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
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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.
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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
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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
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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
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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
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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
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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.
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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.
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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.
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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
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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.
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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
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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
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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,
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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
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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,
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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.
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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
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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
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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}.
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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
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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
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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.
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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}.
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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.
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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
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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
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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
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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,
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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
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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-
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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
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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.
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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
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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?
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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.
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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.
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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.
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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.
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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.
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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
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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
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(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
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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
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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
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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
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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
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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.
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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,
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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.
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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
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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.
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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
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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%).
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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
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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
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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.
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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.
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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%)________________________________________________________________________
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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
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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
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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.
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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.
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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,
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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
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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?
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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
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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.
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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
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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
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(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
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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.
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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
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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
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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
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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
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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.
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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
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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).
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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.
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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
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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.
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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
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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-
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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
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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
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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
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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.
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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
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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.
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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
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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
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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.
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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
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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 ([email protected]) 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 [email protected]
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.
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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.
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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
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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
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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
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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
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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.
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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.
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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.
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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.) ____________________________________________ _________________________________________________________________
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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.) ____________________________________________ _________________________________________________________________
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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________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
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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) ______________________________________________
________________________________________________________________________________________________________________________________________________
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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.
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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)__________________________________________________________
________________________________________________________________________
________________________________________________________________________
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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.
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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)
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
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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.