exception of one student who was not placed on

probation until the end of his second semester.

Informal interviews with these students revealed important information about the

early semesters of their college careers. Five of these students were heavily involved in

activities such as club sports, fraternities, and jobs. These students reported that academics

were not a priority and that they rarely went to class or studied. These five believed they

could have done well academically if they had put forth more effort, although three of them

admitted that their high school careers did not prepare them for the rigor of academics at

UGA. Only one transfer student reported that the academics were very difficult despite his

efforts at studying.

141

It would have been useful to know the exact cumulative GPA of each student at the

time of Learning to Learn enrollment in order to have a point of comparison for future

semesters. However, it is difficult to determine with any accuracy a cumulative GPA from

a previous semester. The University �s computer-based record system provides semester

GPAs for each semester and cumulative GPA at the time the records are accessed;

however, previous cumulative GPAs are not available.

Research Question 2:

Did the academic performance of probationary students change after completion of

Learning to Learn during fall semesters 1998 and 1999?

The purpose of this question was to determine any change in the academic

performance of probationary students after the completion of Learning to Learn. This was

measured in four ways. First , semester GPAs were examined for fall semester of Learning

to Learn enrollment. Second, semester GPAs for each semester after completion of

Learning to Learn through fall 2000 were examined. Third, student records were searched

to locate any change in academic status following Learning to Learn. Fourth, grades were

compiled for reading-intensive courses that were taken by the probationary students either

concurrent with Learning to Learn or in subsequent semesters through fall 2000.

Table 15 summarizes the semester GPAs of the six probationary students, starting

with their fall GPA during Learning to Learn enrollment and ending with fall 2000. As the

data suggest, there was not a definite pattern across the six students in terms of GPA gains.

The number of semesters for each student varies depending on the year of Learning to

Learn enrollment, a possible period of dismissal, and possible summer school enrollment.

142

The four students who were enrolled the spring semester immediately following their

completion of Learning to Learn, earned semester GPAs of 1.50 (student #5), 2.50 (#3),

2.69 (#6), and 3.00 (#1). Therefore, three of these students improved their academic

performance well above the critical 2.0 level, while only one student (#5) continued to

perform below that critical level. The two other students were not enrolled the semester

following completion of Learning to Learn because they were on dismissal. However,

these two students returned to school the following fall semester and earned a 3.00 (#2)

and a 3.67 (#4), demonstrating a substantial improvement. During all the semesters from

Learning to Learn completion through fall 2000, four of the six students (#2, #3, #4, and

#6) continued to keep semester GPAs at 2.00 or above. Only two of the students (#1 and

#5) earned GPAs that dropped below the critical 2.0 level. In summary, four of the six

students made consistent and notable GPA gains after completion of Learning to Learn.

143

Table 15

Semester Grades of Probationary Students________________________________________________________________________1998

Student Fall 98 Spring 99 Fall 99 Spring 00 Summer 00 Fall 00 SRLI (of L to L)

#1 1.50 3.00* 1.25 2.25 1.00 222

#2 1.40 dismissal 3.00 3.36* 2.57 2.42 329

#3 2.67 2.50 dismissal dismissal 2.60 267

#4 1.33 dismissal 3.67* 3.00 2.33 285________________________________________________________________________1999

Student Fall 99 Spring 00 Summer 00 Fall 00 SRLI (of L to L)

#5 2.50 1.50 2.60 296

#6 2.50 2.69* 2.00 2.64 280________________________________________________________________________Note. * indicates the student cleared probation at the end of the semester

Second, the academic standing for these six students was examined. As shown in

Table 15, two of the six students ( #1 and #6) cleared academic probation at the end of the

spring semester immediately following completion of Learning to Learn. Two other

students ( #2 and #4) cleared probation within the next academic year. Two students (#3

and #5) had not cleared probation by the end of fall 2000. One of these two cases (#3) was

a transfer student who had attended three institutions prior to UGA and who had earned

126 hours before he took Learning to Learn. His grades had improved to about a 2.50

144

average for each semester after Learning to Learn; however, because he had so many

credit hours, each semester made only a minimal impact on his overall GPA.

Third, grades in reading-intensive courses were examined. Only grades earned in a

discipline for which more than one student completed a course were reported. For

example, two students took economics so that average was reported, but only one student

took philosophy so that grade was not reported. This decision was made so that the

performance in any one discipline would not be measured by the grades of just one student.

Grades in the following courses were tallied and averaged with the following results: (a)

economics, 2.33; (b) sociology, 2.33; (c) geography, 3.0; (d) anthropology, 1.5; (e) history,

2.7; (f) foreign languages, 2.95; (g) foods and nutrition, 2.66; and (h) psychology, 1.0. It

appears that the six probationary students had the most difficulty with two of the social

science classes, psychology and anthropology. Students earned the highest grades in

geography and foreign languages.

Research Question 4:

What are the reported self-regulatory practices of probationary students who completed

Learning to Learn during fall semesters 1998 and 1999?

The six probationary students took the SRLI (Gordon, et. al., 1996). Their

responses were tallied, and the mean scores were computed for each sub-scale and for the

composite score. The mean responses for all sub-scales ranged from 68 to 74. This

indicates that the probationary students reported that the strategic behaviors reflected on

the SRLI were somewhat typical or frequently typical of them, responses that were either a

145

3 or 4 on the Likert-type scale. Table 16 summarizes the students � individual scores, the

mean scores, and the ranges.

Table 16

Self-Regulated Learning Inventory Scores for Probationary Students________________________________________________________________________

Student M range________________________________#1 #2 #3 #4 #5 #6

FactorSub-scales*

Executive Proc. 56 82 63 74 75 72 70 56-82Cognitive Proc. 60 80 62 64 74 68 68 60-80Motivation 56 90 71 79 77 69 74 56-90Environ. Utiliz. 50 77 71 68 70 73 68 50-77

Composite score** 222 329 267 285 296 282 280 222-329________________________________________________________________________Note. * Out of a possible 100, Out of a possible 400

Research Question 5:

Is there a relation between students � reported self-regulatory practices and their academic

performance?

With such a small number of part icipants, it was hard to analyze the relationship

between academic performance, as measured by GPA, and self-regulated learning

behaviors, as measured by the SRLI (Gordon, et. al., 1996). Analysis through a correlation

was not possible because there were so few students and because there was not a GPA

score that could be used as a consistent measure of performance for all of the students, as

can be seen in Table 15. The SRLI composite scores for the six probationary students

ranged from a low of 222 to a high of 329. Most of the scores clustered around 267 to 296

with a mean of 280. The low score of 222 was 45 points lower than the next lowest score

146

and 58 points lower than the mean. The academic performance of the student (#1) who

earned the lowest composite score (222) on the SRLI can be seen in Table 15. This student

consistently had the lowest semester grades of any of the probationary students, except for

the semester immediately after completion of Learning to Learn, usually averaging a low C

or D for a semester. The student (#2) with the highest composite score earned a 329, 33

points higher than the next highest score and 49 points higher than the mean. After his

return from dismissal during spring 1999, he maintained grades ranging from 2.42 to 3.67

for the next four semesters. Therefore, there seemed to be some relation between the self-

regulated learning behaviors and academic performance of the students who scored at the

low and high ends of the composite SRLI..

Research Question 6:

Which components of the Learning to Learn curriculum do students report helped them

successfully meet the literacy demands of their subsequent courses and regulate their own

learning processes?

This question was addressed by the Students � Perceptions of Learning to Learn

(SPLL) (Randall, 2000a) in the same manner as for freshmen reported earlier. The

discussion of the first four sections of the SPLL is organized as follows. First, the Likert-

type rating in the first section and the students � explanations of their responses from the

second section are addressed for each instructional component. In this way, all of the data

from one instructional component is presented together. A few students did not explain

why they answered either very useful or not at all useful; therefore, the number of

147

explanations do not always equal the number of responses. Then the results of the third and

fourth sections are addressed.

Section One and Two of the SPLL

Annotation instructional component. The first instructional component rated by the

probationary students was annotating texts. Of the six students, one reported that

annotation instruction was very useful, and five reported that it was somewhat useful. No

student said it was not at all useful. The student who explained why it was very useful said

that he found it easy to learn from what he had written in his own words, so he did not

need to reread the text before a test.

Note-taking instructional component. The second component rated was note-taking

instruction. Four of the six students reported that note-taking instruction was very useful.

Two reported that it was somewhat useful. No student said it was not at all useful.

Students reported that the instruction was very useful for several reasons. One student

learned to organize his notes better, and a second student thought that his notes were a

good visual study tool. Two students agreed that their notes helped them prepare better for

tests because they learned to identify what their instructors wanted them to know.

Rehearsal and test preparation instructional component. The third instructional

component rated was rehearsal and test preparation strategies. Four students reported that

this instruction was very useful, one student reported that the instruction was somewhat

useful, and one said that it was not at all useful. Two of the students who reported that

rehearsal strategies were very useful said that concept mapping and concept cards were

especially helpful and that they still use them. Another student said that rehearsal strategies

148

were especially helpful because they provided a visual aid for studying, and they made it

easy to study with other students. The one student who reported that rehearsal strategies

were not at all useful explained that he would rather study his annotations and lecture

notes, with the exception of concept cards for memorization.

Time management instructional component. The fourth instructional component

rated was time management instruction. Two students reported that it was very useful and

four students reported that it was somewhat useful. No student reported that it was not at

all useful. One student who answered very useful reported that he learned to study every

night and to also use daytime hours for studying. The other student who responded very

useful reported that better time management strategies made his test days less stressful.

Motivation instructional component. The fifth component was instruction in

motivational strategies. No student reported that motivation instruction was very useful.

Four of the six students reported that this instructional component was somewhat useful.

Two students reported that the component was not at all useful; one of them explained

that it did not help to motivate him.

Beliefs about knowledge and learning instructional component. The sixth

instructional component rated was instruction in beliefs about learning. One student rated

this instructional component as very useful, four rated it somewhat useful, and one rated it

as not at all useful. The student who found it very useful reported that it helped him see

that he had a habit of trying to justify his poor performance rather than analyzing it more

critically. The student who rated this instructional component as not at all useful reported

it was interesting to learn but it had no personal impact on him.

149

Section Three and Four of the SPLL

The third section of the SPLL (Randall, 2000a) asked the six probationary students

to explain how they would like to see the exist ing instructional components expanded. Only

three students had suggestions. Two students said they would like to see more time

devoted to time management instruction. One of these two students also wanted more

emphasis on annotations. A third student wanted more time spent on note-taking.

The fourth section of the SPLL (Randall, 2000a) asked the probationary students to

explain other features of Learning to Learn that had been especially helpful but had not

been queried on the survey. Only one student responded to this question, and he said that

the reading rate project was useful.

Research Question 7:

What suggestions do students have for additions or omissions to the Learning to Learn

curriculum?

The last two sections of the SPLL addressed this research question. The purposes

of this question were twofold: ( a) to determine what academic needs probationary students

thought they had but were not addressed in Learning to Learn, and (b) to determine what

parts of Learning to Learn did not seem relevant to probationary students � academic lives.

There were three suggestions for additions to Learning to Learn. One student

wanted more emphasis on taking multiple choice exams. This student explained that he

knew it was possible to do well on multiple choice questions with a minimal amount of

knowledge if a student could just learn how to recognize correct answers. A second

150

student suggested that a research skills component be added, and a third student suggested

that stress management techniques would be a good addition to the course.

Only one student suggested omitting an instructional component. He suggested

that annotating was an � unnatural process � and should be omitted from the curriculum.

Research Question 8:

Do students transfer the literacy strategies taught in Learning to Learn to the active

reading, note-taking, and rehearsal/test preparation required in subsequent courses that

have a heavy reading load?

The purpose of this question was to examine the nature of students � transfer of

strategies learned in Learning to Learn to other courses because successful transfer is the

ultimate goal of the course. The survey, Transfer of Learning to Learn Strategies (TLLS)

(Randall, 2000b), asked probationary students to describe how and to what extent they

transferred the strategies learned in Learning to Learn to a difficult reading-intensive

course they had completed the semester immediately prior to meeting with the researcher.

The six probationary students reported on target courses in six different disciplines

with no overlap. The six courses and the grades that students earned, as verified by The

University computer-based record system, were chemistry (F), history (B), legal studies

(D), marketing (C), English literature (D), and geography (B).

As explained in detail for the freshmen participants, in order to examine students �

transfer and modification of strategies in detail, the information requested on the TLLS

(Randall, 2000b) has been organized by the three instructional components of annotating,

note-taking, and rehearsal st rategies. Students were asked about the format of their

151

strategy, how they paced themselves as they created the strategy, and how they used the

strategy when they studied for tests. Multiple responses were often appropriate so the

number of total responses does not always equal six. Student responses for each

instructional component are discussed separately.

Transfer of Annotation Strategy

Four of the six probationary students reported annotating in their target course. The

two students who did not annotate reported that there were too many details to memorize

and one of them added that annotating was an � unnatural � task. In terms of format, of the

four who did annotate, three of them did so in the text margins and one used sticky notes.

In terms of pacing, three students read and annotated once or twice a week while one did

so almost every day. In reference to usage of annotations for studying, students employed a

variety of activities. Three students read the annotations over, three tested themselves on

possible test questions, and two talked the material through to themselves. Only one

student reported that he changed his method as the semester progressed. He did not use

any annotations until after he earned his first failing test grade; at that point he began to

annotate in the margins and with sticky notes.

Transfer of Note-Taking Strategies

All of the probationary students reported taking lecture notes. Four of them

reported using no specific format. In other words, they did not try a method taught in

Learning to Learn. Two students used Learning to Learn methods, either predicting test

questions or writing summaries of the notes. In terms of pacing, four of the six students

reported working with their notes in some way once a week. One student worked with his

152

notes after every lecture and another student waited until the night before the test to work

with his notes. In reference to using lecture notes while studying for tests, students

reported using their notes in a variety of ways. A majority of students, five of the six, read

over their notes before the test. Four students underlined or highlighted key information,

three students combined information from the text with the lecture notes, two students

rewrote their notes to edit and revise them, and two students tested themselves using

predicted questions. One student outlined his notes and one student skimmed the text to

look for information related to his lecture notes. In terms of strategy modifications, two

students changed their note-taking procedure after their first test. One student began to

outline his notes and increased the amount of detail he recorded. The other student reduced

the amount of information in his notes, trying to limit his notes to what he predicted the

instructor thought was important.

Transfer of Rehearsal Strategies

All six probationary students reported using rehearsal strategies learned in Learning

to Learn. In terms of format, the most frequently used strategies and their frequencies

were as follows: (a) making concept cards, four students; (b) predict ing and answering

short answer questions, three students; (c) participating in study groups, two students; and

(d) completing talk throughs, two students. Studying old tests, making concept maps, using

PORPE, constructing time lines, using practice tests, solving problems, and making a study

schedule were each used by one student. In terms of pacing, five out of six students

reported creating rehearsal strategies the week before the test rather than on a continual

basis. In reference to usage of strategies for studying, five of the six students read the

153

material over to themselves and then tested themselves on the content. Two students talked

the information aloud to themselves, and one used the strategies to study with a classmate.

Two students reported modifying their rehearsal strategies in response to their test format

as the semester progressed. One began to work more sample problems and another began

to � get interactive � and look deeper in an attempt to understand the content better.

In summary, this part of the study addressed the four goals of the study in relation

to probationary students. First, the academic performance of these students was examined.

In general, probationary students made GPA gains after the completion of Learning to

Learn. Second, the probationary students scored a mean composite score of 280 for self-

regulated learning behaviors out of a possible 400. Third, there seemed to be some relation

between academic performance and self-regulated learning behaviors for the least and most

successful of the six students. Fourth, students generally found the six instructional

components to be very useful or somewhat useful. Finally, students � transfer and

modification of Learning to Learn strategies in a target reading-intensive course was

examined. The results indicated that probationary students often annotated in the margins

and used rehearsals strategies as taught in Learning to Learn. However, they did not

typically use a note-taking strategy taught in the course.

