The Effectiveness of Concurrent Enrollment

in Remedial Mathematics and General

Education Level Mathematics

By

Ryan Edward Grossman

B.S. (Mathematics & Mathematics Education), Indiana State University, 2010

Advisor: Dr. Subhash Bagui

A Graduate Proseminar

In Partial Fulfillment of the Degree of

Master of Science in Mathematical Sciences

The University of West Florida

July 2013

ii

The Proseminar of Ryan Edward Grossman is approved:

________________________________________ _______________ Subhash Bagui, Ph.D., Proseminar Advisor Date

_________________________________________ _______________ Josaphat Uvah, Ph.D., Proseminar Committee Chair Date

Accepted for the Department:

________________________________________ _______________ Jaromy Kuhl, Ph.D., Chair Date

iii

ABSTRACT

This study focuses on the development and implementation of the co-requisite model

implemented at Midwest Community College. The main research question is whether or not

concurrent enrollment in remediation has a statistically significant impact on student success in

the general education level math. Students were selected for the co-requisite program on the

basis of their major and initial willingness to invest a significant amount of time studying

mathematics. This ex post facto study investigates relationships between students enrolled in the

general education mathematics course and the concurrent tutorial class and those not in the

tutorial class and their overall success in the general education mathematics course. The data

analysis shows that students enrolled in the tutorial class are statistically indistinguishable from

those not enrolled in the tutorial class. Of the tutorial students who completed the general

education class, the general education mathematics class pass rate was 83%, a dramatic

improvement over the pass rate of those not enrolled in the tutorial class. Future research will

focus on variations of the concurrent enrollment model and how those changes affect student

success rates.

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DISCLOSURES

Midwestern Community College, the investigator’s employer, utilized the COMPASS

Placement Exam written by ACT, Inc during the course of this study. The investigator is an

independent contractor for ACT, Inc. who writes test questions for various ACT assessments.

After the investigator finished data collection, the College switched to a new placement exam.

The investigator played no role in the decision to retain or release the services of ACT, Inc. in

regards to College’s use of their placement exam services.

As disclosed on the paperwork for the Institutional Review Board, the instructor of the

tutorial class is the same person as the author of this study. During all phases of the study, the

protocols set forth by the Institutional Review Board approval were followed. Student

participation in this study was completely optional. Participation or the lack thereof did not

impact the grade the student received in either the general education class or the tutorial class.

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ACKNOWLEDGEMENTS

The investigator would like to thank the numerous people involved with this project.

First, I would like to thank my Proseminar advisor, Dr. Bagui. I appreciate his willingness to

coach me through the development of the Proseminar and to instruct me on the fundamentals of

Mathematical Statistics. I also am thankful for the editing services provided by Dr. Hemasinha.

His comments provided me with great insight into improving my Proseminar and Matrix Theory

work.

I would like to express my gratitude to the entire faculty in the Mathematics and Statistics

Department at University of West Florida. Even though I have not worked with everyone, I am

indebted with the quality of instruction offered to me. I especially would like to thank Dr. Li for

setting me on the track to success and for Dr. Kuhl for ensuring my completion.

Next, I would like to thank the students, faculty and staff at Midwestern Community

College for permitting me this opportunity to improve the quality of instruction at our institution.

Without the explicit support of Carrie McCammon, my department chair, Rae Lynn Prouse,

Assistant Registrar, Darla Crist, Writing Center Director, and the students involved with this

study, this project would not be possible.

While no funding originated from my employer or University of West Florida for this

research endeavor, I would like to extend my gratitude to both higher education entities for their

continued support. I appreciate Midwestern Community College’s tuition assistance and the

staff in the Human Resources Office for supporting me in my educational pursuits. The

investigator also thanks the University of West Florida’s Department of Mathematics and

Statistics for sharing their scholarship funding with me.

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Finally, I wish to recognize my family. I am forever grateful for the unconditional

support my family showed me, especially my wife, Tiffany. She made an untold number of

sacrifices in the name of my success. Without her I could not have finished my Master’s degree.

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TABLE OF CONTENTS

Page

TITLE PAGE ....................................................................................................................... i

APPROVAL PAGE ............................................................................................................ ii

ABSTRACT ....................................................................................................................... iii

DISCLOSURES ................................................................................................................. iv

ACKNOWLEDGEMENTS .................................................................................................v

TABLE OF CONTENTS .................................................................................................. vii

CHAPTER I. INTRODUCTION .........................................................................................1

A. Statement of Problem ..........................................................................................1

B. Relevance of Problem .........................................................................................2

C. Literature Review ................................................................................................3

1. Relational versus Instrumental Understanding ...............................................3

2. Ohio University’s Remote Learning Experiment ...........................................3

3. Revamping Virginia Tech’s Mathematics Curriculum ...................................5

4. Tennessee Board of Regents’ Developmental Education Transformation .....6

5. National Redesign Efforts ...............................................................................7

D. Limitations ..........................................................................................................8

CHAPTER II. INSTRUCTIONAL MODEL ....................................................................10

A. Assumptions of the Model ................................................................................10

B. Student Performance Assessment Methodology ...............................................12

C. Description of Statistical Tests ..........................................................................15

1. Mann-Whitney U ..........................................................................................15

2. Chi-Square ....................................................................................................16

3. Pearson Correlations .....................................................................................16

4. Linear Regression .........................................................................................17

D. Statistical Testing and Analysis ........................................................................18

CHAPTER III. CONCLUSIONS ......................................................................................23

A. Summary: Interpretation ...................................................................................23

B. Suggestions for Further Study .......................................................................... 24

REFERENCES ..................................................................................................................27

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APPENDICES ...................................................................................................................30

A. IRB Approval ....................................................................................................30

B. Syllabi................................................................................................................32

C. Attitude Survey Data .........................................................................................58

D. SPSS Output ......................................................................................................64

1

Chapter I-INTRODUCTION

A. Statement of Problem

The community college used in this study is one of the largest college systems in the Midwest with a total

system-wide headcount of 174,806 for the 2010-2011 school year [10, 19]. In an era where more college graduates are

needed, the state government applies great pressure upon Midwestern Community College, and other institutions of

higher learning, to produce more highly qualified graduates in a timely manner by tying a growing portion of the

college’s funding to persistence, degree completion and remediation completion rates [20]. In response to this

demand, the college administration identified several bottleneck factors preventing students from graduating and

persisting: poor success rates in remedial and general education mathematics courses were an immense culprit in this

regard [1].

The author’s home mathematics department faced an extraordinarily high failure rate in remedial and general

education mathematics courses, 56.1% and 41.2% respectively, for the 2010-2011 academic year1. This was not a new

problem; the department routinely faced high failure rates for several semesters prior to the 2010-2011 academic

year. Traditionally, students who need remediation complete a semester (or more) of remedial mathematics course

work, followed by college-level mathematics. A majority of the college’s students need one general education

mathematics course that focuses more on common usages of mathematics and less on the algebra called “Concepts in

Mathematics.” The percent of students who satisfactorily completed the “Concepts in Mathematics” course, much less

completed the remedial mathematics course(s) prior to this general education course, needs significant improvement

because only 54%1 of students passed in the Fall 2011 semester. Of those students that started in remedial

mathematics, only about 6%1 passed a college-level mathematics course. To remedy this problem, the department

decided to adopt the co-requisite enrollment model, which takes students who would otherwise not be eligible to

1 Figures calculated by investigator using data archived by mathematics department. This only includes Fall 2010 and Spring 2011

semesters.

2

attempt the general education math course and concurrently enrolls them into a tutorial class. The reasons the

mathematics department selected the co-requisite model are:

The co-requisite model condenses all of the math requirements into one semester instead of multiple

semesters.

Students receive assistance just-in-time. In addition, the faculty did not try to fill in all of the gaps of

the students’ knowledge base, just what was needed to be successful in the general education level

mathematics course.

This approach to remedial mathematics is in stark contrast to the status quo of remediating students before they

would be permitted to enroll in the mathematics course needed for their degree. The main research question is

whether or not concurrent enrollment in remediation has a statistically significant impact on student success in the

general education level math?

B. Relevance of Problem

Mathematics is often thought of as a “gatekeeper” course: a course that prevents students from completing

their degree. Academic programs with high student interest and demanding academic rigor often require rigorous

mathematics courses as filters for students who want to enter into their programs but cannot handle the demands of

those programs: “‘Remedial math has become the largest single barrier to student advancement’“ [12]. Mathematics

courses also act as an unintentional barrier for students who need at least one mathematics course to graduate.

According to Complete College America, 46.4% of incoming students at Midwestern Community College need

remediation in mathematics. Of those students who enroll in at least one remediation class, only 63.7% complete the

remediation program and 9.2% of those that complete remediation graduate with an associate’s degree within 3 years

[3].

Students enrolled in any remedial course (reading, writing or mathematics) must earn a C or better in order to

move onto the next course per college policy. Statewide, the college’s success rates in remedial mathematics are

3

dismal at best. In the 2010-2011 academic year, 52.6% of all students enrolled in a remedial mathematics course

passed in contrast to the target of 58%. The pass rate only improved two percentage points for the 2011-2012

academic year but did not keep up with the targeted pass rate of 62% [11]. The poor pass rates are frustrating the

college’s efforts to increase graduation and persistence rates as one of the pre-requisites to other non-math intensive

classes is that students completed the remedial math sequence (or tested out of remedial mathematics).

C. Literature Review

1. Relational versus Instrumental Understanding.

Before any discussion on successful instructional methods begins, types and degrees of understanding in

mathematics need to be discerned. The most commonly accepted “types” of understanding in mathematics are

“relational understanding” and “instrumental understanding” as advocated by Skemp [18]. Relational understanding

encompasses comprehension in both the how and why in mathematical phenomenon, whereas instrumental is just the

how; thus, the student with instrumental understanding is being used like an apparatus in a larger process that can be

easily replaced. Society and educators must be careful not to mismatch the instrumental educator with the student

who yearns for relational understanding and vice versa; great time and resources have been and will be wasted

because of this mismatch [18]. Skemp’s article [18] is relevant to the larger scope of this literature review as it

establishes goals and guidelines to which a mathematics educator should strive to obtain: the relationally taught

student is the self-reliant and self-curious student who will perform better in mathematics classes presently and in the

future. Furthermore, the students who enter the co-requisite enrollment program are more likely to be instrumentally

driven. This group of students is ultimately only interested in the pre-requisite concepts and skills needed to be

successful in the college-level course and nothing more.

2. Ohio University’s Remote Learning Experiment.

One of the grandiose questions educational researchers hope to answer is “Is there one (or multiple)

methodologies that work best for certain subjects?” While there is no definitive answer as of yet, the educational

4

community is on track to answering the question “What are effective methodologies for any discipline?” Remote

learning (now often called distance learning or e-learning) was the subject of study at Ohio University for their remedial

mathematics class. Can remote learning be an effective and efficient tool to teach remedial mathematics?

Lopez, Permouth and Keck [16] studied three sections of Math 101 at Ohio University for a particular semester

and varied the attendance policies in each of the three sections where each section had only 20 students. The first

section had mandatory attendance policies. Students in the second section were required to attend at least two days a

week (one for testing and the other for lecture). The third section only stipulated students to attend one day a week

(for testing only). Normally, Math 101 meets three days a week plus an additional day for testing. This quasi-

experimental, repeated measures design kept all other factors constant across all sections: same assessments, same

lecture content, same deadlines, same grading scale. The hypotheses were freedom of choice would direct students

towards remote learning and attendance is positively correlated to class performance. The researchers failed to reject

both null hypotheses; however, they did affirm weaker students are better suited with classes with strict guidelines

and policies. Based upon their review of institutional data, both the remote and traditional sections were consistent

with the average scores on the final exams of years past [16].

While this study has internal validity concerns (testing, selection, small sample size), the conclusion finds that

no adverse conditions were found for students enrolled in the remote learning section. For those students who are

disciplined enough to move through a course with relatively little guidance or pushing from the instructor, they are to

generally do well in the online (or remote) environment. Unfortunately, most remedial students cannot handle such

freedom and responsibility on their own [16]. This conclusion is also supported by a similar study conducted by Li,

Uvah, Amin and Hemasinha [15]because their study showed the success rates for students in a purely online format for

College Algebra was significantly worse than those in face-to-face sections. They varied the instructional format

(purely online; face-to-face with instructional technology inclusion and face-to-face without any instructional

technology) of College Algebra and kept the other factors constant. Even though the Mathematics and Statistics

Department at the University of West Florida did not alter the attendance policies as Ohio University did, they note

5

“maturity and self-discipline” along with “ill-preparedness” are factors that contributed to the poor pass rates in the

purely online section of College Algebra [15].

3. Revamping Virginia Tech’s Mathematics Curriculum.

Virginia Tech was one of the first schools to redesign their mathematics curriculum in light of pathetic student

success rates and ever declining financial support from state government. Greenburg and Williams, mathematics

faculty at Virginia Tech, outline the development of their “Math Emporium” and the reasons for their high success

rates across the undergraduate curriculum. The Math Department at Virginia Tech obtained an abandoned

department store to house five hundred fifty computer work stations for students to complete their course activities

twenty-four hours a day, with instructional staff available fourteen hours a day. Most of the course activities can be

completed anywhere the student has internet access; however, instructional staff proctored all high-stakes

assessments at the Math Emporium. Students prepared for their high-stakes assessments by reading an online text or

watching videos uploaded to the Internet and completing online homework and quizzes. Any time students needed

assistance at the Math Emporium, they flagged down a near-by instructional staff member. The pool of questions used

for homework and quizzes was the same pool of algorithmic questions used for the high-stakes assessments. The

deliberate use of the same pool of questions for all course activities encouraged mastery learning; students knew

simply rehearsing solutions from previous assignments would not be satisfactory to passing the course [9].

The benefits to this approach are numerous, according to the authors. Virginia Tech students witnessed:

greater autonomy in completing course activities; enhanced time management skills; and, improved classroom

performance in future mathematics courses. The faculty and administration of Virginia Tech produced significant cost

savings; streamlined processes and resources; and, achieved economies-of-scale. They continuously search for new

ways to improve student success and lower the cost of instruction [9].

6

4. Tennessee Board of Regents’ Developmental Education Transformation.

The institutions of higher education in Tennessee faced a disproportionate amount of enrollment in

developmental education courses with low success rates. The Tennessee Board of Regents (TBR) received a grant to

develop new models of learning to improve retention rates and simultaneously reduce the instructional costs such that

the models were replicable and scalable across the curriculum. Berryman and Short [2], members of the Tennessee

Board of Regents, oversaw the transformation in developmental mathematics courses, although the grant was aimed

at improving all aspects of developmental education.

Jackson State Community College created their own textbook, assignments and assessments using an online

homework management system in an emporium style similar to Virginia Tech’s model. Instead of starting at the very

beginning of a course, a student starts and stops based on what competencies that student’s major department deems

appropriate and the student’s mathematics scores on the placement assessment. Only 18% of the academic programs

at Jackson State required all competencies to be met in order to be successful in college-level coursework [2].

Cleveland State Community College used a similar design of creating competencies and only requiring students to

master the competencies needed for his or her academic program; however, the faculty at Cleveland State created

their own video lectures that were inserted into the online homework management system. This freed the faculty to

spend more one-on-one time with each student and to teach more than their previous norm of five sections [2].

The benefits of these two redesigns are notable. Jackson State experienced a twenty percent decrease in the

cost-per-student ratio, from $177 to $141. Cleveland State’s reduction in overall instructional costs saved the

institution $51,000 (19% reduction in instructional costs). More impressive is Cleveland State’s increase in the success

rate from 54% to 72% [2]. The most impressive statistic is when Cleveland State compared the students who entered

college-level mathematics using the traditional lecture model versus the emporium model. The math faculty found

that “33% more students passed the next college-level math course after having completed the redesigned

developmental math course when compared with students who went through the traditional approach to

remediation” [2].

7

Another notable Tennessee redesign originates from Austin Peay State University. Instead of requiring

students to advance through a remedial course sequence and then onto their college level mathematics course(s),

students who qualified for remedial classes are enrolled into the college level mathematics course and a concurrent

“linked workshop” where a successful mathematics student who attended the same section as the workshop students

would each workshop students pre-requisite skills, provide peer tutoring to all workshop students and review for tests.

All of this extra scaffolding occurs in the background of the college level course as the professor would progress with

the course as s/he normally would. Of those students who qualified for remediation, the success rates improved for

the Elements of Statistics class from 23% to 54% and a more dramatic improvement in their liberal arts survey course

from 33% to 71% [4].

5. National Redesign Efforts.

The need to redesign remedial courses is not unique to any particular state. The mission of The National Center

for Academic Transformation (NCAT) is to “improve student learning outcomes and reduce the cost of higher

education” using information technology. NCAT works with institutions to achieve this lofty mission by researching ,

giving access to research-based solutions and increasing access to and employing institutional assets more efficiently

[21]. Institutions are asked to “re-conceive” entire courses, not just select sections, to meet the objectives set forth in

NCAT’s mission statement. C. Twigg, the executive director of NCAT, developed four core principles NCAT lives by:

students spend most of their time “doing math problems” and not listening or watching someone else do them; the

amount of time spent on a type of problem is inversely proportional to the perceived level of difficultly; on-demand

assistance is provided to students when needed; and, doing math is obligatory [22].

Twigg [22] continues her discussion of redesign by highlighting four-year and two-year institutions successes

and lessons learned. Each institution modified the NCAT template to meet their unique needs. The common threads

between all of these institutions are the “Five Principles of Successful Course Redesign.” The entire course, from top to

bottom, must be deconstructed, critiqued and reassembled with new curriculum as needed. The focus of the course is

on the student’s learning; therefore, active learning is a necessity. Students cannot learn completely on their own, that

8

is why the instructional staff exists in the first place! Individualized assistance must be provided on-demand with

ongoing feedback from the instructional staff and the computer software. Finally, student successes and frustration

must be tracked to ensure student mastery [22].

D. Limitations

Several limitations exist within this study that impedes upon the generalization of its results. First, students

were hand-selected. The initial criteria used for enrollment into this program were:

The student’s major only required this particular general education math course

The student could devote a large portion of their time to studying math.