Summary of Chapter Four

Chapter 4 presented the results of the data analyses for the research questions that

guided this study. Next, Chapter 5 concludes the dissertation with a summary of the

purposes and procedures and a discussion of the findings, the conclusions, and the

implications of the study.

155

CHAPTER 5

DISCUSSION, CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS

Chapter 5 first presents a summary of the purposes and procedures for this study.

It then offers for consideration a discussion of findings in relation to each research

question. Finally, the conclusions, implications for educators, and recommendations for

future research are presented.

Summary of the Study

This study had four major goals. The first goal was to investigate differences in the

academic performance of students who had completed Learning to Learn with matched

controls who had never enrolled in the course. The second goal was to examine the

differences in self-regulated learning behaviors between these same two groups of

students. The third goal was to examine the perceptions held about Learning to Learn by

students who had completed the course. Finally, the fourth goal was to investigate how

and to what extent Learning to Learn students transferred strategies learned in the class to

subsequent college courses.

The following questions guided this study: (a) Is there a difference between the

academic performance of regularly admitted first-semester freshmen who completed

Learning to Learn during fall semesters 1998 and 1999 and the academic performance of

regularly admitted first-semester freshmen who did not elect to take the course? (b) Did

the academic performance of probationary students change after completion of Learning

to Learn during fall semesters 1998 and 1999? (c) Is there a difference between the

reported self-regulatory practices of regularly admitted first-semester freshmen who

156

completed Learning to Learn during fall semesters 1998 and 1999 and the reported self-

regulatory practices of regularly admitted first-semester freshmen who did not elect to

take the course? (d) What are the reported self-regulatory practices of probationary

students who completed Learning to Learn during fall semesters 1998 and 1999? (e) Is

there a relation between students � reported self-regulatory practices and their academic

performance? (f) Which components of the Learning to Learn curriculum do students

report helped them successfully meet the literacy demands of their subsequent courses and

regulate their own learning processes? (g) What suggestions do students have for

additions or omissions to the Learning to Learn curriculum? And (h) Do students transfer

the literacy strategies taught in Learning to Learn to the active reading, note-taking, and

rehearsal/test preparation required in subsequent courses that have a heavy reading load?

Two populations of students were studied: (a) regularly admitted first-semester

freshmen and (b) students on academic probation. First, a total of 64 first-semester

freshmen completed Learning to Learn, 28 students in 1998 and 36 students in 1999. A

matched control was randomly selected for each of these 64 Learning to Learn students.

Second, a total of 68 students who were on academic probation during either fall 1998 or

1999 completed Learning to Learn. Of these 68 students, only 25 were still enrolled at

UGA at the time of this study, and only eight could be located, even after numerous phone

calls, emails, and letters. Six of these probationary students agreed to participate in the

study. Due to the small sample size, no matched controls were used for these probationary

participants.

Data were collected in two phases. In Phase One, data were collected on both

college admission data and on several indicators of academic performance for all 134

participants. In Phase Two, all participants who could be contacted were requested to

157

meet with the researcher. Out of the 134 participants in Phase One, 30 Learning to Learn

freshmen, 30 control freshmen, and the six probationary Learning to Learn students

agreed to participate in Phase Two. In this second phase, all 66 students completed the

Self-Regulated Learning Inventory (Gordon et al., 1996). In addition, the 36 Learning to

Learn students completed two surveys, Student � s Perceptions of Learning to Learn

(Randall, 2000a) and Transfer of Learning to Learn Strategies (Randall, 2000b). The data

analyses included descriptive statistics, t-tests, correlations, and ANCOVAs with three

different covariates (i.e., AHSGPA, earned credit hours, and motivation).

Discussion of the Findings

The discussion of the findings of this study is presented in two sections, first for

the first-semester freshmen and second for the students on probation. Within each section,

the discussion is organized by the research questions.

Freshmen

Research Question 1:

Is there a difference between the academic performance of regularly admitted first-

semester freshmen who completed Learning to Learn during fall semesters 1998 and 1999

and the academic performance of regularly admitted first-semester freshmen who did not

elect to take the course?

I hypothesized that Learning to Learn students would have an advantage over the

controls on all of the academic indicators for this question because, theoretically, they

would have become more strategic learners over the course of the first fall semester

(Pintrich & DeGroot, 1990; Pressley, 2000; Zimmerman, 1998b). For Research Question

1, the discussion is integrated for all indicators because an important goal of this study was

to examine a pattern of academic performance across several indicators. This discussion is

158

organized as follows. First, a brief summary of the findings for each indicator is offered.

Second, a summary of the patterns observed is presented. Finally, an integrated discussion

is offered for considerat ion.

Indicator 1a. Semester grade point average (GPA) for the fall semester of the

freshman year (1998 or 1999). For 1998, when controlled for motivation and AHSGPA,

there were no statistically significant differences between the Learning to Learn students

and the controls in terms of fall GPA. However, there was a statistically significant

difference favoring the controls when the number of fall earned credit hours was used as

the covariate. For students who earned 15 hours or more, the controls had statist ically

higher fall GPAs. However, for the students who earned 14 hours or less for the fall

semester, there was no significant difference between the 1998 Learning to Learn and

control students. For 1999, no statistically significant differences were found between the

Learning to Learn students and the controls in terms of fall GPA with any of the three

covariates.

Indicator 1b. GPA for the subsequent spring semester (1999 and 2000). For 1998,

when controlled for motivation, there was no statistically significant difference between

the Learning to Learn students and the controls in terms of spring GPA. However, when

controlled for AHSGPA and spring credit hours, there were statistically significant

differences in spring GPA that favored the controls. For students who earned an AHSGPA

of 3.57 or higher and for students who earned 14 hours or more of spring credit hours, the

controls had significantly higher spring GPAs. However, for students who earned

AHSGPAs lower than 3.57 and for students who earned 13 or fewer hours of spring

credits, there were no statistically significant differences between the two 1998 groups.

159

For 1999, no statistically significant differences were found between the groups with any

of the three covariates.

Indicator 1c. Freshman grade point average (FGPA) after 30 earned credit

hours. For 1998, when controlled for motivation or freshmen credit hours, there were no

statistically significant differences between the Learning to Learn and the control students

for freshman GPA. The only statist ically significant difference between the two groups in

terms of freshman GPA was found when AHSGPA was the covariate. For students who

earned AHSGPAs of 3.64 or higher, the controls had significantly higher FGPAs than the

Learning to Learn students did. Conversely, there was no statistically significant

difference between the two groups of 1998 students for the students with AHSGPAs

lower that 3.64. For 1999, results indicated that there were no statistically significant

differences between the controls and the Learning to Learn students for freshman GPA

when adjusted for motivation, AHSGPA, or freshman credit hours.

Indicator 1d. The difference between predicted freshman grade point average

(PFGPA) and actual FGPA. For 1998, there was not a statistically significant difference

between the difference scores of the two groups when adjusted for motivation. However,

there was a statistically significant difference in favor of the control students when the

difference score was controlled for AHSGPA. For students with AHSGPAs of 3.77 or

higher, the controls had greater positive difference scores and that difference was

statistically significant. However, for 1998 students with AHSGPAs lower than 3.77, there

was no statistically significant difference between the Learning to Learn and control

students in terms of their difference scores. For 1999, no statistically significant

differences between predicted and actual performance were found between the two groups

with either covariate.

160

Indicator 1e. Grades in reading-intensive courses taken during the spring,

summer, or fall semester af ter the fall of the freshman year. These comparisons were

adjusted for prior academic performance by using AHSGPA as a covariate. No statistically

significant differences were found between the controls and the Learning to Learn

students for grades in any course for either 1998 or 1999.

Indicator 1f. The number of course withdrawals subsequent to the first fall

semester. This analysis was accomplished with t-tests. For 1998, the results indicated that

Learning to Learn students withdrew from more classes than control students did and that

difference was statistically significant. On average, the Learning to Learn students

withdrew from one and one-half more classes than the control students did during their.

This is a significant difference because the average full-time course load is only four or

five classes. For 1999, there was no statistically significant difference between the two

groups in terms of course withdrawals.

In sum, two interesting patterns emerged from the findings for Research

Question 1, making it difficult to draw conclusions. First, for 1998, there were significant

interactions for five of the six indicators of academic performance; that is, the relations

between the covariate and the dependent variable were different for the two groups.

However, for 1999, there were no significant interactions with any of the covariates on

any of the indicators. Second, a closer examination of the results in terms of each of the

three covariates reveals that any differences discovered between the Learning to Learn

students and the controls depended on the covariate that was used. Therefore, discussion

for Research Question 1 is organized in terms of covariates. First , a discussion of the

findings in terms of motivation as a covariate is presented. Second, a discussion of earned

161

credit hours as a covariate is offered. Third, there is a discussion of AHSGPA as a

covariate. Finally, the issue of transfer is discussed in light of the overall findings.

Discussion of Motivation as a Covariate

For both 1998 and 1999, motivation, as measured by the SRLI (Gordon et al.,

1996), was used as a covariate for the first four indicators of academic performance (i.e.,

fall GPA, spring GPA, FGPA, and the difference score between FGPA and PFGPA). The

motivation sub-scale of the SRLI was selected as a covariate because it has a moderate,

statistically significant correlation (r=.46, p<.001) with undergraduate GPA. Motivation as

a covariate allowed the researcher to control for the fact that about half of the Learning to

Learn students took the course because they were pressured by a parent or advisor or

wanted an easy A, while the other half reported that their primary motivation was to learn

new and effective ways to study as they began their college careers.

The SRLI motivation sub-scale emphasizes beliefs, such as attribution and goal

orientation. These two aspects of motivation for college students form an integral part of

the instruction for all of the academic strategies taught in Learning to Learn. Although

analysis of the academic task is a main focus as each strategy is taught, Learning to Learn

students also are encouraged to examine their goals for each of their exams and courses as

they make their decisions about strategy use. Additionally, students are asked to consider

their attributions for success or failure as they receive the results of their exams in both

Learning to Learn and in their other courses.

There were no significant differences between the Learning to Learn students and

the controls for any of the four indicators of academic performance for either 1998 or

1999 when controlled for motivation. Consequently, in light of the fact that the Learning

to Learn students were not predicted to perform as well as the controls, these findings

162

indicate that, when controlled for differences in motivation, the Learning to Learn

students performed at a higher level than students who were predicted to do better. These

findings may demonstrate that Learning to Learn was of benefit to the students who took

the course.

Discussion of Credit Hours as a Covariate

For both 1998 and 1999, earned credit hours was used as a covariate for three of

the indicators of academic performance (i.e., fall GPA, spring GPA, and FGPA) for two

reasons. First, the researcher assumed that the workload, as measured by the number of

credit hours taken in any one semester or for the freshman year as a whole, might affect

the degree of success of the students. Second, the researcher hypothesized that the

number of credit hours taken might be one indicator of students � academic self-efficacy.

The self-efficacy issue seems to be relevant to this study because the Learning to Learn

students had significantly lower HSGPAs than the controls, a characteristic often

associated with lower academic self-efficacy (Marsh, 1990; Zimmerman, et al., 1992).

Self-efficacy also has been found to be related to other factors salient to college

performance, such as motivation to self-regulate and set higher goals (Zimmerman, 1998a)

and greater use of cognitive and metacognitive strategies (Pintrich & Garcia, 1991).

Therefore, it was important to control for these key differences.

When controlled for credit hours for fall semester, spring semester, and the

freshman year, the majority of Learning to Learn students did as well as the controls,

despite their lower PFGPAs. As discussed earlier in this chapter, there was a significant

difference in favor of the controls only for the 1998 students who earned 14 or more

credit hours, but this was a minority of the students ( 25% for fall and 39% for spring).

The findings suggest that Learning to Learn may have given most students the help they

163

needed to perform at the same level as the controls, despite the differences in predicted

performance. This may have occurred because the strategies taught in Learning to Learn

provided students effective and efficient ways to manage the more difficult academic tasks

in college.

It is puzzling why the Learning to Learn students who earned the most hours for

each time period did not do better. It may be that if, indeed, they began college with lower

academic self-efficacy, as indicated by their HSGPAs, they were not able to manage a

course load greater than a typical full load. Students with low academic self-efficacy have

been found to evidence lower task persistence (Bandura, 1993) and self-monitoring of

study time (Bouffard-Bouchard, et al., 1991), two qualities that seem necessary for

successfully completing a heavier than normal course load.

Discussion of AHSGPA as a Covariate

For both 1998 and 1999, AHSGPA was used as the covariate for five indicators

(i.e., fall GPA, spring GPA, FGPA, the difference score between FGPA and PFGPA, and

grades in reading-intensive courses). AHSGPA was selected as a covariate because of its

relation to academic performance in college, and the researcher wanted to control for the

academic differences that existed before the students entered college. The findings indicate

that the majority of the Learning to Learn students performed as well as the controls.

However, it would not be correct to draw the same conclusion about the possible benefits

of Learning to Learn, as was done for motivation and credit hours, because of the relation

between AHSGPA and PFGPA. The formulas for AHSGPA and PFGPA both include

high school grades, the difficulty of the core courses taken by students, and the relative

difficulty of the high schools attended by students. Hence, when comparisons were

adjusted using AHSGPA as the covariate, the adjustment also essentially corrected for

164

differences in PFGPA. Therefore, only if Learning to Learn students � performance had

exceeded the performance of the controls, could the researcher conclude that Learning to

Learn was of benefit to the students. Although, the researcher had hypothesized that the

intervention of Learning to Learn would provide an academic edge that would enable

students to outperform the controls, this was not found to be true when the covariate was

AHSGPA.

Closer examination of the 1998 findings when AHSGPA was the covariate is

warranted. As discussed in Chapter 3, there was a lack of congruence between Learning

to Learn course goals and the personal goals for many of the students who took the

course. Further, students � goals seem to have had an effect on the benefit they received

from the course. For example, the students who reported that their primary goal for taking

the course was to earn an easy A had high average AHSGPAs (3.76) and did not perform

as well in college as the controls with the same level of AHSGPA. Their self-reported goal

for taking the course indicates that they were not motivated to internalize and personalize

the elaborative strategies that were taught in Learning to Learn. Rather, they apparently

worked through the assignments, completed the course, and then dismissed the ideas. In

contrast, the students who reported that they enrolled in Learning to Learn to learn more

effective ways of studying, had lower AHSGPAs (3.37) and performed as well as or better

than the controls with the same level of AHSGPA. Perhaps the reason that the students

with lower GPAs seemed to benefit most from Learning to Learn instruction was because

their personal goals were congruent with the goals of the course. That is, they were

intrinsically motivated to learn and implement the more elaborative and new strategies.

These findings are confirmed by the research that has found that academic goals provide

165

students with a standard for monitoring and regulating their cognition (Pintrich, 1995;

Weinstein, et al., 2000; Zimmerman, 1998a).

Although this pattern is based on only a small sample from 1998 (16 students),

support for this pattern can be found in the research literature about extrinsic motivation

and students � use of strategies. Weinstein et al. (2000) and Zimmerman (1998a) found that

students who are motivated primarily by extrinsic goals such as grades, as some of these

Learning to Learn students seem to have been, are most likely to employ surface

strategies that are relatively ineffective for most academic tasks in college. This

explanation is certainly speculative and further research is needed.

A final issue to consider in relation to AHSGPA is the quest ion of why the

Learning to Learn students seemed to have had an academic advantage when motivation

and credit hours were used as the covariates but not when AHSGPA was the covariate. It

appears that further research with a variety of indicators and multiple covariates may be

needed in order to address this question.