By nature of the scope and method of sample selection, the results of this study are not generalizable to the

other campuses of Midwestern Community College, much less any other institution of higher education. All of the

students used in this study called the author’s home campus their primary campus. Another factor for limiting the

generality of the results is the uniqueness of the tutorial class. No other campus of Midwestern Community College

taught the tutorial class in the same manner as the author did.

Another limitation to this study is the small sample size and the drop-out effect. The program started with a

total of thirteen enrolled students and ended with a total of ten enrolled students. The successes demonstrated by

this program should be taken with a grain of salt because of the limited pool of students used for this program. The

program experienced a large withdraw and failure to withdraw (FW) rate, in part due to the small number of enrolled

students and the frequency personal emergencies interrupted students’ coursework. Three students experienced life-

changing familial issues and two students gained or lost a job that directly impacted their studies. Maturity of students

should also be taken into consideration as Li, Uvah, Amin and Hemasinha [15] noted in their study of College Algebra

students.

9

The final major limitation to this study was the frequent absence of the general education mathematics course

instructor, the department chair. She attended several meetings that conflicted with her class schedule; often the

meetings were not previously made known to her at the beginning of the semester as they should have been. While

she did provide students with out-of-class assignments when absent, that instructional time can never be made up. In

the instructor’s defense, she did offer optional review sessions on Fridays so her students could receive personalized

assistance from her.

10

CHAPTER II-INSTRUCTIONAL MODEL

A. Assumptions of Instructional Model

The intent of the design of the program was to provide “just-in-time” assistance as in the cases of Virginia Tech

and Jacksonville State with the clear expectations of attendance and participation of Ohio University to enable

students to successfully pass their general education level mathematics class. To that end, the author and the

department chair expected students to attend and participate in both the tutorial class and the general education

math course on a regular basis. Even though students knew they would not directly receive credit towards their final

course grade because of attending either class, they were expected to come to class nevertheless. If a student missed

two consecutive class meetings of either the tutorial or the “Concepts in Mathematics” class, they received a phone

call or email alerting them to the instructor’s concerns using an online retention system. Both instructors could see

when either of them raised attendance or academic concerns on this system.

All students in the “Concepts in Mathematics” course were informed of and highly encouraged to utilize the

college’s tutoring services. It is not a course requirement of the college level course that students attend tutoring

sessions; students in the tutorial class, on the other hand, were expected to use a personal tutor on a regular basis. A

tutor from the tutoring center was set aside specifically for the students in the department chair’s “Concepts in

Mathematics” classes at specific times during the week and by appointment. Even though students from the tutorial

class were expected to obtain a tutor, the tutorial instructor could not award course credit for attending tutoring

sessions because of the tutoring center’s reluctance to release the names of students who were using their services.

The department chair and the author devised a pacing guide describing when topics would be taught in the

college-level course and what skills and concepts should be reviewed/taught in the tutorial class. Each class meeting

was seventy-five minutes in duration. Unlike the Austin Peay’s “Linked Workshop” model, this tutorial class had its

own homework assignments and a full-time instructor leading the class. The lecture component of the tutorial class

11

was kept to a maximum of twenty minutes. The remainder of the class time was used to answer student questions and

to work on their assignments from both the college level course and the tutorial course.

COMMENT: Students could not complete all of their work for one class during the tutorial class time alone.

This is why the department expects all students, regardless if they enrolled in remedial coursework or college level

coursework, to study at least three hours a week for each credit hour they were enrolled in outside of class. The

tutorial class and the “Concepts in Mathematics” class were three credits each, thus giving each student a total of

eighteen hours to spend outside of class studying. The department considers a student to be studying when they are

actively working with mathematics. This can come in many forms: working with a tutor or classmate, completing

homework, reading or watching online multimedia, etc.

So long as students were willing to invest the necessary time to study, it is the Math Department’s assumption

that any student who was willing to spend the requisite study time and utilize the College’s support structures could

pass the “Concepts in Mathematics” course. Both instructors maintained at least eight student office hours per week

and scheduled appointments outside of their normal office hours when necessary. Walk-in tutoring, in addition to

tutoring by appointment, was readily available to all students. The online homework system provided videos and other

learning assistance when the student needed them. The author stayed in close contact with the other instructor

throughout the semester, communicating student concerns and what topics should be reviewed or retaught in the

“Concepts in Mathematics” course. The bottom line is that both instructors were willing to, in the words of the

department chair, “bend over backwards” to be of assistance to the students.

Summary

Remedial students can succeed in the “Concepts in Mathematics” class if they regularly attend the tutorial

class where they receive “just-in-time” assistance.

All students can be successful if they take advantage of the tutoring and instructors’ office hours.

Students were assigned homework and assessments in both classes.

12

The two instructors kept in close contact about student concerns and made themselves widely available for

student interaction outside of class.

B. Student Performance Assessment Methodology

The “Concepts in Mathematics” course has four main themes: probability, statistics, algebra and personal

finance, taught in this order. The reason for this particular ordering of topics was to give the tutorial students more

time to develop their algebra concepts and skills. All students in this general education mathematics class never

worked with Venn diagrams, counting rules, probability and statistics before, so this was a good place for all students

to start, especially since algebra was not a pre-requisite skill. The “Concepts in Mathematics” class contains four one

hundred point paper-pencil unit exams, regular online homework, and two online quizzes per unit, in-class

participation and a paper-pencil multiple choice final exam for a total of 600 points possible. The unit tests were taken

in-class whenever possible; otherwise, the tests were placed in the Testing Center where students were given a five

day window to complete the test. The class was given one bonus opportunity: if a student scored better on the final

exam than on the test with the lowest test score, the percentage the student earned on the final replaced the lowest

test score. Possible final course grades are A, B, C, D and F where the standard grading scale was used to calculate the

minimum number of points needed to earn a specific grade.

The department chair taught two sections of the general education math courses associated with this program;

the author taught the tutorial class. Students from the tutorial class enrolled in one of the department chair’s sections

of “Concepts in Mathematics” that met either before or after the tutorial class. The author coordinated with the other

instructor on a regular basis to address student and course concerns. If either instructor needed to modify what would

occur the next week, the circumstances and reasons were discussed.

The intent of the tutorial class structure was to give students the support they needed to be successful in their

college-level math course. To achieve that effect, the author designed the tutorial class so that students could express

their concerns at the beginning of class, then focus on the skills and concepts needed in the near future. The author

started class off by soliciting the students’ questions. Then, the author spent about twenty minutes discussing a skill or

13

concept the students needed in class in the near future; questions were entertained during this brief lecture period as

well. Finally, students were expected to complete homework and quizzes for the tutorial class using an online

homework management system. 200 points were possible for each unit test and 200 participation points for a total of

1200 points possible for the tutorial class. The standard grading scale was used as well; however, the grade students

earned in their tutorial class did not impact their grade point average but did impact their completion rate as

calculated by the Office of Financial Aid.

The homework and quiz structure inside of the tutorial class online homework system attempted to build upon

the students’ previous knowledge so they could focus on their deficiencies. Each unit had a proctored Pre-Test that all

students were required to attempt. The Pre-Test served one major purpose: to diagnose students’ strengths and

weaknesses. If a student scored 90% or better on the Pre-Test, then the student was excused from completing that

unit. A secondary purpose of the Pre-Test was to customize the students’ homework. If a student demonstrated

mastery of one particular topic on the Pre-Test, then they were excused from completing that type of problem on the

associated homework assignment(s). After completing the Pre-Test, students watched and engaged with the

multimedia. Students chose which multimedia activities they completed so long as they completed at least 70% of

each multimedia assignment. Next, the student would continue onto the homework for that unit. After completing all

of the homework and multimedia pairs in a unit, students could take the Practice Test for that Unit after the minimum

grade of 80% was earned on the homework assignments for that unit. The goal of the Practice Test was to prepare

students for their actual test, a required test review guide in another sense. The Practice Test simulated testing

conditions; students were allotted 90 minutes to complete the Practice Test and could not use notes (although the

Practice Test was not proctored). Finally, students attempted the Post-Test. Students were given 90 minutes to

complete the exam in the Testing Center using a calculator and scratch paper. The Pre-Test and Post-Test could only

be taken once; the multimedia and homework could be stopped and started as the student saw fit. The Final Exam for

the tutorial class consisted of students retaking the COMPASS Placement Exam. If a student completed all of their

units, the final exam was optional. If a student did not complete all of their units, the student was required to take the

14

exam. In either case, if the percent earned on the Final Exam was better than the student’s lowest scoring Post-Test

score, then the score earned on the Final Exam would replace that particular Post-Test score. The syllabi for both the

“Concepts in Mathematics” class and the tutorial class are included in the appendices.

There are a few more intricate details that distinguish this program from others similar to it. First, students

enrolled in this special program were given two scheduling options. They could go to the “Concepts in Mathematics”

course, then the tutorial class; or, students could attend the tutorial class first. Another important factor is regardless

of a student’s choice of which schedule he or she chose, a tutor was dedicated to this program. The tutor, a

mathematics major from a neighboring four-year institution, set aside time each week to work with students on a

walk-in basis. She was also available for appointments as well. Should a student not be able to work with this

particular tutor, the instructor and the author encouraged students to work with any other college tutor or to attend

an instructor’s review session. The author offered review sessions on demand if a student scheduled an appointment

in advance or during office hours. The hallmark portion of this design is that students learned the skills and concepts

necessary to be successful in the general-education level mathematics class when they needed it. This feature permits

students to focus on just the aspects of mathematics that are necessary to be successful in the general education math

class and not spend time on other topics not necessary to complete the “Concepts in Mathematics” course.