Discussion of the Issue of Transfer in Relation to the Findings

Finally, it is important to examine the issue of transfer in light of the findings for

both 1998 and 1999. Although the researcher had hypothesized that the Learning to

Learn students would not only perform as well as the controls, but predicted that they

would outperform the control students, this was not found to be true. A possible

explanation may be found in the literature on transfer. Even for those Learning to Learn

students who were motivated to learn new strategies, research suggests that such transfer

of st rategies is a very complex process that often takes time to accomplish successfully

(McKeough, et al., 1995; Pressley, 1995). A one-semester course may not offer adequate

time for students to make the radical changes in their assumptions about knowledge and

166

learning that research suggests is necessary for the sophisticated transfer of complex study

strategies to new contexts (Salomon & Perkins, 1989; Zimmerman, 1998b). Also, one

semester may not provide enough practice time for students to master the use of the

strategies and learn to modify them for a variety of future classes (Mentkowski, 2000;

Winne, 1995).

Transfer of strategy use also requires that students become adept at � volit ional,

metacognitively guided employment of nonautomatic processes � (Salomon & Perkins,

1989, p. 126), something that researchers find is problematic when students are facing a

new level of cognitive demand. According to Winne (1995), any new level of cognitive

demand � levies charges against a learner �s limited attentional resources (or working

memory capacity), � making it more difficult for the learner to monitor these cognitive

processes (p.177). This suggests that when freshmen come to college and must cope with

a significantly greater cognitive challenge, they may revert to the strategies they

automatized in high school because those old strategies require less cognitive and

metacognitive effort. In fact, research has found that students rarely risk adopting new

strategies in academic situations when there are high demands and significant risk, such as

the first semester of the freshman year (Garner & Alexander, 1989; Simpson & Nist,

1997). Consequently, the reason the Learning to Learn students did not outperform the

controls may be related to these problematic issues of transfer.

In sum, two of the covariates, motivation and earned credit hours, suggest that

Learning to Learn may have given a majority of students for both 1998 and 1999 an

academic edge so that they could perform at the same level as the control students, despite

lower PFGPAs. However, the findings in relation to AHSGPA as a covariate do not

support these findings. These results point out how difficult it is to accurately measure the

167

multiple variables that impact academic performance in college. Finally, the issue of the

problematic nature of transfer helps to explain why the Learning to Learn students were

able to match but not outperform the controls during their first year in college.

Research Question 3:

Is there a difference between the reported self-regulatory practices of regularly admitted

first-semester freshmen who completed Learning to Learn during fall semesters 1998 and

1999 and the reported self-regulatory practices of regularly admitted first-semester

freshmen who did not elect to take the course?

The researcher hypothesized that students who had completed Learning to Learn

would report more effective self-regulatory behaviors, as measured by the SRLI (Gordon

et al., 1996). However, no statistically significant differences were discovered between the

controls and the Learning to Learn students for either 1998 or 1999 in terms of self-

regulated learning behaviors. There are at least two possible explanation for these findings.

First, the most logical explanation may be found in the fact that the student

participants took the SRLI (Gordon, et. al., 1996) several semesters after their freshman

year. The 1998 students had completed a minimum of six semesters, and the 1999 students

had completed four semesters by the time they completed the inventory. During those

semesters, both the Learning to Learn students and the control students had been

challenged by a variety of courses with different academic demands. To some degree, they

all had learned how to cope with the difficult literacy demands of college through

experience and trial and error. They also had had a chance to find the resources that are

available to all students on the UGA campus, such as free individual tutoring, adjunct

classes which teach academic strategies, support from their instructors and teaching

assistants, and the services of the Division of Academic Assistance Learning Center. The

168

longer students remain in college, the more skilled they become at negotiating the literacy

demands. Therefore, any differences that might have existed at the end of the fall semester

of the freshman year may have been equalized by several semesters of college experience.

It may be correct to conclude that some of the academic advantage gained from a course

such as Learning to Learn can be gained in several ways on a campus as large and diverse

as UGA.

A second possible explanation may be due to the fact that the SRLI (Gordon, et al.,

1996), like other inventories of self-regulation, is an instrument that relies on self-report

data. Researchers agree that there are some problems inherent in self-report data (Garner,

1988; Pajares, 1992). The SRLI asks students to report on complex cognitive and

metacognitive processes that they have used for studying in the past. For example, a

typical item reads, � Before I begin to seriously study, I carefully examine and analyze the

amount, familiarity, and difficulty of the material I need to master in order to succeed. � A

time lag between the � thinking/doing � and the reporting of study processes, what Garner

terms the � processing-reporting distance, � may result in inaccurate and incomplete data

(p.69).

Garner (1998) suggests this problem exists for several reasons. First , it may be

difficult for students to access the cognitive and metacognitive processes that they had

activated at an earlier point in time. Students may not be aware of the automatic thinking

that they did in preparation for studying in any specific course. They may also

underestimate or overestimate the depth and quality of these cognitive and metacognitive

processes. Second, students may report what they think they should have done rather than

their true behaviors because they realize that a statement such as the example above

reflects a wise practice, and, therefore, they respond in a socially desirable way. Third,

169

verbal report results are often inconsistent over time, with results varying significantly

over subsequent replications. Therefore, it is possible that self-report instruments, such as

the SRLI used in this study, fail to access the actual complex and ever-changing processes

that students act ivate as they study.

According to Pajares (1992), a further problem with self- report data is the fact

that students are subject to � it depends � thinking (p. 327). This is especially true on a

Likert-type scale because students tend to answer to the middle and not the extremes.

They think about the contingencies of many situations and realize that different variables

would alter their responses to many questions, so they choose the middle, non-committal

response. Consequent ly, the scores on the SRLI (Gordon, et al., 1996) may not have

allowed for fine distinctions and �useful inferences about behavior � (Pajares, p.327). In

sum, the results would suggest that both explanations may have been operating in this

study.

Research Question 5:

Is there a relation between students � reported self-regulatory practices and their

academic performance?

The researcher hypothesized that there would be a correlation between students �

self-regulated learning behaviors, as measured by the SRLI (Gordon, et al., 1996), and

their academic performance, as measured by the difference between actual performance

(FGPA) and predicted performance (PFGPA).This analysis was completed for the 60

students who met with the researcher and completed the SRLI. For 1998, there was a

moderate and statistically significant correlation between the students � self-regulated

learning behaviors and their academic performance. For 1999, there was only a small

positive correlation between students � self-regulated learning behaviors and their academic

170

performance. The findings confirm prior research that asserts that self-regulated learners

perform at a higher level than students who do not self-regulate (Zimmerman, 1998b), but

the evidence is not strong. Two possible explanations for these findings are considered.

First, there may have been problems with the self-report data from the SRLI

(Gordon, et al., 1996), as suggested in the discussion for Research Question 3. If students

could not or did not accurately report their study behaviors, for whatever reasons, the

correlation results might not have revealed the true relation between self-regulated

learning behaviors and academic performance.

Second, although the SRLI (Gordon, et al., 1996) has good internal reliability, its

validity, as measured by its correlation to students � academic performance, was not strong

(r=.30). It may be that the SRLI does not tap into all of the behaviors that self-regulated

students employ to achieve academic success. The complexity of the self-regulation of

learning processes at the college level may make it very difficult to design an instrument

that effectively measures students � abilities to self-regulate.

Third, as mentioned earlier, this study uncovered significant unexplained

differences between the 1998 and 1999 students. There were probably several

confounding variables at work that were not measured by this study. It may be that one of

these undefined variables had an effect on these correlations. In sum, there may be several

reasons for the findings in terms of the relation between self-regulated strategy use and

academic performance.

Research Question 6:

Which components of the Learning to Learn curriculum do students report helped them

successfully meet the literacy demands of their subsequent courses and regulate their own

learning processes?

171

The researcher hypothesized that significant numbers of the 30 freshmen who

completed the SPLL (Randall, 2000a) during Phase Two would report that the three

cognitive/metacognitive components of annotations, note-taking, and rehearsal strategies

had been very useful to them in their subsequent courses but that fewer students would

say that the personal management components of time management, motivation, and

beliefs had been very useful. Overall, this was found to be true. In general, these findings

might be anticipated by the fact that Learning to Learn is promoted as a class that focuses

on enhancing academic strategies with emphasis on � note taking skill, critical reading, and

test preparation strategies � (Office of Undergraduate Admissions, 2000-2001, p.589).

Therefore, the affective personal management components may be perceived as less

central to the Learning to Learn curriculum by both instructors and students. Additionally,

the three personal management components are addressed in more depth in another

Academic Assistance course at UGA.

Further discussion of Research Question 6 is organized as follows. First, an overall

discussion of students � general comments about the three cognitive/metacognitive

components is offered. Second, each cognitive/metacognitive component is addressed

individually. Third, the findings for the three personal management components are

discussed. Finally, students � suggestions about how to expand existing components are

discussed.

Overall Discussion of the Cognitive/Metacognitive Strategies

An overall analysis of the findings for the three cognitive/metacognitive

components of annotating, note-taking, and rehearsal strategies reveals some positive

results. A large majority of students found that the strategies were very useful and

somewhat useful to them in their subsequent courses. Very few thought they were not at

172

all useful. In general, students � comments indicated that they found that these three

components helped them isolate important information to be learned, enhanced their

comprehension of the material, increased their learning through writing, and helped them

prepare for tests. A possible explanation for these findings may be found in the fact that, in

Learning to Learn, students are required to apply the strategies to other courses they are

taking at the time. According to Hadwin and Winne (1990), practicing a strategy in a

course for which students will earn grades is the best context for learning and assimilating

a new strategy.

Overall, the major disadvantage, cited by only a few students, was that the

strategies required too much time to complete. This response has been confirmed in recent

studies that indicate that many students use the easiest and quickest strategies they know,

even for difficult tests (Wood, Motz, & Willoughby, et al., 1998). Students may do this

because of immature epistemological beliefs (Schommer, 1993). If students who take an

elective academic assistance course believe that learning is quick and effortless, they may

not have the task persistence to spend the time required for the more elaborative

strategies.

Individual Cognitive Components

Discussion of ratings for annotations. Annotations were rated as very useful by

40% of the students surveyed and as somewhat useful by another 47%. It is not surprising

that so many students rated this strategy highly because the research shows that

annotations help students concentrate while they read (Simpson & Nist, 1997b). They also

provide a condensed and organized record of information for future studying. However,

the largest percentage of students rated annotations as only somewhat useful.

173

There appear to be three possible related explanations for why more students did

not rate annotations as very useful. First, low HSGPAs have been associated with

immature epistemological beliefs (Schommer, 1993). Students with a belief that learning is

quick do not adequately monitor their comprehension and, therefore, tend to be over-

confident in their level of text comprehension after a quick reading of the text.

Consequently, the Learning to Learn students, whose HSGPAs were significantly lower

than the controls, may not have seen the need for time-consuming annotations. Second,

most students rarely had to read their texts in high school (Murden & Gillespie, 1997;

Randall, 1999), and some of these Learning to Learn freshmen may have transferred this

belief that they do not need to read with them to college. Finally, it is impossible to

carefully annotate extensive readings the day or two before an exam, so if the Learning to

Learn students believed in quick learning and did not carefully pace their reading between

exams, they could not annotate effectively. Therefore, even if they tried annotations the

night before their test, students may have discarded annotations as a useful strategy

because they did not help them. It appears that if freshmen bring immature epistemological

beliefs related to the use of texts with them to college, they may not understand the

advantage of carefully paced text annotations as taught in Learning to Learn.

Discussion of ratings for note-taking. Note-taking using one of the methods taught

in Learning to Learn was perceived as very useful by 40% of the students surveyed and as

somewhat useful by another 40%. Therefore, it appears that many students may have

begun to realize that taking effective lecture notes in college is a more complex task than it

was in high school. There are many variables that make note-taking an anxiety-laden

activity for students, including the speed of the instructor �s presentation, the kind of

organization provided in the lecture, and the level of clarity of the instructor � s signals

174

about which information is most important (Van Meter, Yokoi, & Pressley, 1994).

Seventeen percent of the students reported that they had already developed good note-

taking skills before they began Learning to Learn. Comments from these students indicate

that they found it to be too confusing to change how they had always taken notes,

suggesting that they may not have understood that the academic task in terms of note-

taking was different in college. Consequently, some of these students may have continued

to use their comfortable high school practices rather than risk making a change at this new

level of cognitive challenge (Garner & Alexander, 1989; Simpson & Nist, 1997).

A possible reason that some Learning to Learn students rated the note-taking

component as only somewhat useful may be related to their interpretation of the question

on the survey. They may have understood the question to be asking only about note-

taking formats, rather than the study activities such as predicting questions, summarizing,

and charting of retrieval cues that form the heart of the note-taking instruction in Learning

to Learn.

Discussion of ratings for rehearsal /test preparation strategies. Rehearsal/test

preparation strategies were rated as very useful by 50% of the students surveyed and as

somewhat useful by another 33%. These percentages may have occurred because a large

variety of rehearsal strategies are taught in Learning to Learn, so there are likely to be

strategies that will be appropriate for different learning styles, different content areas, and

a variety of assessment tasks. Students � comments seem to reflect what has been found in

the research; that is, most students were not exposed to a repertoire of such test

preparation strategies in high school (Wood, et al., 1998). It is possible that students

realized that, without the typical teacher compensations as were provided in high school

175

(Thomas et al., 1987), they needed techniques that would help them organize information

for rehearsal and final test preparation.

Discussion of Three Personal Management Components

The three components of time management, motivation, and beliefs about

knowledge and learning were rated as very useful by only nine (30%), four (13%), and

five (17%) of the students respect ively. These percentages are disappointing because the

existing literature reports that these components form the foundation for effective strategy

change (Bouffard-Bouchard, et al.,1991; Schommer, 1990; Zimmerman, 1998a).

However, by the very nature of the course, as discussed earlier, the major emphasis is on

the three cognitive/metacognitive components. Most of Learning to Learn class time is

devoted to assignments and projects that focus on the academic strategies. Although the

personal management components are continuously discussed as the foundations of

effective strategy use, a minimum of class time is devoted to their explicit instruction.

Therefore, it is logical that students may have perceived that the cognitive/metacognitive

components were more useful. Another possible explanation for these low percentages

may be found in the fact that a significant number of students believed that they had

already developed their skills in these areas before they enrolled in Learning to Learn.

Discussion of Students � Recommendations

For the final part of Research Question 6, students were asked to further evaluate

the course by explaining what instructional components of Learning to Learn should

receive more attention and what areas other than the six major components were useful to

them. Many students suggested that applying the Learning to Learn strategies to one of

their other courses was very helpful and should be done more frequently. This finding is

not surprising considering that the research on transfer suggests that the transfer of

176

strategies to a new learning environment is a difficult and complex process (Salomon &

Perkins, 1989; Winne, 1995; Zimmerman, 2000). Students � responses may have indicated

that they had begun to realize the difficulty of effectively transferring strategies to a variety

of courses without significant guided practice in how to modify strategies to fit each new

academic task. Students also may have hoped that Learning to Learn would be more of a

tutorial format in which they could receive specific content and strategy help for their

current courses.

The reading rate project was an instructional area that was not assessed along with

the six major components, but it was the area that students mentioned most frequently as

an area that was especially helpful to them. The reading rate project provides students

with visible evidence of their progress as they work to improve their speed and

comprehension. The most likely explanation for this finding is that, despite the fact that

efficient reading of text was not a skill students had to master in high school (Thomas et

al., 1991), by the end of their freshman year at UGA, many students realized it is a

necessary skill because, at the college level, they must read extensively in many of their

courses.

In sum, students appear to have perceived that the academic strategies were the

most valuable of the instructional components of Learning to Learn. This may be because

these strategies were the primary focus of Learning to Learn instruction and provided

students with strategies to help them manage the increased academic challenge of college.

Additionally, students reported that the application of strategies to other courses and the

reading rate project were especially helpful to them.

177

Research Question 7:

What suggestions do students have for additions or omissions to the Learning to Learn

curriculum?