Summary

“Concepts in Mathematics” class covers probability, statistics, algebra and personal finance. The class’s major

components feature: Four unit tests, online homework and quizzes, in-class participation and a multiple choice

final exam. Standard grading scale used for all assessments and for the final course grade.

The tutorial class’s purpose was to prepare students for their “Concepts in Mathematics” coursework. The

class featured question and answer time, brief lecture and time to work on tutorial homework or “Concepts in

Mathematics” homework.

The tutorial class contained five online unit tests and in-class participation. If students scored high enough on

the Pre-Test, they were excused from completing the remaining assignments for that unit. The standard

15

grading scale was also used for this class; however, the grade students earned in this class did not impact their

GPA.

C. Description of Statistical Tests

1. Mann-Whitney U

The Mann-Whitney U (sometimes referred to as the Wilcoxon-Mann-Whitney Test, WMW abbreviated) takes

the sample space under study and partitions it into two groups, say H (the control) and K (the experimental group) [6].

The Mann-Whitney U investigates if the distribution of H is identical to K by converting data into ranks while

maintaining group membership [17]. Larsen and Marx [14] stipulate that the probability density functions (pdfs) and

standard deviations of the two groups being compared must be the same in order to use the Mann-Whitney U Test.

The null hypothesis is and the alternative hypothesis is . Suppose two independent random

samples of sizes n and m are obtained from probability density functions , respectively. Combine the

samples together and rank the observations; note that is the rank of the i th observation. In the event of a tie,

average the ranks they would have otherwise received, if different. Now an indicator variable, , is introduced, where

if the i th observation originates from and 0 else wise. The test statistic is then defined as

∑ . The null hypothesis is rejected if where is the critical value for the WMW U distribution

[14]. This test determines if there is any gap between the two distributions; the larger the sum of the ranks, the larger

the shift between .

Why not use the t-test instead of the Mann-Whitney U Test when comparing two groups? According to Fay

and Proschan [6], the WMW test should be used for very skewed distributions and if there exists a “small possibility of

gross errors in the data” [6]. Since the author cannot validate the accuracy of all the data used in this study, a

conservative approach was applied. Furthermore, WMW better discriminates outliers than t-tests do. This is due to

the Mann-Whitney U’s high asymptotic relative efficiency (ARE) compared against the Student t-test under non-normal

populations [17].

16

2. Chi-Square

The Chi-Square statistic is calculated by ∑

where is the observed frequency of the ith category

and

is the expected value of the ith category with being the row and column totals respectively [7].

Gingrich spells out the primary assumptions of the Chi-Square Test to be and each observation is independent

of one another [7]. The null hypothesis is no association between the two variables under study; the alternative

hypothesis states there exists an association between the two variables. The null hypothesis should be rejected if

where r and c represent the number of rows and columns present in the contingency table,

respectively [14].

3. Pearson Correlations

Correlations provide researchers with a “dimensionless measure of dependency so that one relationship can be

compared to another” with relative ease [14]. In general, this is accomplished by setting:

√ . Correlations exhibit the property | | [14]. When the moments are replaced by their

respective estimators, we arrive at the Pearson Correlation Coefficient. The Pearson correlation coefficient is given by

√ where ∑ ∑

∑ ∑

∑

∑ ∑

[8]. The null hypothesis stipulates no population correlation exists ; the alternative

hypothesis states there is a population correlation .

If any relationship exists between two variables, correlations strive to demonstrate the direction, form and

strength of the relationship. Correlations do not imply causation; they simply assert the (non)existence of a

relationship between two variables. Additionally, correlations cannot be generalized beyond the scope of these

students under study. Finally, the most useful aspect of correlations is the coefficient of determination; this statistic

17

measures the variability of the first variable as explained by the second variable [8]. Outliers can dramatically affect

correlations; therefore, the zero scores were removed from the data set before the correlations were calculated.

4. Linear Regression

Larsen and Marx [14] point out four important assumptions for the linear regression model. First, | , the

pdf of Y for a given x, is normal for all x. Second, the standard deviation for | is the same for all x. Third,

| . Finally, all of the distributions are independent. Given the points adhere to

the simple linear model, | , the maximum likelihood estimators are given by [14]:

∑

∑

∑

(∑ ) (∑

)

∑( )

In order to discern if the linear regression model itself as a whole is significant, the F ratio of MSR to MSE is

constructed. This number is then compared to its critical value where

[13]. If the

regression model survives this first step, then the coefficients of the regression model are tested. To test the

coefficients of a given regression model for significance, the null hypothesis is pitted against the

alternative hypothesis . Using the same data to form the linear regression model, we use the given t

statistic to determine if the null hypothesis should be rejected. should be rejected if | | . The hypothesis

test for is similar to that of [14].

√∑

⁄

18

D. Statistical Testing and Analysis

For all testing and analysis, the author set alpha to be 0.05 and used SPSS Version 20 for all statistical testing.

When comparing factors outside the scope of the “Concepts of Mathematics” class, the author used Mann-Whitney U

Tests to study the two groups. Table 1 shows the only statistically distinguishable difference between those students

in the tutorial class versus those not enrolled in the tutorial class is the COMPASS algebra placement test score

(COMPASSAlg). This is reasonable because the COMPASS Algebra score prevented tutorial students from registering

for this general education mathematics class by itself instead of through this special program, thus highlighting the

primary distinction between the two groups. Tables 2 and 3 make the same comparisons as in Table 1 except using the

course section and students’ gender as the grouping variable, respectively. Tables 2 and 3 do not show any significant

difference except on the COMPASS Algebra score when students are grouped by gender. Since student ages were not

factored into this study, it is impossible to distinguish those recent high school graduates from those with previous life

experience in between high school and college. Tables 4-9 examine mean differences among components of the actual

course, grouped by tutorial class, section and gender with zero scores included in Tables 4-6 and zero scores excluded

in Tables 7-9. When the zero scores were factored out of the analysis, none of the assessments, regardless of how the

data was grouped, exhibited any significant differences. The Pre-Test tells another story altogether.

Because the author had no control over the content of the unit tests, a pre-test and post-test assessment

instrument was implemented to provide more depth to this study. All students enrolled in both sections of the

“Concepts in Mathematics” class completed the formative assessment on Midwestern Community College’s learning

management software featuring question types students would encounter during the class. The Pre-Test and Post-Test

were identical up to changes in the problem’s values and scrambled question order. The Pre-Test was administered

during Week 2 of the semester and the Post-Test was administered during Week 15. Students were given the entire

week to complete the assessment wherever they had access to the internet. To no one’s surprise, the tutorial students

scored lower overall than those not in the tutorial class on the pre-test; however, the post-test comparisons resulted in

19

no significant difference between the two groups. The Pre-Test and Post-Test were compared in a similar manner as

the unit tests.

A commonly accepted notion among the educational community is attendance is strongly related to classroom

performance. The Chi-Square Test for Independence was used to test this commonly held relationship. Attendance

rate was grouped into three categories: High (80%-100%), Average (60%-79%) and Low (0%-59%). Grades were

grouped according to the standard grading scale. The attendance rate was categorized in this manner as the mean

attendance rate was approximately 80% with a standard deviation of 20 points. Chi-Square Tests for Independence as

summarized in Tables 10-21 show attendance rate is independent of the scores students receive on all of the formal

high-stakes assessments when analyzed as a whole and by tutorial enrollment with the exception of Test 4. It is not

too surprising then to see that attendance and the final course grade are not related either.

Next, Pearson correlations were computed as a spring board for investigating additional relationships. Tables

22-25 show correlations tutorial and non-tutorial student data analyzed together and separately based on enrollment

in the tutorial class. Significant correlations are starred with one asterisk or two asterisks, 0.05 or 0.01 alpha levels

respectfully. Only notable correlations will be discussed herein. The author encountered sales personnel from ACT

proclaiming the strong connection between placement test scores and success in college level mathematics. The

COMPASS Algebra score was not significantly relatable to the final course grade. In the Chi-Square Testing for

Independence, it was noted attendance and the course grade were independent of each other; however, the

correlations suggest a significant relationship between attendance and the final course grade exclusive for the tutorial

students. Finally, the Problem and Activities Average (ProbActAvg) variable exhibited strong and significant

correlations for all formal assessments for non-tutorial students whereas ProbActAvg was not related to Test 2 for the

tutorial students.

When comparing unit test scores, final exam scores and the final course grade between those enrolled in the

tutorial class and those not enrolled in the tutorial class but in the same general education course as the tutorial

students, the normality or homogeneity assumptions of the t-tests were often violated. So, the Mann-Whitney U

20

Independent Samples Test was used because it is robust when the normality and homogeneity assumptions are not

upheld [1]. Each unit test administration produced scores of zero; therefore, each possible situation was tested: with

and without the zero scores. Tables 26-49 show the results of the Mann-Whitney U Independent Samples Test with

the test scores of zeros included and excluded, appropriately marked grouped by tutorial enrollment, section and

gender. The questions that were significantly different in the tables including test scores of zero were the same as the

tables excluding the test scores of zero. Unit Tests 2 and 3 only exhibited one question that was significantly different

between the three groups. Unit Test 4 questions did not exhibit any differences between those enrolled in the tutorial

class and those not enrolled in the tutorial class. The unit tests were departmentalized across all sections, not just

those taught by the department chair.