To understand the students � open-ended responses to this question, it is important

to understand the organization of Learning to Learn. The course centers around three

simulated units, usually one in a social science, one in history, and one in a pure science.

These units are taught so that there are common content areas for practicing a wide

variety of strategies. The students are tested on the material in order to encourage them to

make a legitimate attempt to try out the new strategies and also to assess their

effectiveness in using the new strategies. After learning and practicing each strategy,

students are required to use them in one of their other classes.

In terms of suggested omissions, several students reported that these simulated

course units were not useful and should be omitted. The students apparently did not

understand the rationale of a common content and only saw the added burden of another

test. Seemingly, they did not realize that the three simulated units were chosen because

they provided rich contexts for learning a wide array of strategies that would be useful in

the majority of their future classes. Rather, students expressed a desire for immediate help

in one of the courses they were taking during the semester of Learning to Learn (e.g.,

English, foreign languages, business, and math) rather than spending time on a simulated

course unit.

These findings seem to indicate that students had begun the process of

understanding the specificity of strategy application. The students were correct that the

pract ice opportunities provided in Learning to Learn could not apply to all learning

contexts without significant modifications. Thus, their need for immediate help in a

178

specific course seems to have overshadowed the value of the simulated units as specific

learning contexts for future application.

Research Question 8:

Do students transfer the literacy strategies taught in Learning to Learn to the active

reading, note-taking, and rehearsal/test preparation required in subsequent courses that

have a heavy reading load?

The purpose of this question was to determine to what degree students

independently applied the strategies they learned in Learning to Learn because such

transfer is the ult imate goal of the course. The researcher was unable to find any studies in

the current literature that directly examined college students � transfer of strategies to

subsequent courses. However, the literature does offer theories on the transfer of learning

and on self-regulation that provide a solid framework for evaluating the transfer that was

observed in this study.

Summary of the Findings

The researcher hypothesized that, by the year following completion of Learning to

Learn, there would be evidence of significant strategy transfer to the difficult courses that

students chose to target for their responses on the TLLS (Randall, 2000b). However, in

terms of annotations, this study found that annotations were employed by only 47% of the

students surveyed. In terms of format, pacing, and usage, most students who annotated

implemented the annotation process as recommended in Learning to Learn. In terms of

taking lecture notes, 83% of the students surveyed reported that they took notes, although

a majority of students reported using no specific format. In other words, they did not use

a format taught in Learning to Learn. However, students reported that, as they studied

their lecture notes, they employed the strategies and processes taught in the course. In

179

terms of rehearsal st rategies, 90% of the students surveyed used strategies taught in

Learning to Learn; in fact, students reported using an average of more than three different

strategies. Students � implementation of the strategies seems to reflect the instructional

emphasis of the course.

Interpretation of the Findings

The following discussion focuses on several possible interpretations of the findings

for the three cognitive/metacognitive components of Learning to Learn (i.e., annotations,

note-taking, and rehearsal/test preparation strategies). The discussion of these findings is

organized in the following way. First, possible interpretations are offered for the findings

in regard to students who used the strategies as taught in Learning to Learn. Second,

interpretations are provided for the results related to students who apparently made

strategic decisions not to use the strategies. Finally, there is a discussion of the findings in

regard to the students who seem to have made non-strategic decisions not to employ the

strategies. Throughout this discussion, the researcher employs the criterion of a course

grade of a B or higher to indicate academic success.

Students who employed the strategies. A majority of students reported that they

used st rategies taught in Learning to Learn. However, the findings indicate that , at the

end of their sophomore and junior years, there remained a wide variation in students �

abilities to effectively use strategies. The analyses that follow should be viewed in light of

the 2.86 overall average earned by all 30 of the Learning to Learn students in their

targeted courses, as reported in the TLLS (Randall, 2000b).

Of all of the students who chose to annotate (14 out of 30), those who annotated

on a daily or weekly basis earned an average of 3.3 in their targeted courses. Their grades

suggest that these students had learned how to annotate effectively, that they understood

180

the importance of the pacing of their reading and annotating, and that they used their

annotations as effective study aids. However, those students who annotated just one or

two days before their tests averaged only a 2.5 in their targeted courses. Interestingly,

these students continued to follow this relatively ineffective practice over the course of the

entire semester. These findings suggest that many students are not yet self-regulated

learners who effectively evaluate their progress and take corrective action to adjust their

study strategies (Philips, 1992; Salomon & Perkins, 1989). Alternatively, it may be

possible that these students were satisfied with their Cs and were not motivated to devote

any more time and attention to annotations.

In terms of students who took lecture notes, it appears that a majority of them had

assimilated the rationale presented in Learning to Learn. Even though they did not use a

specific format that was taught in Learning to Learn, the most frequently cited usage of

notes for review, used by 84% of the students, was to combine them with text

information, a strategy that encourages students to integrate and organize information on

major topics. No clear relation was found between any particular review strategy for

lecture notes (e.g., summarizing, predicting questions, or integrating text and lecture) and

students � grades in their targeted courses. However, the pacing used by students as they

reviewed, edited, and studied their notes had an major impact on their grades. A large

majority of students (60%) worked with their notes on a daily or weekly basis and earned

an average of 3.05 in their targeted courses. This practice seems to indicate that they

understood the necessity of regular review. However, the students who waited unt il the

night before the test to study their notes averaged a 2.4 in their targeted courses.

The findings in terms of rehearsal strategies for test preparation indicate that many

students continue to use strategies that have universal application to all disciplines, such as

181

answering predicted test questions (69%) or constructing concept cards (67%). It is

possible that students may still be using the easiest route to solve their study problems by

choosing the most comfortable and familiar strategies (Pintrich & DeGroot, 1995).

Although researchers agree that the format of the strategy is not as important as the

cognitive processing that occurs (Nist & Simpson, 2000), several strategies encourage a

deeper level of processing than these two generic strategies do. Both concept maps and

charts help students uncover the relation between concepts, but each of these was used by

only 15% of the students in their targeted courses. Interestingly, these few students who

used either charts and/or concept maps, averaged 3.17 in their targeted courses. No

students used a time line, an effective strategy for integrating historical information from

multiple sources. It appears that students may still be developing the skill to analyze

specific academic tasks, define the specific attributes of those tasks (Salomon & Perkins,

1989), and choose the most effective strategy. In terms of pacing of rehearsal strategies,

students who waited until the night before their exam to develop their strategies earned

only a 2.6 in their targeted courses. This may be evidence that some of these Learning to

Learn students still held the immature belief that learning is quick and effortless

(Schommer, 1993). In contrast, those students who constructed strategies on a daily or

weekly basis earned an average of 3.09 in their targeted courses.

Students who made strategic decisions not to employ the strategies. Some students

apparently made strategic decisions not to annotate or take lecture notes. In terms of

annotations, of the 16 students who chose not to annotate, the 10 whose reasons seemed

to show careful analysis of the academic task for their targeted course, all earned As and

Bs. For example, seven students reported that their instructors � lectures covered the text

in an organized and thorough way, and reading and annotating the text was not necessary.

182

The pattern was the same for note-taking. Of the five students who decided not to take

notes, the three students who carefully explained their rat ionale for not taking notes in

relation to the academic task made As and Bs in their targeted courses.

Students who made non-strategic decision not to employ the strategies. Many of

the students who decided not to employ the strategies taught in Learning to Learn may

not have analyzed their academic tasks carefully. In terms of annotations, of the 16

students who chose not to annotate, the 6 whose explanation was unrelated to an analysis

of their academic task all earned Cs in their targeted courses. For example, one student

reported that she had a photographic memory, and another student reported that she

preferred highlighting. Neither of these justifications took into considerat ion that the

academic tasks probably required more than memorization or familiarity with the material,

suggesting that these two students may have held immature epistemological beliefs.

In terms of note-taking, of the five students who decided not to take notes, two

students apparently did not analyze the academic task carefully and earned Cs in their

targeted courses. For example, the student who purchased commercial notes did not seem

to understand the encoding function of note-taking; that is, � taking notes facilitates ...

learning by affecting the nature of cognitive processing at the time the lecture is delivered

and the notes taken � (Armbruster, 2000, p.177). Also, the student who decided that just

listening was a better strategy apparently did not seem to understand the second function

of note-taking; that is, notes provide an external � repository of information � to use in later

review and test preparation (Armbruster, p.177).

In terms of rehearsal strategies, the results were similar. Only three students chose

not to create any rehearsal strategies and they all earned C s in their targeted courses. One

student explained that rehearsal strategies were not necessary because his exams were

183

essays that required analysis of theories. It is possible that this student did not understand

that such analysis requires a solid foundation in the basic knowledge of the theories. Two

other students decided that reading and studying their notes was sufficient. They may not

have understood the value of developing strategies that would integrate material from

multiple sources, demonstrate relation between concepts, and sort and reduce the

information to be learned.

In sum for Research Question 8, the effectiveness of students � transfer of strategies

taught in Learning to Learn appears to confirm what transfer theory (Salomon & Perkins,

1989) and Schommer �s work in epistemology (1990, 1993) both suggest. First, students

who appeared to have understood the importance of carefully analyzing each new

academic task experienced the most effective transfer of strategies to future courses, as

demonstrated by their higher grades. According to transfer theory, this kind of task

analysis requires students to look for the attributes of each new task and analyze

alternative strategies before they make a decision about how best to study. Conversely,

students made lower grades if they did not effectively reflect upon and evaluate their

academic performance and make strategic adjustments as the semester progressed.

Transfer theory would suggest that these students did not question their automat ic

assumptions about learning and try more elaborative strategies if their first study attempts

were not successful (Brell, 1990; Zimmerman, 1998b). Second, in terms of pacing, the

students who paced themselves during the weeks before their tests had better academic

performance than students who waited until the night before the test to create annotations,

study lecture notes, and construct rehearsal strategies. Apparently these higher performing

students shared the mature beliefs that learning takes time and effort (Schommer, 1990).

184

A brief summation of all of the findings related to freshmen may help to integrate

all of the research questions for the freshman population in this study. First, the findings in

terms of academic performance are inconclusive. However, it appears that, for a variety of

reasons, the majority of the Learning to Learn students performed as well as the control

students during their freshman year, despite their lower PFGPAs. Second, on the whole,

students � perceptions were that Learning to Learn had been beneficial to them, especially

the cognitive/metacognitive components. Third, it seems that by the time of the study,

which was anywhere from one to four semesters after the end of their freshman year, many

Learning to Learn students were beginning to analyze their academic tasks and

appropriately transfer Learning to Learn strategies to their other courses.

Probationary Students

Only six probationary students agreed to participate in this study, as previously

explained. Due to this small number of students, the discussion of the findings must be

descriptive rather than inferential. First, a brief summary of the findings is presented for

each question. Second, the interpretation highlights two of the probationary students in

brief case studies in order to look at the multiple variables that apparently influenced the

academic performance of the probationary students. These two students are the two who

seemed to make the least and the most academic progress after completion of Learning to

Learn. Finally, a brief examination of the other four students is provided. For the

discussion of probationary students, a 2.0 semester average is used as a criterion for

success because that is the critical point for determining probationary status.

185

Brief Summary of Findings

Research Question 2:

Did the academic performance of probationary students change after completion of

Learning to Learn during fall semesters 1998 and 1999?

Academic performance was assessed in four ways: (a) semester GPAs for fall

semester of Learning to Learn enrollment, (b) semester GPAs for each semester after

completion of Learning to Learn through fall 2000, (c) any change in academic status

following Learning to Learn, and (d) grades earned in reading-intensive courses that were

taken during or after the semester of Learning to Learn enrollment through fall 2000.

First, in terms of semester GPA following completion of Learning to Learn, four of the

six students made consistent academic gains. The semester averages for these four

students ranged from 2.00 to 3.67. The academic performance of the other two students

was inconsistent, often dropping below the 2.0 level. Second, in terms of academic

standing, two students cleared academic probation the semester immediately following

Learning to Learn and two others cleared within the year. Two of the students were still

on probation at the time of the study. Third, in terms of grades in reading-intensive

courses, averages for the different disciplines ranged from 1.0 (D) in psychology to 3.0

(B) in geography for all six students. In sum, four of the six students made significant

academic improvement after completion of Learning to Learn.

Research Question 4:

What are the reported self-regulatory practices of probationary students who completed

Learning to Learn during fall semesters 1998 and 1999?

The SRLI (Gordon et al., 1996) was used to derive a measure of self-regulated

learning. On a range of 20 to 100 on each sub-scale, the four sub-scale averages ranged

186

from 68 to 74, indicating that students reported that the self-regulatory behaviors were

somewhat typical or frequently typical of them.

Research Question 5:

Is there a relation between students � reported self-regulatory practices and their

academic performance?

Due to the small number of participants and the inconsistent data from student to

student, it was not possible to compute a correlation. However, an examination of the

SRLI (Gordon, et al., 1996) composite scores in relation to academic performance

suggested there was some type of pattern. That is, the student with the highest SRLI

composite score maintained semester grades of 2.42 to 3.36 for the semesters following

Learning to Learn. Conversely, the student with the lowest SRLI composite score

consistently had the lowest semester grades of any of the six students, ranging from 1.00

to 3.00.

Research Question 6:

Which components of the Learning to Learn curriculum do students report helped them

successfully meet the literacy demands of their subsequent courses and regulate their own

learning processes?

Probationary students found instruction in note-taking instruction and rehearsal

strategies to be the most useful cognitive/metacognitive components and instruction in

annotations as somewhat less useful. No student rated any of the cognitive components as

not at all useful. In terms of the three personal management components, somewhat useful

was the most frequent rating for instruction in time management, mot ivation, and beliefs

about learning.

187

Research Question 7:

What suggestions do students have for additions or omissions to the Learning to Learn

curriculum?

The probationary students had few suggestions. They recommended adding

instructional components in stress management, library research, and st rategies for taking

multiple choice tests. They made no suggestions for any Learning to Learn component to

omit.

Research Question 8:

Do students transfer the literacy strategies taught in Learning to Learn to the active

reading, note-taking, and rehearsal/test preparation required in subsequent courses that

have a heavy reading load?

A majority of the probationary students reported using the three major academic

strategies in their targeted courses. Four of the probationary students reported annotating.

They reported completing annotations on a regular basis, none of them waiting unt il the

night or two before the test. In general, they studied the annotations by testing themselves

and talking about the material aloud. All of the students reported taking lecture notes.

Most of the students worked with their notes on a weekly basis and studied them using a

variety of effective strategies that were taught in Learning to Learn. The probationary

students reported using an average of 3.5 of the rehearsal strategies suggested in Learning

to Learn; concept cards and answering predicted questions were the most frequent ly

reported.

Interpretation of Findings

Interpretat ion for highest and lowest performing students. It appears that the

probationary students � epistemological maturity (Schommer, 1993) and their ability to

188

analyze their academic tasks (Simpson & Nist , 1997) determined their relative academic

success. These two possible interpretations are best explained by examining the academic

performance and reports of two individual students.

Overall, Student #1 made the least academic progress after completion of

Learning to Learn, and it appears that he did not become a self-regulated learner. He

cleared probation the semester immediately following Learning to Learn by earning a 3.00

semester GPA. However, during the subsequent semesters, his semester GPAs averaged

only 1.5, well below the critical 2.0 level. This student had the lowest composite score on

the SRLI (Gordon, et al., 1996). He had a verbal SAT score of 530, significantly lower

than the 598 mean SAT-V of the entire freshman class for fall of 1998. Therefore, to be

competitive at UGA, it would have been necessary for him to develop some reading and

studying strategies that would help him understand the material he was studying at a

deeper level than just memorization. However, he reported that he took Learning to

Learn to boost his overall GPA, that learning new strategies was a secondary goal, and

that he found that only two of the Learning to Learn components were very useful to him.