Finally, Midwestern College’s administrators and the author wanted to know which factors could be used to

predict the final course grade. The remaining SPSS tables show the construction of first order regression models and

their tests for significance. Ten regression models were constructed in hopes to find the best fitting model and most

practical model. The table below summarizes the models when results from tutorial students and non-tutorial

students are analyzed together.

Model Target Input Variables Significant?

1 CourseGrade NumRemedial,

NumAttempts,

NumCredits,

GPACUMFall2011

Yes 0.163

2 CourseGrade Test1 and Final Yes 0.959

3 CourseGrade Test1 Yes 0.538

4 CourseGrade Test2 Yes 0.230

5 CourseGrade Test3 Yes 0.634

21

6 CourseGrade Test4 Yes 0.697

7 CourseGrade Final Yes 0.932

8 CourseGrade COMPASSAlg No N/A

9 CourseGrade PercentPresent No N/A

10 CourseGrade Num118Attempts,

GPACUMFall2011

No N/A

Model 1:

Model 2:

Model 3:

Model 4:

Model 5:

Model 6:

Model 7:

Last, but not least, is a summary of the pass rates. Midwestern Community College policy states that any student who

does not attend the “last academic event” (which in this case is the final exam for this class) automatically fails the

class, regardless of their previous work and score in the class. With this policy in mind, Tables 69-72 show the

distribution of final course grades by tutorial class enrollment and the inclusion or exclusion of those students who did

not attempt the final exam. Midwestern Community College considers a “D” or better to be passing for most academic

programs; however, a grade of “C” or better is needed if the student intends to transfer the class to another institution

of higher learning. For the purposes of this analysis, the investigator will consider passing to be a grade of “D” or

22

better as that is what Midwestern Community College’s success rate is measured against. When zero scores are

excluded, the pass rate of those in the tutorial class is 83.3% versus those not in the tutorial class of 78.1%. When the

zero scores are incorporated into the analysis, the pass rates are 50% and 69.44%, respectively. Several of the tutorial

students experienced “life events” that dramatically impacted their ability to perform well in class, namely

transportation, family medical emergencies and employment status changes. These reasons were verified by the

author with documentation, when possible.

The investigator created and analyzed attitude surveys for students enrolled in the tutorial class and for those

not enrolled in the tutorial class. The survey questions along with survey results can be found in the appendix.

23

CHAPTER III-CONCLUSION

A. Summary: Interpretations

Students who do not meet the stated pre-requisites for general education mathematics can be successful in

the college level coursework with the proper support structures in place. When comparing the overall unit test mean

scores of those in the tutorial class to those not in the tutorial class, there was no significant difference! Despite the

fact that a small quantity of questions from each unit test were significantly different between the tutorial and non-

tutorial students, the two sections and genders, the overall unit tests were indistinguishable between the groups. This

result is equivalent to saying students who did not meet the pre-requisites are on the same equal footing as those who

have satisfied the proper pre-requisites prior to enrollment.

Proper support structures are necessary for student success, especially for the tutorial students as evidenced

by the strong correlation between the Problems and Activities Average (which can only be completed in-class) and Unit

Tests 1, 3, 4 and the Final Exam for tutorial students. Students need to see how the mathematics taught in-class

applies to their homework and life. Unfortunately, regardless of the quality of support structures in place, students,

especially tutorial students, still must participate during class time to gain any benefits. It is not enough just to show

up to class as demonstrated by the chi-square independence tests comparing percent present versus each high-stakes

assessment. The nice aspect of this design is a student’s gender and section does not significantly impact the final

course grade.

When attempting to predict a student’s final course grade, Model 2 provides the most complete picture;

however, its fruitfulness in prediction is minimal as the final exam is the last assessment given to students before the

end of the semester. With timeliness in mind, Model 3 is the best of the group; while its value is less than stellar,

it is an early indicator of student success. If students do not perform well on the first test, they can still recover as

“Concepts in Mathematics” allows for the Final Exam score to replace the lowest test score. Models 8 and 9 are in line

24

with the previous results of this analysis: attendance alone and the placement test score do not accurately predict or

correlate to student success in this course.

Not enough students sought out the college’s free tutoring services offered to them to include into this

analysis. From personal conversations with the former director of tutoring at Midwestern College, students that

regularly attend one-on-one peer tutoring sessions earn, on average, at least one half a letter grade higher than those

that do not attend tutoring [1]. The question that naturally arises from this conclusion is why is a pre-requisite needed

for the course if the co-requisite model is successful?

B. Suggestions for Further Study

Naturally, one easy extension of this study would be to expand the population under study, thereby reducing

or eliminating the size of the study limitation this study posed. The author’s employer is currently expanding the

breadth and depth of the co-requisite model by offering more sections and investigating professional development

opportunities for more adjunct faculty to become qualified to teach the “Concepts in Mathematics” course. Academic

advisors recruit students for this program if “Concepts in Mathematics” is the appropriate course for their degree,

regardless of their prior academic background. Future studies should examine the differences in success rates and

factors that influence student success such as instructors’ pedagogical backgrounds, number of qualified tutors

employed by the college, the average amount of time spent on mathematics coursework outside of class, amount of

time spent on other coursework, number of credits students are enrolled in that given semester, number of years since

each student completed their high school degree/GED, number of hours spent working for income, how many

dependents the student is responsible for; the students’ socioeconomic status as determined by Pell grant eligibility,

employment status of the instructor with the college (~76% of our mathematics faculty are adjunct instructors),

frequency their mathematics instructor misses class meetings, and, the frequency student meets with the instructor(s)

outside of class. For this study, students who did not need or otherwise qualified for the tutorial class enrolled in the

100 level mathematics course alongside tutorial students. What if all the students in the general education level

mathematics class were tutorial students enrolled in the co-requisite program? What if the co-requisite program

25

expanded to all freshman and sophomore level classes, regardless of pre-requisite requirements of the course? If this

is to be the case, why would Midwestern Community College need a placement test (The College has an open

admissions policy with a high school diploma or acceptable GED scores requirement for admission.).

Personalized tutoring is known to be a significant factor in improving success rates in mathematics coursework

[22]. What would happen to student success rates if students enrolled in the tutorial class were required to attend at

least one hour of one-on-one tutoring per credit hour of instruction? Students might object to this proposal given their

busy schedules, rightfully so if the instructor dictated the tutoring must occur on campus. Pearson, a vendor of online

learning, started offering one-on-one online tutoring twenty-four hours a day, seven days a week to be accessed when

and where the student is ready. So long as verification of tutoring can be provided, this might be a feasible option.

There are several logistical and financial problems associated with that question; so, a more realistic research question

to propose would be what would happen if all students enrolled in the general education math class were required to

spend a pre-determined number of hours in the Math Center (a place where students can quietly work on math

homework and ask for help from tutors as needed) each week as a part of their grade? Students “do not do optional”

and simply making college resources available to students in the past has not been a successful motivator to utilize

them [21, 22]. Midwest Community College’s remedial mathematics program began requiring students to visit the

Math Center in the Fall 2012 semester as a part of their course grade. The math department witnessed some

improvement in the overall pass rates in remedial coursework; however, other significant structural changes occurred

with the remedial coursework that prevent definite correlation of required time in the Math Center and success rates.

Students enrolled in the revamped remedial coursework certainly appreciated the Math Center and its tutors2.

The college, on a statewide level, gradually replaced the COMPASS Placement Test (produced by ACT) with the

ACCUPLACER Placement Test (produced by the College Board) starting October 2012. How does this change of

placement affect enrollment in the general education math class? At some specified point in the future, the

2 The author created and administered an attitude survey for our emporium style remedial classes. One of the questions asked

about the students’ experience in the Math Center (open computer lab with tutoring).

26

ACCUPLACER Placement Test itself will be customized to fit the needs of the college. How will the customizations

affect student placement and student success versus the “off-the-shelf” version currently employed?

27

REFERENCES

[1] Baker, L. (October 2010). Indiana Association of Developmental Educators Annual Conference. Indianapolis,

Indiana.

[2] Berryman, T. and Short, P. (2010). Leading developmental education redesign to increase student success and

reduce costs. Enrollment Management Journal: Student Access, Finance, and Success in Higher Education, 4(4),

106-114. Retrieved from Indiana State University’s Interlibrary Loan Service.

[3] Complete College America. Indiana remediation report. Retrieved from

<http://www.completecollege.org/docs/Indiana_remediation.pdf>.

[4] Complete College America. Transform Remediation: The-Co-Requisite Model. Retrieved from

<http://www.completecollege.org/docs/CCA%20Co-Req%20Model%20-

%20Transform%20Remediation%20for%20Chicago%20final(1).pdf>.

[5] Dancey, C and Dancey J. Statistics Without Maths for Psychology: Using SPSS for Windows. Page 548.