Moreover, the comments of Student # 1 indicate that his epistemological beliefs

(Schommer, 1993) were relatively immature and that he did not correctly analyze the

academic tasks he faced (Simpson & Nist, 1997). For example, he suggested that

Learning to Learn should teach students how to do well on multiple choice tests without

them having to know the material very well. He expressed certainty that this was possible,

evidence that he believed that learning was quick and simple (Schommer, 1993) and that

he did not understand that most college level multiple choice tests require understanding

and application of concepts. He also expressed the belief that annotations were an

� unnatural process � that he would not even consider, a belief he apparently never re-

189

examined carefully. He did not annotate in his targeted legal studies class and earned a D

for the semester. Despite Learning to Learn instruction, he apparently did not question his

assumptions about learning, and he did not look for alternative solutions when he did not

meet with success. In sum, he did not have the critical thinking dispositions necessary for

transferring strategy use to new academic contexts (Philips, 1992; Salomon & Perkins,

1989).

In contrast, Student #2 made the greatest academic gains and appears to have

begun to better regulate his own learning processes. Although he was dismissed from

UGA for the semester immediately following completion of Learning to Learn, he

returned the next semester and consistently earned semester averages between 2.42 and

3.36, averages well above the critical 2.0 level. He had the highest composite score on the

SRLI (Gordon et al., 1996) of all six probationary students. However, he had the lowest

SAT-V score with a 440, a score significantly lower than Student # 1 who was discussed

in the preceding paragraphs. Student #2 reported that he took Learning to Learn primarily

to learn new study strategies and reported that three of the components of Learning to

Learn were very useful to him.

Student #2 seems to have monitored his level of success, as transfer theory

suggests self-regulated students do (Philips, 1992). In his targeted chemistry course, he

realized he was not doing well and changed both his note-taking strategy and his rehearsal

strategies after making an F on the first test. However, he did not annotate because he

believed that there was too much information to memorize, and, according to his report,

he never seriously considered annotating. Therefore, it appears that, although he was

monitoring his success, he may not have completely analyzed all the nuances of the

academic task. He apparently did not understand that significant information on the tests

190

may have come from the text. Annotations might have helped him understand the

organization of the concepts and provide a supplementary understanding of his lecture

notes. The comments of this student indicate that he was open to learn new strategies and

that he realized that different tasks required different approaches. It appears logical to

conclude that his significant overall improvement was a result of this ability to monitor his

academic performance and try new strategies. However, the combination of weak verbal

skills and a lack of careful attention to his text may have handicapped him in chemistry and

led to his F for the semester.

Interpretat ion for the other four students. The other four students had very

different profiles that demonstrate how hard it is to draw conclusions about the

effectiveness of one variable, such as Learning to Learn, for students in academic crisis.

Students #3 transferred so many credit hours to UGA that, although he began to make

semester averages above 2.50 after completion of Learning to Learn, he was never

dismissed from probation because the hours for one semester had such a small impact on

his overall GPA. Students #4 and #6 were successful in maintaining averages above 2.0

and were able to stay off of academic probation. Student #5 never cleared probation

although, although he had the highest SAT-V score of the six probationary students (670)

and two of his semester averages were well above 2.0.

In sum, for students who were not in the habit of even going to class, at a

minimum, the variety of strategies offered in Learning to Learn may have provided a

majority of these students with some ideas for bringing some structure to their study

attempts. It is difficult to make any generalizations about the effectiveness of Learning to

Learn as an aid to probationary students with such a small sample and impossible to draw

191

conclusions in regard to the reasons for the improvement that was made by four of the six

students.

Conclusions

There seem to be at least seven possible conclusions that can be drawn from the

findings of this study.

First, it appears that many of the freshmen who elect to take an academic

assistance course such as Learning to Learn may be different in two important ways from

similar freshmen who do not choose to enroll in such an elective. First, the findings of this

study suggest that students who enroll in elective academic assistance courses may enter

college with weaker records of high school academic performance, despite the fact that

they have equivalent SAT-V scores. Consequently, some of these students appear to have

lower feelings of academic self-efficacy and immature epistemological beliefs,

characteristics that are often associated with low academic performance.

Second, the complexities of academic performance make it challenging to assess

the impact of an academic assistance course on students � academic lives. Students have

many personal characteristics (e.g. , level of motivation, epistemological beliefs, academic

self-efficacy, and prior academic achievement) that have a significant influence on their

academic performance and growth. Additionally, students access help in many ways on a

campus as large and diverse as UGA. Therefore, it is difficult for researchers to conclude

what personal variables and what campus resources had a major impact on students �

academic performance. However, it does appear that elective academic assistance courses

are one avenue for students to pursue to become more strategic and successful students.

Third, multiple indicators of academic success may be necessary to accurately

draw conclusions about college students � academic performance. For example, in this

192

study, the findings in terms of academic performance were different for the 1998 than they

were for the 1999 students. The findings were also remarkably different among the 1998

students, depending on which covariate was used for the comparison. If only one indicator

of academic performance, such as FGPA, or one covariate, such as AHSGPA, had been

used, inaccurate conclusions may have been drawn. These findings suggest that this kind

of triangulation of data through the use of multiple indicators and replications provides a

more accurate picture of the findings and trends of educat ional research.

Fourth, this study confirms the tenets of current transfer theory; that is, the transfer

of sophisticated cognitive and metacognitive strategies to new academic contexts is a

long-term developmental process (Salomon & Perkins, 1989; Winne, 1995; Zimmerman,

1998a). For the participants of this study, the transfer of elaborat ive cognitive and

metacognitive strategies to other courses is apparently still in the developmental stage,

evidenced by the fact that they tended to use fairly generic study strategies rather than

more content-specific strategies. Compared to the generic strategies, content-specific

strategies require more integration of material, a clearer understanding of the relation

between ideas, and a greater cognitive effort during the construction of the strategy. It

may be that most students need several years of challenging academic tasks for them to

evolve into being self-regulated learners who can effectively transfer study strategies to

multiple contexts. Even though strategies may be taught as Mentkowski (2000) suggests

(e.g., with an emphasis on task analysis, with explicit instruction, with simulations and

modeling within a specific context, with application to students � actual course work, and

with ample instructor feedback), it appears that students do not implement the strategies in

the most effective way early in their college careers.

193

A fifth conclusion is also related to the issue of transfer; that is, it is difficult for

researchers to draw some of the most important inferences without more intensive work

with individual students. For example, it is difficult for researchers to determine the quality

and usefulness of students � strategies without examining the actual strategies in the

context of a specific course. This is true because it is not the strategy itself that is the

critical part of transfer, rather the cognitive and metacognitive processing that is employed

in the construction and implementation of that strategy. It might be important to examine

strategies for the following: (a) the thoroughness of the informat ion included, (b) the

degree of elaboration, (c) a pattern of organization that reflects the relation between

concepts, and (d) the level of integration from multiple sources. Moreover, it is important

for researchers to determine how well students have identified their academic tasks.

Researchers have difficulty accurately drawing such inferences without having a significant

amount of knowledge about the students (e.g., their level of interest in the course and their

prior knowledge of the discipline) and the course (e.g., the professor �s lecture style, the

amount and variety of required readings, and the assessment tasks). One way to address

this problem would be for researchers to use a technique such as protocol analysis (Payne,

1994) to record students � cognitive processing as they create and study the strategies.

A sixth conclusion is that researchers must be cognizant of the fact that when

students make decisions not to use part icular strategies, their decisions may be strategic

ones. In fact, research has found that the use of some learning strategies only correlates

with academic performance for the most � cognitively demanding � assessments (Sparrow,

1998, p. 141) and not for easier assessment tasks. Often questionnaires about strategy

usage only ask students to rate a strategic behavior as either yes or no (they did or did not

use the strategy), or ask students how consistently they used the strategy. With such

194

questionnaires, a numerical score does not really reveal how strategic students have been

or to what degree they have become self-regulated learners. However, a great deal can be

learned by asking students why questions as was done in this study, such as � Why did you

decide not to annotate as a strategy for this course? � In this study, the results of the why

questions on the TLLS (Randall, 2000b) indicate that some students apparently make

strategic decisions not to use certain strategies after they have carefully analyzed the

academic task. This is certainly a desired behavior that might not show up on a Likert-type

scale, a checklist, or questionnaire.

A seventh and final conclusion can be drawn in regard to probationary students.

From a limited sample, it appears that a course such as Learning to Learn can be

beneficial to students on academic probation if they make the decision that they want to

perform better academically. Students � comments in this study indicate that they were not

focused on academics when they first entered college and that they devoted very little time

to going to class or studying. However, the majority of the six probationary students

reported that the strategies that were taught in Learning to Learn helped them to manage

the work load and make better grades once they were more academically motivated.

Implications for Educators

Students � responses to the two surveys contribute a great deal of information that

may be useful to academic assistance instructors at many post-secondary institutions.

Students � comments provide substantive information in three major areas: (a) the need for

instructors to keep informed about changing academic tasks, (b) the need to provide

significant course time to application of strategies in students � courses, and (c) the need to

focus on cognitive/metacognitive processes rather than strategy format.

195

First, academic assistance instructors must make it a regular practice to

communicate with instructors in various departments on campus to be certain that the

academic assistance instruction reflects the academic tasks that the students actually

encounter. For example, the academic tasks have changed dramatically in many

disciplines, primarily because of technological innovations. These changes have been

observed by the researcher and were reported by participants in this study for both the

processes of reading texts and taking lecture notes. In terms of reading texts, some

instructors present abbreviated slides of the main ideas presented in their texts and read

them to the class with elaborations and examples. In terms of taking lecture notes, many

instructors now post an out line of their lectures on the web for students to access and

print before they come to the lecture. In this study, some students reported that these

instructor practices alleviated them of the necessity for careful reading and annotating of

their text or taking detailed notes during lecture. This assumption may be true or may be a

misunderstanding on the part of students, indicating that they do not understand the value

of the cognitive processes involved in reading texts and taking lecture notes. In fact, many

instructors on campus intend that the slides and web printouts will serve only as a basic

introduction and organization of the material. Many of these instructors assume that

students will carefully read chapters and add extensive elaborations and examples to notes

as they listen to lectures (M. L. Simpson, personal communication, November 27, 2001).

Therefore, it is important that academic assistance instructors stay informed about the

tasks students face and help them analyze their performance early in the semester to

determine if, for example, reading the text and taking extensive lecture notes is a necessity

or not. It appears that students need this kind of guidance in order to learn to monitor

196

their performance and revise their approaches to studying as needed (Phillips, 1992;

Salomon & Perkins, 1989).

A second implication for educators is that the context in which students learn new

study strategies determines whether or not they are likely to value those strategies and

transfer them to other courses. According to students � comments, those who take

academic assistance courses expect to see immediate application of new study strategies to

their current courses. Researchers agree that transfer of st rategic behaviors is most likely

to occur if the initial instruction is situated in a specific context (Mentkowski, 2000). In

an attempt to teach strategies within specific contexts, a large part of Learning to Learn

instruction at UGA typically has been focused on the simulated units in social science,

history, and biological science because of the need for a common content for teaching a

wide variety of strategies. However, from students � comments it appears that some of the

benefit of these units is lost because students feel pressure to focus on their other current

courses. According to students � reported motivation for taking the course, the value they

see in learning new strategies is dependent on making good grades in their other courses;

and they seem to want immediate results. Therefore, instructors should evaluate the

curricular balance of their courses and focus as much as possible on students � application

of strategies to their current courses and less on the simulated units. The introduction of

each new strategy could be done with a shared content, but most of the pract ice could be

assigned to be completed with the students � other courses. Organizing students into small

groups by disciplines might be product ive. For example, students taking psychology,

sociology, and anthropology might work together for several weeks. These students could

help each other analyze the academic tasks they encounter, evaluate the effectiveness of

197

particular strategies in their discipline of focus, and collaborate on how to modify the

strategies.

A third implication for educators is the importance of focusing on the underlying

cognitive and metacognitive processes of strategy usage rather than on the format of

specific strategies (Nist & Simpson, 2000). For example, this study �s examination of

students � selection of rehearsal st rategies indicates that most students used the generic

strategies such as concept cards and predicting questions rather than strategies such as

concept maps, charts, or time lines that might have been more effective strategies for

some of the most complex cognitive tasks that students faced. This may be because it is

easy for instructors to become caught up in arming students with a wide array of strategies

and, consequently, not spending enough time helping students understand the importance

of selecting a strategy that requires the same kind of cognitive processing that their

assessment task will require. Theory suggests that the ability to independently transfer

academic strategies is much more complex than understanding how to use a variety of

strategies (Salomon & Perkins, 1989). Students must be able to abstract the properties of

the study task, understand the cognitive processes required for each task, and choose the

strategies that will most likely enhance that cognitive processing. In order for students to

be able to do this, instructors must provide many opportunities for students to practice

strategies in different contexts, they must be explicit in their focus on

cognitive/metacognitive processing over format, and they must provide quality feedback

for students. This is a significant challenge for academic assistance instructors who are

working with groups of students who have a wide range of beliefs and academic abilities

and who are working in a wide range of disciplines.

198

Recommendations for Future Research

Further research into the effectiveness of academic assistance courses is needed.

This study revealed several areas that should be explored by researchers interested in the

academic performance of students who have completed a course such as Learning to

Learn.

Most importantly, the results of the current study may not have demonstrated the

potential impact that an elective academic assistance course can have on the long-term

self-regulated study behaviors of students. Future researchers might extend their study for

a longer period of time because the effective transfer of self-regulated study strategies is a

long-term process (Winne, 1995; Pressley, 2000). Researchers also might follow students

throughout their college years, obtaining baseline measures of students � self-regulated

learning behaviors and their epistemological beliefs early in their freshman year before the

students are exposed to college academic tasks. They could then periodically administer

measures and interview students to determine how their behaviors and beliefs change over

time and experience. They could also gather academic performance data throughout the

students � college careers and examine the relation between their grades and their behaviors

and beliefs.

Second, future research is needed to determine which students benefit most from

elective academic assistance courses such as Learning to Learn. The first

recommendation, explained in the previous paragraph, might be able to answer this

question, but a less ambitious research study might be to replicate parts of this current

study with sophomores who take an elective academic assistance course. Sophomores

might have a clearer understanding of the complexity of the academic tasks in college

when they begin the elective academic assistance course. Therefore, they might have more

199

mature epistemological beliefs, understanding that learning is an effortful process that

requires significant cognitive engagement. Consequently, they might realize more

immediate gains in their academic performance than freshmen do because they might

understand the value of transferring the strategies learned in Learning to Learn to their

other courses.

Third, future researchers might examine students � strategy employment rather than

relying exclusively on self-report data.. One possible approach might be a quantitative

study in which researchers collect, examine, and code strategies in terms of their

appropriateness to the academic task, the depth of understanding represented, and the

integration of ideas from multiple sources. Nist, Simpson, and Olejnik (1991) used

descriptive data, frequencies, correlations, and regression analyses in such a study of the

relation between study processes and academic performance. Alternately, a qualitative

perspective might record students � cognitive processes as they create and study strategies,

also recording their motivations for using specific strategies and their ongoing decisions

about the continuation, modification, or cessation of strategy use, such as the work of

Connell (2000).

Finally, academic assistance instruction might be enhanced if researchers could

develop more valid and reliable instruments for assessing students � self-regulated learning.

As discussed earlier in this chapter, the problems inherent in Likert-type scales, as found in

most existing instruments, make it difficult to obtain accurate measures of students � self-

regulated learning. Instruments that use other formats (e.g., interviews or scenarios) are

needed in order to assess the growth students make in the area of self-regulated learning

after the completion of an elective academic assistance course.

200

Summary of Chapter Five

This chapter presented a summary of the study, a discussion of the findings, final

conclusions, implications for educators, and recommendations for future research.

201

REFERENCES

Abrams, H. G., & Jernigan, L. P. (1984). Academic support services and the

success of high-risk college students. American Educational Research Journal, 21, 261-

274.

Algier, A. S., & Algier, K. W. (1982). Improving reading and study skills. San

Francisco: Jossey-Bass.

Alvermann, D. E., & Moore, D. W. (1991). Secondary school reading. In R. Barr,

M. L. Kamil, P. Mosenthal, & P. D. Pearson (Eds.), Handbook of reading research (Vol.

II, pp. 951-983). New York: Longman.

Anderson, W., Best, C., Black, A., Hurst, J., Miller, B., & Miller, S. (1990).

Cross curricular underlife: A collaborative report on ways with academic words. College

Composition and Communication, 41, 11-36.

Applebee, A. N., Langer, J. A., & Mullis, I. V. S. (1987). Literature and US

history: The instructional experience and factual knowledge of high school juniors.

Princeton, NJ: Educational Testing Service.

Armbruster, B. B. (2000). Taking notes from lecture. In R. F. Flippo & D.C.

Caverly (Eds.), Handbook of college reading and study strategy research (pp. 175-200).

Mahwah, NJ: Erlbaum Associates.

Bandura, A. (1993). Perceived self-efficacy in cognitive development and

functioning. Educational Psychologist, 28, 117-148.

202

Bassok, M., & Holyoak, K. J. (1993). Pragmatic knowledge and conceptual

structure: Determinants of transfer between quantitative domains. In D. K. Detterman &

R. J. Sternberg (Eds.), Transfer on trial: Intelligence, cognition, and instruction (pp. 68-

98). Norwood, NJ: Ablex.

Bean, J. P. (1985). Interaction effects based on class level in an explanatory model

of college dropout syndrome. American Educational Research Journal, 22, 35-64.

Beretier, C. (1995). A dispositional view of transfer. In A. McKeough, J. Lupart,

& A. Marini (Eds.), Teaching for transfer: Fostering generalization in learning (pp. 21-

34). Mahwah, NJ: Erlbaum.

Bloom, B. S. (1984). Taxonomy of educational objectives. New York: Longman.

Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2000). Self-regulation: An

introductory overview. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook

of self-regulation (pp. 1-9). New York: Academic Press.

Booth, A. (1997). Listening to students: Experiences and expectations in the

transition to a history degree. Studies in Higher Education, 22, 205-219.

Bouffard-Bouchard, T., Parent, S., & Larivee, S. (1991). Influence of self-efficacy

on self-regulation and performance among junior and senior high-school age students.

International Journal of Behavioral Development, 14, 153-164.

Boylan, H. R. (1997). Criteria for program evaluation in developmental education.

Research in Developmental Education, 14 (1), 1-4.

Boylan, H. R., Bliss, L. B., & Bonham, B. S. (1997). Program components and

their relationship to student performance. Journal of Developmental Education, 20 (3),

2-8.

203

Boylan, H. R., George, A. P., & Bonham, B. S. (1991). Program evaluation. In R.

F. Flippo & D. C. Caverly (Eds.), College reading and study strategy programs (pp. 70-

117). Newark, DE: International Reading Association.

Brell, C. D. (1990). Critical thinking as transfer: The reconstructive integration of

otherwise discrete interpretations of experience. Educational Theory, 40, 53-68.

Breneman, D. W., & Haarlow, W. N. (April, 1999). Establishing the real value of

remedial education. Chronicle of Higher Education, pp. B6-B7.

Campione, J. C., Shapiro, A. M., & Brown, A. L. (1995). Forms of transfer in a

community of learners: Flexible learning and understanding. In A. McKeough, J. Lupart,

& A. Marini (Eds.), Teaching for transfer: Fostering generalization in learning (pp. 55-

68). Mahwah, NJ: Erlbaum.

Carson, J. G., Chase, N. D., Gibson, S. U., & Hargrove, M. F. (1992). Literacy

demands of the undergraduate curriculum. Reading Research and Instruction, 31, 25-50.

Carver, C. S., & Scheier, M. F. (2000). On the structure of self-regulation. In M.

Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 41-84).

New York: Academic Press.

Casazza, M. E., & Silverman, S. L. (1996). Learning assistance and

developmental education. San Francisco: Jossey-Bass.

Chase, N. D., Gibson, S. U., & Carson, J. G. (1993). Preparing students for

college: Making the transition. Georgia Journal of Reading, 19, 4-10.

Chase, N. D., Gibson, S. U., & Carson, J. G. (1994). An examination of reading

demands across four college courses. Journal of Developmental Education, 18 (1), 10-16.

204

Christopoulos, J. P., Rohwer, W. D., & Thomas, J. W. (1987). Grade level

differences in students � study activities as a function of course characteristics.

Contemporary Educational Psychology, 12, 303-323.

Clark-Thayer, S. (1995). NADE self-evaluation guides. Clearwater, FL: H & H

Publishing.

Commander, N. E., & Smith, B. D. (1995). Developing adjunct reading and

learning courses that work. Journal of Reading, 38, 352-360.

Connell, C. M. (2000). A multi-case study of five former successful students of a

strategic learning course in a subsequent sociology course. (Doctoral Dissertation,

University of Georgia, 2000).

Curley, R. G., Estrin, E. T., Thomas, J. W., & Rohwer, W. D. (1987).

Relationships between study activities and achievement as a function of grade level and

course characteristics. Contemporary Educational Psychology, 12, 324-343.

Dembo, M. H., & Jakubowski, T. G. (1999, April). An Evaluation of a Learning

to Learn Course. Paper presented at the annual meeting of the American Educational

Research Association, Montreal, Canada.

Detterman, D. K. (1993). The case for the prosecution: Transfer as an

epiphenomenon. In D. K. Detterman & R. J. Sternberg (Eds.), Transfer on trial:

intelligence, cognition, and instruction (pp. 1-24). Norwood, NJ: Ablex.

Doyle, W. (1983). Academic work. Review of Educational Research, 53, 159-

199.

Dubois, N. F., Dennison, R. S., & Staley, R. (1998). Longitudinal Analysis of a

College Study Skills Course: Motivation, Study Tactics and GPA Relationships. Paper

205

presented at the annual meeting of the American Educational Research Association, San

Diego, CA.

Elifson, J. M., Pounds, M. L., & Stone, K. R. (1995). Planning for and

assessment of developmental programs. Journal of Developmental Education, 19 (1), 2-

11.

Garcia, T. (1995). The role of motivational strategies in self-regulated learning. In

P. R. Pintrich (Ed.), Understanding self-regulated learning (pp. 29-42). San Francisco:

Jossey-Bass.

Garner, R. (1988). Metacognitive reading comprehension. Mawah, NJ: Ablex.

Garner, R. (1990). When children and adults do not use learning strategies:

Toward a theory of settings. Review of Educational Research, 60, 517-529.

Garner, R., & Alexander, P. A. (1989). Metacognition: Answered and

unanswered questions. Educational Psychologist, 24, 143-158.

Gebelt, J. L., Parilis, G. M., Kramer, D. A., & Wilson, P. (1996). Retention at a

large university: Combining skills with course content. Journal of Developmental

Education, 20 (1),

2-10.

Gordon, W. I., Lindner, R. W., & Harris, B. R. (1996, April). A Factor Analytic

Study of the Self-Regulated Learning Inventory. Paper presented at the meeting of the

American Psychological Association, New York.

Graham, S. (1994). Classroom motivation from an attributional perspective. In

H. F. O �Neil & M. Drillings (Eds.), Motivation: Theory and research (pp. 31-48).

Hillsdale, NJ: Erlbaum.

206

Greene, J. C. (1994). Qualitative program evaluation. In N. K. Denizen & Y. S.

Lincoln (Eds.), Handbook of qualitative research (pp. 530-554). London: Sage.

Hadwin, A. F., & Winne, P. H. (1990). Study strategies have meager support.

Journal of Higher Education, 67, 692-715.

Haskell, R. E. (2001). Transfer of learning: Cognition, instruction, and

reasoning. NY: Academic Press.

Hennessey, J. H. (1990). At-risk community college students and a reading

improvement course: A longitudinal study. Journal of Reading, 34, 114-120.

Hinchman, K. A., & Zalewski, P. (1996). Reading for success in a tenth-grade

global studies class: A qualitative study. Journal of Literacy Research, 28, 91-106.

House, J. D., & Wohlt, V. (1990). The effect of tutoring program participation on

the performance of academically under-prepared college freshmen. Journal of College

Student Development, 31, 365-370.

Huck, S. W., & Cromier, W. H. (1996). Reading statistics research. New York:

Harper Collins College Publishers.

Hynd, C. R. (1999). Teaching students to think critically using multiple texts in

history. Journal of Adolescent and Adult Literacy, 42, 428-436.

Jenkins, J. J. (1979). Four points to remember: A tetrahedral model of memory

experiments. In L. S. Cermak & F. I. M. Craik (Eds.), Levels of processing in human

memory (pp. 429-446). Hillsdale, NJ: Erlbaum.

Joint Committee on Standards for Educational Evaluation. (1994). The Program

evaluation standards: How to assess evaluations of educational programs (2nd ed.).

London: Sage Publications.

207

Jones, L. R., Mullis, I. V. S., Raizen, S. A., Weiss, I. R., & Weston, E. A. (1990).

The 1990 science report card: NAEP �s assessment of fourth, eighth, and twelfth graders.

Washington, D. C.: Educational Testing Service.

Keimig, R. T. (1983). Raising academic standards: A guide to learning

improvement.

(ASHE ERIC Report No 4). Washington, D.C.: Association for the Study of Higher

Education. (ERIC Document Reproduction Service No. ED 346 082).

Keppel, G. (1991). Design and analysis: A researchers � handbook. Upper Saddle

River, NJ: Simon & Schuster.

King, P. M., Wood, P. K., & Mines, R. A. (1990). Critical thinking among college

and graduate students. The Review of Higher Education, 13, 168-186.

Kulik, C. C., Kulik, J. A., & Shwalb, B. J. (1983). College programs for high-risk

and disadvantaged students: A meta-analysis of findings. Review of Educational

Research, 53, 397-414.

Lauridsen, C. C. (1980). Examining the scope of learning centers. San Francisco:

Jossey-Bass.

Lindner, R. W., & Harris, B. R. (1992). Self-regulated learning: Its assessment

and instructional implications. Educational Research Quarterly, 16 (2), 29-37.

Lindner, R. W., & Harris, B. R. (1998). Self-regulated learning in education.

Journal of General Education, 47, 63-78.

Maring, G. H., Shea, M. A., & Warner, D. A. (1987). Assessing the effects of

college reading and study skills programs: A basic evaluation model. Journal of Reading,

30, 402-408.

208

Marini, A., & Genereux, R. (1995). The challenge of teaching for transfer. In

A. McKeough, J. Lupart, & A. Marini (Eds.), Teaching for transfer: Fostering

generalization in learning (pp. 1-19). Mahwah, NJ: Erlbaum.

Marsh, H. W. (1990). Causal ordering of academic self-concept and academic

achievement: A multiwave, longitudinal panel analysis. Journal of Educational

Psychology, 82, 646-656.

Martin, D. C., & Arendale, D. (January, 1994). Review of Research Concerning

Effectiveness of SI from the University of Missouri - Kansas City and Other Institutions

Across the United States. Paper presented at the annual conference of Freshman Year

Experience. Columbia, SC.

Maxwell, M. (1997). Improving student learning. Clearwater, FL: H & H

Publishing.

McCombs, B. L. (1986). The role of the self-system in self-regulated learning.

Contemporary Educational Psychology, 11, 314-332.

McKeough, A., Lupart, J., Marini, A. (1995). Preface. In A. McKeough, J. Lupart,

& A. Marini (Eds.), Teaching for transfer: Fostering generalization in learning (pp. vii-

viii). Mahwah, NJ: Erlbaum.

McPeck, J. E. (1992). Thoughts on subject specificity. In S.P. Norris (Ed.), The

generalizability of critical thinking (pp. 198-205). NY: Teachers College Press.

Mealey, D. L. (1991). Program evaluation: The politics of developmental reading.

In T. Rasinski, N. Padak, & J. Logan (Eds.), Reading is knowledge (pp. 1-16). Pittsburg,

KS: College Reading Association.

Mentkowski, M. (2000). Learning that lasts: Integrating learning, development,

and performance in college and beyond. San Francisco: Jossey-Bass.

209

Murden, T., & Gillespie, C. S. (1997). The role of textbooks and reading in

content area classrooms: What are teachers and students saying? In W. M. Linek & E. G.

Sturtevant (Eds.), Exploring literacy (pp. 51-84). Commerce, TX: The College Reading

Association.

Napoli, A. R., & Hiltner, G. J. (1993). An evaluation of developmental reading

instruction. Journal of Developmental Education, 17 (1), 14-20.

Nist, S. L., & Simpson, M. L. (2000). College studying. In P. D. Pearson, P.

Mosenthal, M. L. Kamil, & R. Barr (Eds.), Handbook of reading research (Vol. III, pp.

645-666). Mahwah, NJ: Erlbaum.

Nist, S. L., Simpson, M. L., & Olejnik, S. (1991). The relation between self-

selected study processes and test performance. American Educational Research Journal,

28, 849-874.

Norris, S. P., & Phillips, L. M. (1987). Explanations of reading comprehension:

Schema theory and critical thinking theory. Teachers College Record, 89, 281-306.

Office of Undergraduate Admissions (2000-2001). Undergraduate Bulletin.

Athens, GA: The University of Georgia.

O �Hear, M. F., & McDonald, R. B. (1995). A critical review of research in

developmental education: Part I. Journal of Developmental Education, 19 (2), 2-6.

Olejnik, S., & Nist, S. L. (1992). Identifying latent variables measured by the

Learning and Study Strategies Inventory ( LASSI). Journal of Experimental Education, 60

(2), 151-169.

Orlando, V. P., Caverly, D. C., Swetnam, L. A., & Flippo, R. F. (1989). Text

demands in college classes: An investigation. Forum for Reading, 21, (1), 43-48.

210

Pajares, M. F. (1992). Teachers � beliefs and educational research: Cleaning up a

messy construct. Review of Educational Research, 62, 307-332.

Paris, S. G., Lipson, M. Y., & Wixson, K. K. (1983). Becoming a strategic reader.

Contemporary Educational Psychology, 8, 293-316.

Payne, D. A. (1994). Designing educational project and program evaluations.

Boston: Kluwer Academic Publishers.

Pedhazur, E. J. (1997). Multiple regression in behavioral research. New York:

Harcourt Brace College Publishers.

Phillips, L. M. (1992). The generalizability of self-regulatory thinking strategies.

In S. P. Norris (Ed.), The generalizability of critical thinking (pp. 138-156). NY:

Teachers College Press.

Pintrich, P. R. (1995). Understanding self-regulated learning. In P. R. Pintrich

(Ed.), Understanding self-regulated learning (pp. 3-12). San Francisco: Jossey-Bass.

Pintrich, P. R., & DeGroot, E. V. (1990). Motivational and self-regulated learning

components of classroom academic performance. Journal of Educational Psychology, 82,

33-40.

Pintrich, P. R., & Garcia, T. (1991). Student goal orientation and self-regulation in

the college classroom. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation

and achievement: Goals and self-regulatory processes (Vol. 7, pp. 371-402). Greenwich,

CT:JAI Press.

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993).

Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire

(MSLQ). Educational and Psychological Measurement, 53, 801-813.

211

Pressley, M. (1995). More about the development of self-regulation: Complex,

long-term, and thoroughly social. Educational Psychologist, 30, 207-212.

Pressley, M. (2000). What should comprehension instruction be the instruction

of? In M. Kamil, P. B. Mosenthal, P. D. Pearson, & R. Barr (Eds.), Handbook of reading

research (Vol. III, pp.545-559). Mahwah, NJ: Erlbaum.

Pressley, M., El-Dinary, P. B., Brown, R., Schuder, T., Bergman, J. L., York, M.,

& Gaskins, I. W. (1995). A transactional strategies instruction Christmas Carol. In

A. McKeough, J. Lupart, & A. Marini (Eds.), Teaching for transfer: Fostering

generalization in learning (pp. 177-213). Mahwah, NJ: Erlbaum.