[6] Fay, M. and Proschan, M. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple

interpretations. Stat Surv. 2010 ; 4: 1–39. Retrieved from

<http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857732/pdf/nihms-185373.pdf>.

[7] Gingrich, P. Chi-Square Tests. University of Regina. Retrieved from <http://uregina.ca/~gingrich/ch10.pdf>.

[8] Gravetter, F. and Wallnau, L. (2009). Statistics for the Behavioral Sciences. 8th ed. Cengage: Belmont.

[9] Greenberg, W. and Williams, M. (2008). New pedagogical models for mathematics instruction. Proceedings from

Rockefeller Foundation’s Bellagio Conference, 361-371. Retrieved from Indiana State University’s Interlibrary

Loan Service.

28

[10] Ivy Tech Community College of Indiana. (2011a). Annual unduplicated headcount enrollment. Retrieved from

<http://ivytech.edu/institutional-research/enrollment/FINAL_10-11_headcount.pdf>.

[11] Ivy Tech Community College of Indiana. (2011b). Metrics & targets: accelerating greatness. Retrieved July 8,

2012 from <http://ivytech.edu/acceleratinggreatness/>.

[12] Jacobs, J. Community colleges consider math options. US News and World Report. Retrieved from

<http://www.usnews.com/education/best-colleges/articles/2012/01/27/community-colleges-consider-math-

options>.

[13] Kuter, M., Nachtsheim, C., Neter, J. and Li, W. (2005). Applied Linear Statistical Models. 5th ed. Boston: McGraw-

Hill.

[14] Larsen, R. and Marx, M. (2006). An Introduction to Mathematical Statistics and Its Applications. 4th ed. Upper

Saddle River: Pearson.

[15] Li, K., Uvah, J., Amin, R., Hemasinha, R.. A study of non-traditional instruction on qualitative reasoning and

problem solving in general studies mathematics courses. Journal of Mathematical Sciences and Mathematical

Education, March 2010, 37-49, 4(1). Retrieved from Dr. Uvah.

[16] Lopez, J., Permouth, S. and Keck, D. (2002). Implications of mediated instruction to remote-learning in

mathematics. American Educational Research Association. Retrieved from ERIC database.

[17] “Mann-Whitney U Test (Wilcoxon Rank-Sum Test).” Encyclopedia of Measurement and Statistics. Thousand Oaks:

Sage Publications, 2007. Credo Reference. 30 July 2010. Retrieved from

<https://login.ezproxy.lib.uwf.edu/login?url=http://www.credoreference.com.ezproxy.lib.uwf.edu/entry/sage

measure/mann_whitney_u_test_wilcoxon_rank_sum_test>.

[18] Skemp, R. Relational understanding and Instrumental understanding. Arithmetic Teacher, November 1978, 9-15.

Retrieved from Dr. Elizabeth Brown’s Middle School Mathematics Methods Class, Indiana State University.

29

[19] Soderlund, K. Ivy Tech grows to biggest state college. The Journal Gazette. Retrieved from

<http://www.journalgazette.net/apps/pbcs.dll/article?AID=/20081211/LOCAL04/812110306/1026/LOCAL04>.

[20] Stokes, K. The seven new benchmarks for funding Indiana colleges. National Public Radio. 9 December 2011

from<http://stateimpact.npr.org/indiana/2011/12/09/the-seven-new-benchmarks-for-funding-indiana-

colleges/>.

[21] The National Center for Academic Transformation. (2005). Who we are. Retrieved from

<http://thencat.org/whoweare.html>.

[22] Twigg, C. (2011). The math emporium: higher education’s silver bullet. Change: The Magazine Of Higher

Learning, May-June 2011. Retrieved from <http://www.changemag.org/Archives/Back%20Issues/2011/May-

June%202011/math-emporium-full.html>.

30

APPENDIX

A. IRB Approval

Mr. Ryan Grossman February 23, 2012

8000 South Education Drive

Terre Haute, IN 47802

Dear Mr. Grossman:

The Institutional Review Board (IRB) for Human Research Participants Protection has completed its

review of your proposal titled "The Effectiveness of Concurrent Enrollment in Remedial Mathematics

and General Education Level Mathematics," as it relates to the protection of human participants used in

research, and granted approval for you to proceed with your study on 02-23-2012. As a research

investigator, please be aware of the following:

* You will immediately report to the IRB any injuries or other unanticipated problems involving

risks to human participants.

* You acknowledge and accept your responsibility for protecting the rights and welfare of human

research participants and for complying with all parts of 45 CFR Part 46, the UWF IRB Policy and

Procedures, and the decisions of the IRB. You may view these documents on the Research and

Sponsored Programs web page at http://www.research.uwf.edu/internal. You acknowledge

completion of the IRB ethical training requirements for researchers as attested in the IRB

application.

* You will ensure that legally effective informed consent is obtained and documented. If written

consent is required, the consent form must be signed by the participant or the participant's legally

authorized representative. A copy is to be given to the person signing the form and a copy kept for

your file.

* You will promptly report any proposed changes in previously approved human participant research

activities to Research and Sponsored Programs. The proposed changes will not be initiated without

IRB review and approval, except where necessary to eliminate apparent immediate hazards to the

participants.

31

* You are responsible for reporting progress of approved research to Research and Sponsored

Programs at the end of the project period 08-30-2012. If the data phase of your project

continues beyond the approved end date, you must receive an extension approval from the

IRB.

Good luck in your research endeavors. If you have any questions or need assistance, please contact

Research and Sponsored Programs at 850-857-6378 or irb@uwf.edu.

Sincerely,

Dr. Richard S. Podemski, Associate

Vice President for Research

And Dean of the Graduate School

CC: Subhash Bagui, Kuiyuan Li

Dr. Carla Thompson, Chair

IRB for the Protection of Human

Research Participants

32

B. Syllabi

MIDWESTERN COMMUNITY COLLEGE

MATH 118-00G Concepts in Mathematics

Spring 2012 Mon/Wed 10:00-11:15 Room-H102

INSTRUCTOR: Carrie McCammon OFFICE: H-116A

E-M AIL: cmccammo@midwestern.edu PHONE: (XXX) XXX-XXXX

or 1-800-XXX-XXXX ext XXXX

OFFICE HOURS: Additional times available by appointment

Monday 8:30-10:00 Thursday 10:30-12:00 Wednesday 8:30-10:00 Friday 8:30-12:00

PREREQUISITE (S): Demonstrated competency through appropriate assessment or a grade of

“C” or better in MATH 015 Fundamentals of Algebra I or MATH 023 Essentials of Algebra I or MATH 050 Basic Algebra or MATH 080 Mathematics Principles with Algebra

PROGRAM: Liberal Arts CREDIT HOURS: 3

RESPONSIBLE DIVISION: Liberal Arts CONTACT HOURS: 48

CATALOG DESCRIPTION: Through real-world approaches, present mathematical concepts of measurement, proportion, interest, equations, inequalities and functions, probability and statistics. Brief survey of college mathematics.

COURSE OBJECTIVES: Upon successful completion of this course the student will be expected to:

1. Recognize proportional reasoning and solve proportion problems including both direct and inverse variation.

2. Translate realistic problems into mathematical statements using formulas as appropriate.

3. Use function notation. Graph linear and quadratic functions by the point-plotting method. 4. Solve linear equations and inequalities in one variable. 5. Graph linear equations in two dimensions and inequalities in one dimension.

6. Calculate slope, use slope-intercept form of a line, and interpret slope as a rate of change.

7. Recognize and operate within and between different measurement systems including dimensional

analysis.

8. Solve percent problems including financial applications with simple and compound interest.

33

9. Analyze data including creating frequency distributions and calculating mean, median, mode, range and standard deviation.

10. Recognize characteristics of a normal distribution. Calculate z-scores and percentiles. 11. Calculate probabilities, including AND, OR, NOT and conditional probability

12. Calculate and interpret expected values and weighted averages.

13. Solve counting problems using Fundamental Counting Principle, permutations and combinations.

14. Use relevant mathematical language, laws, notations and reasoning appropriately. 15. Solve a variety of real-world application problems in the above areas. 16. Use a scientific calculator proficiently as related to coursework.

17. Use computer technology, which may include the Internet, the Web, email, or computer

tutorials to enhance the course objectives.

COURSE CONTENT: Topical areas of study include

– Measurement systems Real-world applications and

problem solving Percent and proportion Simple and compound

interest

Probability and statistics Equations, inequalities and functions

TEXT/CURRICULUM MATERIALS: REQUIRED: Blitzer, Robert. Thinking Mathematically. FIFTH edition, Prentice Hall

(If purchased through the bookstore, a student solution’s manual is included at no

additional charge).

NOTE: This is a new edition compared to previous semesters. The new edition contains

several changes. Students are encouraged to purchase the new edition. However, an

older edition would be allowed if the student purchases a new MML access code and

understands that he/she will need to use the online ebook frequently to access the new

material.

REQUIRED: access code for MyMathLab

When purchased new through the Midwestern City Midwestern Community College

bookstore the book package includes this code. Used books or books purchased

elsewhere will require that you buy the access code separately. The code is sold

individually by the Midwestern Community College bookstore as well.

REQUIRED: any brand Scientific Calculator (non-graphing)

There are many good calculators such as Texas Instruments TI-30X II S or

TI-30X Multiview. If you would like help in selecting a calculator, please

contact your instructor.

REQUIRED: Frequent use of online resources To complete graded tasks, this course requires the use of online resources. To support your learning, the College provides access to computers in a variety of locations.

34

ACADEMIC HONESTY STATEMENT: The College is committed to academic integrity in all its practices. The faculty value intellectual integrity and a high standard of academic conduct. Activities that violate academic integrity undermine the quality and diminish the value of

educational achievement.

Cheating on papers, tests or other academic works is a violation of College rules. No student

shall engage in behavior that, in the judgment of the instructor of the class, may be construed

as cheating. This may include, but is not limited to, plagiarism or other forms of academic

dishonesty such as the acquisition without permission of tests or other academic materials

and/or distribution of these materials and other academic work. This includes students who aid

and abet as well as those who attempt such behavior.

The Midwestern Community College Community College Student Handbook defines the

“Scholastic Dishonesty” policy in this way: “Any student found guilty of scholastic

dishonesty, which includes plagiarism, collusion, or cheating on any examination or test is

subject to suspension from the college.”

ADA STATEMENT: Midwestern Community College seeks to provide effective services and accommodations for qualified individuals with documented disabilities. The goal of

Disability Support Services (DSS) is to provide opportunities for equal access in college

programs, services, and activities. DSS assists students with disabilities in achieving their

educational goals through such services as academic and career counseling, adaptive testing,

tutoring, note taking, interpreting, and test proctoring.

If you need a course accommodation because of a documented disability, you are required to

register with Disability Support Services at the beginning of the semester. You may contact

this department at 800-377-4882 ext. 2282 or 812-298-2282. If you require assistance during

an emergency evacuation, notify your instructor, immediately. Look for evacuation procedures posted in your classrooms.

COPYRIGHT STATEMENT: Students shall adhere to the laws governing the use of

copyrighted materials. They must insure that their activities comply with fair use and in no

way infringe on the copyright or other proprietary rights of others and that the materials used

and developed at Midwestern Community College contain nothing unlawful, unethical, or

libelous, and do no constitute any violation of any right of privacy.

LIBRARY STATEMENT: The Midwestern Community College Virtual Library is available to

students on and off campus. It offers full- text journals and books and other resources essential for course assignments. It can be accessed by going to XXXXXXXXXXXXX.

WE CARE ABOUT YOUR SUCCESS: In addition to your instructor and your classmates, there are several ways for you to receive assistance as needed for the topics in this course:

Starfish This course is part of a student success project between our institution and Starfish Retention Solutions. Throughout the term, you may receive emails from Starfish regarding

your course grades or academic performance. Please pay careful attention to these messages

and consider the recommended actions. These are sent to you to help you be successful! In

addition your instructor may request that you schedule an appointment through Starfish or

recommend that you contact a specific campus support resource or you may be contacted

35

directly by the staff from one of these departments. To access Starfish, login to Blackboard,

select Tools, and click on the Starfish Link. If you have any questions about Starfish, please

contact your instructor.

Online Resources (MyMathLab) MyMathLab is a website that accompanies our textbook. Online, you will have access to an enormous wealth of information. We will use this website to complete graded work, but you will want to use the site as a learning tool to

enhance your performance in this course.

Math & Writing Study Center At the South campus in Midwestern City, we have an open computer classroom staffed by math instructors and tutors dedicated to students

completing math and/or writing assignments. For our class, this is a great resource because

students can use the computers to access MyMathLab and other online math resources while

trained staff is nearby to help as needed. The Math & Writing Study Center is located in room

H104. The Center is staffed Monday-Thursday 9:00am-8:00pm, Friday 9:00am-4:00pm and

Saturday 9:00am-2:00pm. For more information, stop by H104 or call (812) 298-2521.

Math Tutoring Students may also receive extra help on all course concepts from the peer tutors in the Academic Enrichment Center located in room C114 on the South campus in

Midwestern City. Trained Midwestern Community College student tutors are available

Monday-Thursday 8:00am-8:00pm and Friday 8:00am-4:45pm. Study tables are offered for

student in MATH 118. No reservations required. Come for as long as your schedule will allow

to work on course material with students from any of 118 section while tutors are nearby to

help. For more information, stop by the AEC or call (812) 298-2389. Tutoring is available at

all Midwestern City Midwestern Community College sites as well. Inquire at your local site

or look for posted advertisements.

Tutoring via Blackboard IM (instant messaging) Online tutoring sponsored by the Academic Enrichment Center is available through Pronto between the hours of 8:00am and 4:45pm Monday through Friday. Students can access Pronto through their class inside

Blackboard. After opening any class, go to Communications and look for the Pronto link. Once in Pronto, look for “07 Midwestern City Ask a Tutor”.

Tutoring available through PEARSON (MyMathLab company) The Pearson Tutor

Center provides a convenient opportunity for students to speak with qualified college

mathematics and statistics instructors for valuable help during evening study hours. Once

registered, students are ready to use the service in four ways: phone, fax, email, or interactive

web. Please note that this free, helpful service is NOT affiliated with the College but rather is

a service of the textbook company. The Pearson Tutor Center is available at no additional

charge with your MyMathLab subscription. To register, contact the Tutor Center by calling 1-

800-877-3016 (5pm-12am est. Sunday-Thursday) and provide your access code, Course ID, or

valid username and password of your MyMathLab account. For more information you may

call them at toll free at 1-800-877-3016 or visit their website at

www.pearsontutorservices.com

36

CALCULATOR USE: Calculators may be used in this course, for homework and for all tests. The math department policy declares that the following types of calculators are NOT allowed:

Graphing Calculators

Those that make noise or beep Calculators that factor polynomials or perform measurement conversions

The calculator function on a cell phone

The calculator within any hand-held device such as a Palm or other PDA

Some topics in the course may be more challenging without the use of a scientific calculator. You

should use a calculator such as the TI-30X IIS or TI-30X Multiview, but there are many other good

options. Please ask if you have questions about using a particular calculator.

ATTENDANCE POLICY: In order to provide you with a quality education, it is important for you to

attend class regularly. Any student who has decided to not complete the course should withdraw

him/herself from the course. Students must complete this process by contacting an advisor or the Office of Admissions.

Any student who remains enrolled will receive zero scores for any work not completed and will

also receive a final course grade based on the total points possible for the course.

Students who miss class are responsible for making up the work missed. Contact your instructor

and/or a classmate. Make arrangements to copy notes from a classmate. Stay on track with the

syllabus deadlines. Utilize tutoring resources and/or instructor office hours as needed.

LAST DATE TO WITHDRAW: Friday, April 6, 2012

METHOD (S) OF DELIVERY: Lecture

METHOD (S) OF EVALUATION: In class and out of class Activities/Homework/Quizzes, 4 Unit

Tests, and 1 Final Exam. No additional points, extra credit or bonus will be offered.

GRADING PROCESS AND SCALE:

Activity Points Each Total

4 Unit Tests 100 points each 400

Final Exam 100 points 100

MyMathLab Homework

Average of all multiplied by 0.25 to convert % to points

25

MyMathLab Quizzes Average of all multiplied by 0.25 to convert % to points

25

Problems/Activities Average of all multiplied by 0.50 to convert % to points

50

There are 600 points possible in the course. Your final course grade will be determined using the following

scale.

37

Overall

Grading Scale (%)

Course

Grade

Total Points

Needed 90% - 100% A 537 – 600

80% - 89% B 477 – 536

70% - 79% C 417 – 476

60% - 69% D* 357 – 416

Below 60% F 0 - 356

*NOTE: A grade of C or better is required in order to transfer credits to another institution.

Also, some programs will require a grade of C or better in this course. Please contact

your advisor or the admissions office with questions about this.

GRADE RECORD: All scores will be recorded in the course’s online grade book inside Distance

Learning. You access this through Campus Connect. Click on Distance Learning and then select this course. Inside the course, you will find the button called My Grades.

Students are responsible for tracking their progress by referring to the grade book. The end of the list

will always show you an updated average of where you stand in the course. This average only

calculates the scores that have been entered up to that time. The average is weighted correctly with the

percentages that will be used to calculate your final course grade (as shown in the table above).

Inside MyMathLab, students will have access to another grade book. The scores provided in

this location are only from the activities completed in MyMathLab. These scores will

periodically be transferred into your grade book inside the course site so that all grades can be

monitored in one location.

Please check your grade book inside Distance Learning often. This is the official grade book of

the course. Please let me know if you think something might be recorded incorrectly.

MAKE-UP AND LATE WORK POLICY: Unless ADVANCE permission has been requested and granted, all work not completed by the deadline date and time due will be subject to the following penalties.

MYMATHLAB ASSIGNMENTS: There are NO MAKE UPS for missed MyMathLab work. Students

who miss these assignments are encouraged to use the Study Plan area inside MyMathLab to practice

material from the missed sections. NOTE: Technology issues are NOT an excusable reason for not

submitting work.

PROBLEMS/ACTIVITIES: There are NO MAKE UPS for points missed from any assigned

in-class problems or activities. In most cases, you will not be able to make these up even with

advance permission.