Pugh, S. L., Pawman, F., & Antommarchi, C. (2000). Academic literacy and the

new college learner. In R. F. Flippo & D.C. Caverly (Eds.), Handbook of college reading

and study strategies research (pp. 25-42). Mahwah, NJ: Erlbaum.

Randall, S. N. (1999). [Survey of high school history experiences]. Unpublished

raw data.

Randall, S. N. (2000a). Students � Perceptions of Learning to Learn. Unpublished

survey.

Randall, S. N. (2000b). Transfer of Learning to Learn Strategies. Unpublished

survey.

Ravitch, D., & Finn, C. E. (1987). What do our 17-year-olds know? New York:

Harper & Row.

Rossi, P.H., Freeman, H.E., & Lispsey, M. W. (1999). Evaluation: A systematic

approach. London: Sage.

Salomon, G., & Perkins, D. N. (1989). Rocky roads to transfer: Rethinking

mechanisms of a neglected phenomenon. Educational Psychologist, 24, 113-142.

212

Schommer, M. (1990). Effects of beliefs about the nature of knowledge on

comprehension. Journal of Educational Psychology, 82, 498-504.

Schommer, M. (1993). Epistemological development and academic performance

among secondary students. Journal of Educational Psychology, 85, 406-411.

Simpson, M.L., Hynd, C. R., Nist, S. L., & Burrell, K. I. (1997). College academic

assistance programs and practices. Educational Psychological Review, 9, 39-87.

Simpson, M. L., & Nist, S. L. (1997). Perspectives on learning history: A case

study. Journal of Literacy Research, 29, 363-395.

Simpson, M. L., Stahl, N. A., & Hayes, C. G. (1989). PORPE: A research

validation. Journal of Reading 33, 22-28.

Somers, R. L. (1987). Evaluation of developmental education programs: Issues,

problems, and techniques. Research in Developmental Education, 4 (1), 1-6.

Sparrow, D. E. L. (1998). The relation between long-term goals, learning

strategies, motivation, and academic performance of college freshmen and sophomores in

a history course. (Doctoral Dissertation, University of Georgia, 1998).

Sternberg, R. J., & Frensch, P.A. (1993). Mechanisms of transfer. In D. K.

Detterman & R. J. Sternberg (Eds.), Transfer on trial: Intelligence, cognition, and

instruction (pp. 25-38). Norwood, NJ: Ablex.

Strage, A., Tyler, A. B., Rohwer, W. D., & Thomas, J. W. (1987). An analytic

framework for assessing distinctive course features within and across grade levels.

Contemporary Educational Psychology, 12, 280-302.

Sturtevant, E. G. (1996). Lifetime influences on the literacy-related instructional

beliefs of experienced high school history teachers: Two comparative case studies.

Journal of Literacy Research, 28, 227-257.

213

Thomas, J. W., Bol, L., & Warkentin, R. W. (1991). Antecedents of college

students � study deficiencies: The relationship between course features and students � study

activities. Higher Education, 22, 275-296.

Thomas, J. W., Iventosch, L., & Rohwer, W. D. (1987). Relationships among

student characteristics, study activities, and achievement as a function of course

characteristics. Contemporary Educational Psychology, 12, 344-364.

Trigwell, K., Prosser, M., & Waterhouse, F. (1999). Relations between teachers �

approaches to teaching and students � approaches to learning. Higher Education, 37, 57-

70.

Van Etten, S., Frelbern, G., & Pressley, M. (1997). College students � beliefs about

exam preparation. Contemporary Educational Psychology, 22, 192-212.

Van Meter, P., Yokoi, L., & Pressley, M. (1994). College Students � theory of

note-taking derived from their perceptions of note-taking. Journal of Educational

Psychology, 86, 323-338.

Walvekar, C. C. (Ed.). (1981). Assessment of learning assistance services. San

Francisco: Jossey-Bass.

Warkentin, R., Stallworth-Clark, R., & Nolen, M. (1999, April). Course features

that influence self-directed study practices in a college history class: Linking strategy

instruction to authentic content and context. Paper presented at the Annual Meeting of the

American Educational Research Association, Montreal.

Weinberg, S. L., & Goldberg, K. P. (1990). Statistics for the behavioral sciences.

New York: Cambridge University Press.

Weinstein, C. E., Dierking, D., Husman, J., Roska, L., & Powderill, L. (1998).

The impact of a course in strategic learning on the long-term retention of college students.

214

In J. Higbee & Pat Dwinnell (Eds.), Developmental education: Preparing successful

college students (pp. 85-96). Columbia, SC: National Research Center for the First Year

Experience and Students in Transition.

Weinstein, C. E., Hanson, G., Powdrill, L., Roska, L., Dierking, D., Husman, J.,

& McCann, E. (1997). The design and evaluation of a course in strategic learning. NADE

Selected Conference Papers, Vol.3 (pp.53 -55).

Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Self-regulation

interventions with a focus on learning strategies. In M. Boekaerts, P. R. Pintrich, &

M. Zeidner (Eds.), Handbook of self-regulation (pp. 727-747). New York: Academic

Press.

Weinstein, C. E., Schulte, A. C., & Palmer, D. R. (1987). The Learning and Study

Strategies Inventory (LASSI). Clearwater, FL: H & H.

Wilcox, K. J., del Mas, R. C., Stewart, B., Johnson, A. B., & Ghere, D. (1997).

The � package course � experience and developmental education. Journal of

Developmental Education, 20 (3), 18-26.

Winne, P. H. (1995). Inherent details in self-regulated learning. Educational

Psychologist, 30, 173-187.

Wood, E., Motz, M., & Willoughby, T. (1998). Examining students � retrospective

memories of strategy development. Journal of Educational Psychology, 90, 698-704.

Zimmerman, B. J. (1998a). Academic studying and the development of personal

skill: A self-regulatory perspective. Educational Psychologist, 33, 73-86.

Zimmerman, B. J. (1998b). Developing self-fulfilling cycles of academic

regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J.

215

Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp.

1-19). New York: Guilford Press.

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive

perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-

regulation (pp. 13-39). New York: Academic Press.

Zimmerman, B. J., Bandura, A., & Martinez Pons, M. (1992). Self-motivations

for academic attainment: The role of self-efficacy beliefs and personal goal setting.

American Educational Research Journal, 29, 663-676.

Zimmerman, B. J., & Martinez Pons, M. (1986). Development of a structured

interview for assessing student use of self-regulated learning strategies. American

Educational Research Journal, 23, 614-628.

216

APPENDICES

APPENDIX A

EMAIL MESSAGE TO POTENTIAL PARTICIPANTS

Need some quick and easy CASH?

$200.00 $100.00 $75.00 $50.00 Tom Montgomery, who was your UNIV 1102 instructor, suggested I contact you as a possible participant in my study. WHO? Students who have taken UNIV 1102. Everyone earns $10.00. WHAT? Research focusing on how to improve UNIV 1102 HOW? Spend 45 minutes to complete 3 written questionnaires WHY? To help improve an important course here at UGA GRAND PRIZE? Drawing for four large cash prizes: $200, 100, 75, and 50 It sounds good, doesn't it? This may be the best offer you have had all semester! Are you interested? If you would be willing to participate and want to be eligible for one of the large cash prizes, please email me (srandall@arches.uga.edu) or call me (work - 542-0468 or home - 769-7540) before February 19th. Thanks for considering my request. Sally N. Randall The Division of Academic Assistance 205 Milledge Hall 542-0468 Work 769-7540 Home srandall@arches.uga.edu

217

APPENDIX B

LETTER TO PARENTS OF POTENTIAL PARTICIPANTS

March 5, 2001 Dear Parents,

As you may have read in the papers recently, there is concern among the faculty and the administration at the University of Georgia about the experience undergraduates have at this large research-oriented university. Efforts are underway by several groups on campus to study the undergraduate experience and subsequently create a more personal and supportive environment for undergraduate students.

We share this concern and are especially interested in how the courses offered by the Division of Academic Assistance help students make a successful transition from high school to college academic pursuits. We are in the process of evaluating the impact of one course, UNIV1102 Learning to Learn. We would like very much to meet with students who took the course a year or more ago to see if they are still using the skills and techniques they learned in UNIV1102 and how useful they find these skills to be in their other courses on campus.

We are asking for your help in locating your daughter or son so she/he can consider participating in this research. We will work around her/his busy schedule and it will take only about 45 minutes. There is a small $10.00 monetary incentive for that time, and there will also be a drawing in April for large cash awards of $200, 100, 75, and 50 as an added incentive. Each participant will be eligible for the drawing. To date, we have had only 12 participants so the odds of winning one of the larger awards is quite good. We have offered these monetary rewards because we realize that time is very precious to students, but we believe that the information they can provide us is crucial for our ongoing course improvement.

Enclosed is a flyer that explains the research and appeals to students to participate. Please share the flyer with your child or send her/him contact information so she/he can get in touch with us. We would like to finish meeting with students by the end of March so all potential participants should contact us within the next week if at all possible.

Thank you for helping in this important endeavor.

218

APPENDIX C

CONSENT FORM I agree to participate in the research titled Evaluation of "Learning to Learn:”Impact of Academic Assistance Instruction on Students’ Subsequent Academic Performance and Self-Regulation that is being conducted by Sally N. Randall in the Department of Reading Education, 542-0468. The research is being conducted under the direction of Dr. Michele L. Simpson, Division of Academic Assistance, 542-0470.

I understand that participation in this study is totally voluntary and is in no way related to any course grade. I also understand that I can withdraw from this research project at any time.

I understand the following about this research study:

The purpose of this study is to gain an understanding about the following: (a) self-regulated learning behaviors of college students, (b) students' perceptions of the usefulness of UNIV 1102 ("Learning to Learn") instruction, (c) students' ideas for modifying "Learning to Learn" so it is more beneficial to future students, and (d) how and to what extent students modify and transfer skills from "Learning to Learn" to their subsequent classes.

Participants might receive helpful ideas about effective studying from reading the surveys and inventory. The procedures will be the following. I will meet with the researcher for one 50-minute period either individually or in a small group during February, March, or April of 2001. We will meet at a mutually convenient location. I will be asked to read and complete one to three written instruments pertaining to self-regulated learning, my perceptions of UNIV 1102, and my own study strategies.

No discomforts or stresses are foreseen. No risks are foreseen.

The results of this participation will be confidential and will not be released in any individually identifiable form without my prior consent, unless otherwise required by law. I understand that after the data is analyzed, all identifying information will be removed from the instruments I complete.

I give my permission for the researcher to quote my written comments anonymously in future publications reporting on this research.

The researcher will answer any further questions about the research, now or during the course of the project, and can be reached at 542-0468.

Signature of Participant ______________________________________ Date__________ Signature of Researcher ______________________________________ Date _________

Please sign both copies of this form. Keep one and return the other to the investigator.

Research at the University of Georgia that involves human participants is overseen by the Institutional Review Board. Questions or problems regarding your rights as a participant should be addressed to Ms. Julia Alexander, M. A., Institutional Review Board; Office of the V. P. for Research; The University of Georgia; 606A Graduate Studies Research Center; Athens, Georgia 30602-7411; Telephone (706) 542-6514.

220

APPENDIX D

SELF-REGULATED LEARNING INVENTORY© Lindner, Harris & Gordon V 4.01, 1995

Instructions: Please read each statement and then circle a response according to the followin g key:a = Almost always typical of meb = Frequently typical of mec = Somewhat typical of med = Not very typical of mee = Not at all typical of me

Respond as candidly and completely as possible by selecting the response most descriptive of your usualapproach, and/or attitude, toward academic coursework. Try to rate yourself according to how well thestatement describes you, not in terms of how you think you should be or what others think of you. Thereare no r ight or wrong answers. Your responses will be kept str ictly confidential and are for resear chpurposes only. Please complete all of the items.

1. Studying is a mysterious process. Sometimeswhat I do is successful, other times it is not. Butin either case, I don � t real ly kn ow why.

a b c d e

2. I come to each class session prepared todiscuss the assigned reading material (e.g.,chapter, handout, ar ticles). a b c d e

3. Mastery of new knowledge or skills is moreimportant to me than how well I do compared toothers. a b c d e

4. If I am struggling to understand thematerial presented in the course, I try to getsome useful hints from someone who does.

a b c d e

5. When r eading a text or listening to a lecture,I consciously attempt to separatethe main idea from the supporting details.

a b c d e

6. In classes where I find notetaking to benecessary, I review my notes from the previousclass sometime before the next class meeting.

a b c d e

7. In order to help me do my best and keepmyself focused, I develop specific, short-termgoals for the courses in which I am enrolled.

a b c d e

8. If I am having trouble understandingmaterial as presented in a class or text, I try tolocate and read different materials which help toexplain or clarify the ideas with which I amhaving trouble. a b c d e

9. After studying new information for a class, Ipause and perform a mental review in order todetermine how much of what I have read I amable to recall. a b c d e

10. When reviewing my class notes, I try toidentify the main points of a lecture by markingor highlighting them. a b c d e

11. When I fall behind most of the rest of theclass in a subject, I worry I may not be smartenough to succeed. a b c d e

12. When unclear about material presented inclass, one str ategy I use is to check my notesagainst those of a classmate. a b c d e

13. When reading a text or reviewing my notes,I sometimes stop and ask myself: � Am Iunderstanding any of this? � a b c d e

221

14. I try to pick out and write down the mainpoints during a class lecture. a b c d e

15. To help me stay on track, I promise toreward myself if I do well on a test or in acourse. a b c d e

16. When they are available and I feel I need thehelp, I part icipate in study group sessions.

a b c d e

17. When evaluating my level of readinessbefore taking an exam, if I determine I am notquite ready, I const ruct a plan to help me bebetter prepared. a b c d e

18. To help me retain and understand what I amstudying, I diagram, outline or otherwiseorganize the material I am learning.

a b c d e

19. I find that if I �m not doing as well as Iexpected in a course, I become less motivated.

a b c d e

20. When studying, I isolate myself fromanything that migh t distract me. a b c d e

21. If my attention starts to drift when studying,I pull myself back on task by mentally sayingthings like; �Stay focused, � � Work carefully, �etc. a b c d e

22. To help me understand and comprehend thematerial I am studying, I try to rephrase it in myown words. a b c d e

23. In deciding which sections of a class toenroll in, I look for situations that offer a modestdegree of challenge. a b c d e

24. I study pret ty much on an � as the needarises � basis. a b c d e 25. After having taken an exam, I consciouslytry to determine how well I did in selecting andprepar ing for the concepts that actually appearedon the test. a b c d e

26. When learning unfamiliar material that iscomplex, I organize (e.g., outline, map) it insuch a way that it fits logically together in mymind. a b c d e

27. I only strive to do well in classes or coursesthat are important or interesting to mepersonally. a b c d e

28. When I study, I set aside a certain amountof time and choose an appropriate place where Iwill not be interrupted. a b c d e

29. When reviewing sections of a text or mynotes in preparing for an exam, I deliberatelypause and attempt to recall from memoryeverything I can about those sections before Ireread them. a b c d e

30. To help make it easier for me to understandwhat I am studying, I try to relate it to or thinkof examples from my own life. a b c d e

31. Even if a course becomes boring, or is lessthan interesting to begin with, I continue towork hard and to try to do my best.

a b c d e

32. Due to competing demands, I find it difficultto stick to a study schedule. a b c d e

33. Even when I feel like I put a lot of effort intopreparing for an exam, I don � t do as well as Iexpected. a b c d e

34. When learning new material, I try toelaborate, expand on, or otherwise add � life � towhat I am learning. a b c d e

35. Whenever I am not doing as well in acourse as I would like, my approach is toidentify the problem and develop a plan to solveit. a b c d e

36. To help me accomplish the academic goals Ihave set, I develop, post and regularly review aplan or schedule to follow. a b c d e

37. After studying for an exam, I try to reflecton how effective my study strategy was inhelping me learn the material on which I havebeen working. a b c d e

222

38. When studying or learning concepts orideas which are abstract, I t ry to visualize orthink of a concrete situation or event in whichthey might be useful or occur. a b c d e

39. I feel confused and undecided about whatmy educational goals should be. a b c d e

40. Al though I know what things I should bedoing to get better grades, I often don � t do thembecause of conflicts and distractions which comeinto my life. a b c d e

41. When studying, I mark or otherwise keeptrack of any concepts, terms, or ideas I do notfully understand. a b c d e

42. When I have to lea rn unfamiliar concepts orideas which are unrelated, I use mental imageryto help tie them together. a b c d e

43. Even when a class turns out to be moredifficult or less interesting that I expected, it isstill personally important for me to do my best.

a b c d e

44. I study pretty much on a � cram the nightbefore the exam � basis. a b c d e

45. When studying, instead of simply rereadingeverything twice, I go back and focus on theconcepts, ideas, or procedures I found mostdifficult to understand or remember.

a b c d e

46. If a topic I am learning is unfamiliar, I tryto think of an analogy to ideas and/orexperiences with which I am already familiar.

a b c d e

47. Even when I find myself really struggling ina class, I don �t give up but continue to try to domy best. a b c d e

48. Even when struggling in a course, I find itvery difficult to go to my instructor and talkabout the situation. a b c d e

49. Before reading a chapter in a textbook orother assigned reading, I first skim through thematerial to get a general idea of the topic andthen ask myself, � What do I know about thistopic already? � a b c d e

50. When I have to learn or recal l a lengthy setof related items from memory. I try to associateeach item with an un usual image.

a b c d e

51. I tend to believe that how much I learn froma given class or course is pr imari ly determinedby myself. a b c d e

52. To help me get the most from my courses, Iask questions or otherwise seek clarificationfrom my instructors as much as I can.

a b c d e

53. Before I begin to seriously study, I carefullyexamine and analyze the amount, familiarityand difficulty of the material I need to master inorder to succeed. a b c d e

54. When studying for an exam, I have a hardtime distinguishing the main ideas and conceptsfrom the less important information.

a b c d e

55. I approach most of my classes withconsiderable confidence because I know what Iam capable of academically. a b c d e

56. If I do not understand something during aclass meeting, I will ask for additionalclarification. a b c d e

57. After preparing for an exam, I ask myself, � If I had to take a test on this topic right now,what grade would I expect? � a b c d e

58. Before reading a chapter in the text book, Iread the review questions at the end of thechapter (or provided by the instructor) to helpme decide what to focus on when studying.

a b c d e

59. When learning becomes stressful ofdifficult, I actively try to get a handle on thesituation by doing things such as increasingeffort or seeking additional information to helpclarify the task. a b c d e

223

60. I use a calendar /daily planner or otherwisekeep track of my classes, assignments, andimportant dates. a b c d e

61. When faced with a problem in my classes(e.g., preparing for an exam, writing a paper), tohelp me succeed I develop a plan or strategy touse as a guide and to evaluate my progress.

a b c d e

62. During class presentations, I attendcarefully to any cues the instructor providesabout which concepts and ideas ar e the mostimportant to learn and retain. a b c d e

63. I believe tha t ability is what determinesacademic success or failure. a b c d e

64. Even when unsure if I understand what isbeing presented, I don � t ask questions in class.

a b c d e

65. After taking an exam, I review and evaluatethe strategies I used in preparing for the exam todetermine how effective I was and how I coulduse th is information to improve in preparing forfuture exams. a b c d e

66. When taking notes in class, I usually try toorganize,( map, highl ight, underl ine, outl ine,etc.) the in formation presented in a logical way.

a b c d e

67. If I don � t learn a concept or skill fairlyquickly, I become discouraged and stop trying.

a b c d e

68. In preparing for a class presentation or termpaper, I carefully investigate and fully utilize theresources of the campus library. a b c d e

69. When preparing to study a chapter in atextbook or other reading material, in order todetermine where I need to focus my attention, Ifirst skim over the entire text to get a mentalpicture of how the material is presented.

a b c d e

70. In reading from a textbook, I focus mostlyon the meanin g of specific words or terms.

a b c d e

71. I see grades as something an instructor givesrather than something a student earns.

a b c d e

72. If I run into an unfamiliar word or term inmy reading for a class, I stop and look it up in adictionary. a b c d e

73. When stuck on a problem or in my attemptto comprehend material for a class, I try to thinkof an analogy or a comparison between mypresent si tuat ion and similar situations I havebeen in. a b c d e

74. During class lectures, I find it difficult toseparate the main points from the less importantmaterial. a b c d e

75. The grades I receive are pretty much amatter of how hard I work and how much time Iput into studying. a b c d e

76. I turn my assignments in on time and keepup with the assigned reading in my courses.

a b c d e

77. When preparing for a class paper, project , orpresentation, I not only think about the topic andcreate an outline to work from, but try toanticipate any questions the audience I ampreparing for might have. a b c d e

78. I always try to learn new or unfamiliarmaterial exactly as stated in my text ro by myinstructor. a b c d e

79. I enjoy taking courses that are challengingor cover unfamiliar subject mater ial becausethey present the greatest oppor tunity forlearning.

a b c d e

80. Deciding how to most effectively utilize mytime in preparing for exams is difficult for me.

a b c d e

224

APPENDIX E

STUDENTS � PERCEPTIONS OF LEARNING TO LEARN

Learning to Learn (UNIV 1102) is designed to help students meet their potentialsas effective and efficient students at The University of Georgia. We would like yourfeedback in order to make improvements to the course for future students. Rate theoverall value of each of these six instructional components from UNIV 1102 bycircling your answer choice.

5. Annotating textsa. very usefulb. somewhat usefulc. not at all usefuld. unsuree. I had this skill before I took UNIV 1102.

6. Taking lecture notesa. very usefulb. somewhat usefulc. not at all usefuld. unsuree. I had this skill before I took UNIV 1102.

7. Rehearsal/test preparations strategies (maps, cards, PORPE, talk throughs, etc.)a. very usefulb. somewhat usefulc. not at all usefuld. unsuree. I had this skill before I took UNIV 1102.

8. Time management strategiesa. very usefulb. somewhat usefulc. not at all usefuld. unsuree. I had this skill before I took UNIV 1102.

225

9. Motivation strategiesa. very usefulb. somewhat usefulc. not at all usefuld. unsuree. I had this skill before I took UNIV 1102.

10. Beliefs about knowledge and learninga. very usefulb. somewhat usefulc. not at all usefuld. unsuree. I had this skill before I took UNIV 1102.

Now, for any above that you circled � a � ( � very useful � ), please explain why. Besure to indicate which number you are discussing.

For any above that you circled � c � ( � not at all useful � ), please explain why it wasnot useful. Be sure to indicate which number you are discussing.

226

Was there anything else about the course that was useful to you that we did notinclude in the six components above? Please explain.

Are there any components in the six above that you would suggest be expanded byspending more time, by offering more practice, or by discussing in more depth?Please explain.

What instructional areas were not included in the course that you would like to seeadded to the curriculum? Please explain why.

227

What instructional areas were unnecessary and should be omitted from thecurriculum? Please explain why.

How could the organization of the course be improved so that instruction would bemore efficient and effective? In other words, how might the course be taughtdifferently?

11. What was the main reason you took UNIV 1102? Circle just one answer.a. I needed an A to boost my GPA.b. I wanted to learn new ideas for improving my study techniques.c. My parent or advisor pressured me to take it.

Please explain if you think an explanation will clarify your answer.

APPENDIX F

MOTIVATION QUESTIONS A For students who were informed about Learning to Learn when they registered for fall semester of their freshman year - Code ______________________

UNIV 1102 is a credit-bearing class that teaches effective study techniques for many different kinds of courses and test formats. It also focuses on motivation, time management, learning theories, and other issues related to being a successful student. UNIV 1102 is designed to help students make the academic transition from high school to college. 1. Why did you not enroll in UNIV 1102 for the fall of your freshman year? Circle one only.) a. I did not think I needed to learn new study techniques. b. I wanted to take it but I did not have room in my schedule. c. I wanted to take it but it was not an elective for my major. d. I wanted to take it but all the sections were closed. e. I had heard it required a lot of work. f. Other (Please explain) _____________________________________________ _________________________________________________________________ _________________________________________________________________ 2. Did you discover during fall semester that you could use some extra help with studying for your one or more of your difficult classes? (Circle one.)

a. No (If you answered "No," go no further with these questions.) b. Yes (If you answered "Yes," please answer the next question.) 3. What kind of help did you find for your difficult classes during your freshman year? (Circle all that are true.) a. I tried to find help but I could not find any. b. I used departmental tutors. c. I used tutors at Milledge Hall.

d. I found a tutor on my own. (Please explain who this tutor was and how you located the tutor.)___________________________________________________ __________________________________________________________________ __________________________________________________________________ e. I asked my professor or TA for help. f. I registered for an adjunct class. g. I studied regularly with other students who understood the material better than I did. h. Other (Please explain.) ____________________________________________ _________________________________________________________________

228

APPENDIX G

MOTIVATION QUESTIONS B

For students who were not informed about UNIV when they registered for fall semester of their freshman year - Code __________________

UNIV 1102 is a credit-bearing class that teaches effective study techniques for many different kinds of courses and test formats. It also focuses on motivation, time management, learning theories, and other issues related to being a successful student. UNIV 1102 is designed to help students make the academic transition from high school to college. 1. If you had known about UNIV 1102 when you registered for fall semester of your freshman year, would you have enrolled if you could have fit it in your schedule? (Circle one.) a. Yes (If you answered "Yes," skip the next question and go to Question #3.) b. No (If you answered "No," please answer both Question #2 & #3.) 2. Why would you not have enrolled in UNIV 1102 ? Circle one only.)

a. I did not think I needed to learn new study techniques. b. I had heard it required a lot of work. c. Other (Please explain) _____________________________________________ _________________________________________________________________ _________________________________________________________________ 3. What kind of help did you seek out for your difficult classes? (Circle all that are true.) a. I did not need any help. b. I tried to find help but I could not find any. c. I used departmental tutors. d. I used tutors at Milledge Hall.

e. I found a tutor on my own. (Please explain who this tutor was and how you located the tutor.)___________________________________________________ __________________________________________________________________ __________________________________________________________________ f. I asked my professor or TA for help. g. I registered for an adjunct class. h. I studied regularly with other students who understood the material better than I did. i. Other (Please explain.) ____________________________________________ _________________________________________________________________

229

230

APPENDIX H

TRANSFER OF LEARNING TO LEARN STRATEGIES

Think about the courses you took last semester and choose one of the most difficultcourses that required a lot of reading. It must be a course that you completed. DONOT CHOOSE A MATH COURSE OR A MATH-BASED COURSE OR ANENGLISH COURSE. You will answer this part of the survey about this one targetcourse. Circle your response. Then add explanations or additional information asrequested.

Target Course (Name and Number)___________________ Example: History 1112Professor _________________________12. How would you rate the difficulty level of your target course compared to othercourses you have taken at UGA?

a. difficult b. averagec. easy

13. What was your first test grade?a. Ab. Bc. Cd. De. F

14. Did your test grades for this course improve overall as the semester progressed?a. yesb. no

15. Why do you think your test grades did or did not change________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

231

16. What was your final grade for this course at the end of the semester?a. Ab. Bc. Cd. De. F

Annotations of text: Please answer these questions about annotating in the same targetcourse.

17. Did you annotate in this target course?a. yesb. no

If you answered � no, � explain in detail why you decided not to annotate as a strategy inthis course. Then skip the rest of this section and go to #32 under Taking LectureNotes.

18-23. What was the usual format of your annotations? Circle all that apply.a. in the text margins b. on sticky notesc. on the back of the previous paged. on paper stripse. another method taught in UNIV 1102 (please explain)

________________________________________________________________________f. other (explain) ______________________________________________

________________________________________________________________________________________________________________________________________________

232

24. How did you usually pace yourself as you read and annotated? Circle only one.a. I read and annotated almost every day, dividing the reading into smallsections.b. I read and annotated once or twice a week.c. I read and annotated the day or two before the test.d. other (explain) ___-__________________________________________

__________________________________________________________________

25-30. How did you usually use your annotations when you studied for tests? Circle allthat apply.

a. I read them over several times.b. I covered parts of the annotations and tested myself on possible questions.c. I talked them through one section at a time.d. I studied them a litt le every day or almost every day.e. I only used them to study the day or two before the test.f. other(explain)______________________________________________________

________________________________________________________________________

________________________________________________________________________

31. Did you change your method of annotation after the first test?a. yesb. no

If � yes, � explain in detail what changes you made.

Please explain why you made those changes.

233

Taking Lecture Notes: Please answer these questions about note-taking in the sametarget course.

32. Did you take lecture notes in your target course? a. yesb. no

If � no, � explain in detail why you decided not to take lecture notes in this class. Thenskip the rest of this section and go to #44 under Rehearsal/Test Preparation Strategies.

33. What was the usual format of your note-taking? Circle only one.a. no specific format, I just wrote down the important informationb. 1/3 of paper saved for predicting questionsc.1/2 of paper for lecture notes and ½ for text informationd. bottom of paper saved for questions, summary or retrieval cuese. another method taught in Learning to Learn (please explain) __________________________________________________________________________________________________________________________________________________f. other(explain)__________________________________________________________

________________________________________________________________________

________________________________________________________________________

34. How did you usually pace yourself as you worked with your notes? Circle one only.a.I usually did some work with my notes after every lecture before the next day � slecture.b. I usually waited to work with my notes on a weekly basis.c. I usually waited until the night or two before an exam to work with my notes.d. other

(explain)__________________________________________________________

________________________________________________________________________

________________________________________________________________________

234

35- 42. How did you usually use your notes when you worked with them or when youstudied for an exam? Circle all that apply.

a. I combined my notes with information from the text.b. I rewrote my notes, editing and adding missing material.c. I tested myself using predicted questions, retrieval cues, or summaries.d. I underlined or highlighted key parts.e. I read over my notes.f. I summarized the main points.g. I outlined my notes.h. other(explain)__________________________________________________________ _________________________________________________________________

_______________________________________________________________________

43. Did you change your note-taking procedure after the first test? a. yesb. no

If � yes, � explain in detail what changes you made.

Please explain why you made the changes.

235

Rehearsal/Test Preparation Strategies: Answer these questions about rehearsal/testpreparation strategies in the same target course.

44. Did you use any rehearsal strategies in your target course. (See question #44 belowfor possible types of rehearsal strategies.)

a. yesb. no

If � No, � explain in detail why you did not choose to make rehearsal st rategies for thiscourse. Then go on to the Self-Regulated Learning Inventory.

45-57. What was the usual format of your rehearsal strategies? Circle all that you used. a. CARDS b. studying old tests from previous semesters c. maps d. predicting and answering short answer questions e. charts f. PORPE (predicting and outlining essay answers) g. time lines h. practicing tests from the web or within computer programs i. study groups j. a study schedule for the days before the test k. talk throughs l. practiced solving sample problems m. other

(explain)________________________________________________________________

________________________________________________________________________

________________________________________________________________________

58. How do you usually pace yourself as you make these rehearsal strategies? Circle onlyone.

a. on a continual basis as I read and take lecture notesb. the week before the test c. the night before the testd. other (explain)

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

236

59-64. How did you usually use these strategies when you studied before the test? Checkall that you did.

a. I read over the information.b. I covered part of the information and tested myself.c. I sorted or eliminated the information I had learned.d. I talked about the information aloud to myself.e. I used my strategies to study with a classmate.f. other (explain)

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

65. Did you change your rehearsal strategies after the first test?a. yesb. no

If � Yes, � explain in detail what changes you made.

Please explain why you made these changes.