UNIT TESTS: If you are not in class on test day and have not made advance arrangements,

you can take the test with a penalty. Tests may be taken up to 7 days past the deadline but

will result in a loss of 10 points for each day late (excluding Sunday). You must contact the

instructor to make arrangements for the test to be available in the campus Testing Center.

38

FINAL EXAM: There are NO MAKE UPS

Regardless of the policy above, ALL WORK must be completed by the time of the FINAL EXAM.

What is ADVANCE permission? If you have a legitimate reason to miss class, talk with your

instructor ahead of time. If your instructor agrees, he/she will work with you to negotiate a new

deadline date for that task. This process must be complete before the deadline arrives, so plan

ahead.

In the case of unforeseen emergency, you must contact your instructor as soon as

possible (use email or leave a phone message). In most cases, you will be

required to provide documentation of your emergency for your instructor to

determine if an exception to the above rules would be appropriate in that

circumstance.

HOMEWORK/QUIZZES: Work will be assigned inside MyMathLab to earn points. These assignments will have completion deadlines that are displayed within the MyMathLab website.

Technology issues are NOT an excusable reason for not submitting work. PLAN AHEAD.

Even though not required for a grade, students are expected to practice textbook exercises for

each section studied during the course. Since it is not possible to cover every problem type

through examples in class or during graded assignments or quizzes, completing the suggested

problems from the text is the best way to ensure that you are fully prepared for the exams.

ABOUT HOMEWORK – Some important things to know about the homework:

Work problems from the textbook before attempting the homework online. You can submit answers in any order and as many times as you would like (until the deadline).

This is like having built-in extra credit since you can continue to redo homework sections

until achieving 100%

You may print out your problems to work with them offline and then return online to answer the questions.

There are no time limits. The assignments can be completed at multiple times. This means you can leave and come back as often as you would like.

There are many ways to get help on your homework (videos, similar problems, etc). Use these resources with caution – they are very helpful, but might make it too easy to complete the problems without fully learning the material. Make note of when you need to use the extra helps. These are areas that you will need to study.

Some homework questions give hints that you will NOT see on unit exams. For example, the homework question might tell you which formula to use.

The online homework does NOT cover every problem type from the unit. To prepare for the unit tests, you will still need to practice the textbook problems.

39

Follow these steps to complete a HOMEWORK assignment. Go to http://www.mymathlab.com and log in. Once you are inside the course:

1. Click on the HOMEWORK tab.

2. Click on the assignment (the section) that you wish to do.

3. Click on the first question and it will open the assignment. 4. Complete the problem and click CHECK ANSWER.

a. If you are correct, it will proceed to the next question.

b. If you are incorrect, you will receive a message. Then you can try again.

c. After about three incorrect responses (or invalid answers), the correct answer will

be displayed. Then, you may either go to a SIMILAR EXERCISE (in order to try

to a new problem to earn the points) or you can choose NEXT EXERCISE (and

leave this problem without earning its points). 5. You may jump from one exercise to another by clicking on the numbers at the top of

your screen. On the question numbers, the red and green marks designate your missed and correct problems.

6. To receive extra help with a problem, you can click on VIEW AN EXAMPLE or HELP ME SOLVE THIS. Anytime you see a camera icon, you can use it to watch a video clip.

7. The SUBMIT button at the bottom is optional in the homework. Your scores automatically go into the grade book as you work each individual problem.

ABOUT QUIZZES – Some important things to remember about the quizzes:

Be sure to finish the textbook problems AND the MyMathLab homework (earning 100% if possible) before attempting the quizzes over those sections.

Each quiz must be completed within 75 minutes of starting it.

Quiz questions will be similar to the online homework. Practice how to enter answers using the homework sections so you can answer quizzes as well.

Unlike the homework, this time there are no help buttons as you solve the quiz problems. Each quiz can be taken 2 times. You are not required to redo your quiz, but it is an

optional chance to learn and to improve your grade. The highest score will count as your quiz grade.

Once you start a quiz, you must finish it completely or receive no points for unanswered questions. Starting a quiz or accidentally leaving during the quiz will count as one of your 2 attempts.

Each quiz can be reviewed after you have completed it. This means you can see the correct

answers in order to study for the retake or for the exams. Review your quizzes by going to the

Gradebook inside MyMathLab and clicking “Review” next to the quiz name.

Follow these steps to complete a QUIZ. Go to http://www.mymathlab.com and log in. Once you

are inside the course:

1. Click on the Do a Quiz tab. 2. Click on the name of the Quiz that you wish to do. 3. You will be taken to a screen reminding you of the time limit and the number of attempts you

have remaining.

To begin, click “I am ready to start”.

4. You may jump from one question to another using the buttons at the bottom. DO NOT

click the Back arrow on your Browser window. If you leave the quiz page, the software

will assume you are finished (meaning you would receive zeros for any unanswered

problems). 5. The number of questions left to answer as well as the time remaining to complete will be

40

displayed in the panel at the right hand side. 6. When you are finished with all of the questions, use the buttons at the top to go back and

review your work. Use your time wisely and make sure that you are satisfied with your answers

7. When you are completely sure that you are finished, click SUBMIT TEST. Once you submit, your score will appear on the screen and will also go into the grade book in MyMathLab.

8. After you have completed a Quiz, you have the option to Review it. Anytime after taking a quiz, you can go to your grade book inside MyMathLab and click the Review button next to the quiz name. This allows you to see the correct answers. Point your mouse to that answer to see a pop up containing the solution you entered for that problem.

9. You have the option to take each quiz 2 times before the deadline. Both scores will show in your grade book, but only the higher of the two scores will be used in your grade.

MORE HOMEWORK/QUIZ INFO

Although the assignments and quizzes are excellent practice for the tests, not all of the material covered in the homework or quiz will appear on the tests. Additionally, not everything on the tests will have been

covered in the homework or on the quizzes. Therefore, students must practice MORE than just

these graded problems in order to be successful.

While completing your homework and quizzes, you should work all problems onto scrap

paper. Organize this work so that you have the problems as notes to use as you study for the

exams. These notes will also be helpful to you in case you want to ask a question of your

instructor during class. Graded MyMathLab homework and quizzes are due before 11:59pm EST on the deadline date.

Technology issues are NOT an excusable reason for not submitting work. PLAN AHEAD.

PROBLEMS/ACTIVITIES: Throughout the semester, class time will be spent exploring

and investigating mathematics. Work will be completed during class and/or assigned to

be submitted by a given deadline. Sometimes quizzes or other homework will be given.

Unless otherwise announced, all problems, activities, assignments and quizzes will be

graded based on a score of 10 points each.

Since you must be in class for many of these activities, it is rare that any points due to absence

will be made up. However, your lowest three scores will be dropped. All remaining items

will be averaged together then multiplied by 0.50 to convert the percentage into points to

determine the 50 points possible out of the overall course grade.

Even if not assigned for a grade, you are required to do the suggested problems from each

section to keep up with the course work. Check your performance using the answers provided

at the back of the book.

UNIT TESTS: Each unit test is worth 100 points toward your final course grade. In some cases, partial credit points can be earned if the problem is not completely correct but the right procedure was followed. In order to earn this credit you must show all of your work.

Students may use a calculator during the tests. No personal notes will be allowed during the

exam. Selected formulas, charts, and conversion tables will be supplied for you on the exam

itself. Your instructor will notify you of the information that you can expect to see on the

exam.

41

Within one week of the exam deadline, your instructor will post your score inside the official

Gradebook in Distance Learning. You will also be given the chance to review your actual

graded test. If you have any question about how your test was graded or about how many

points you earned, you MUST discuss this with your instructor at this time (or arrange an

appointment to do so). After this initial chance, your instructor has the right to decline any requests for grade corrections.

Once your instructor has been given permission by the department to do so, your graded test

will be released for you to keep. NOTE: It is your responsibility to hold onto your graded

exam. These actual documents will be extremely useful as you study for the final exam. In the

event that you question your grade, you would be responsible for producing the actual test to

prove the correct score.

FINAL EXAM: All students will take a final exam covering all concepts studied over the semester. This test will be worth 100 points toward your overall grade. The final is multiple choice. After completing the paper copy of your test, you will enter your answer choices into a computer database. This will allow you to instantly receive your exam score.

Partial credit will NOT be given on this multiple choice exam. However, you should still clearly label

and organize your work. The paper copy of the test will be checked against the computer answers to

confirm accuracy of your grade. In the case of any technical issues or discrepancies in answers, the

paper copy will be used to determine your exam score.

Students may use a calculator during the final. No personal notes will be allowed during the exam. The same formulas or charts that were provided during the unit tests will also be supplied for you on the exam itself.

If it would improve your overall course grade, the final exam can be counted twice (replacing your

lowest test score). Therefore, doing well on the final can enhance your semester grade. The final

exam is NOT optional and the score on the final exam may NOT be dropped.

CLASSROOM BEHAVIOR: Our classroom should be a positive learning environment. When we work together, we can all succeed! Therefore, behavior that infringes upon a classmate’s

ability to receive instruction will not be tolerated. Such behaviors may include (but are not

limited to) talking without permission, disrespectful comments, or inappropriate use of a

computer, cell phone, or other technology. If a classmate is disturbing your learning

opportunity, please notify the instructor.

Switch cell phones to silent mode and put them out of sight before entering the

classroom. Students should not participate in sending or receiving text messages or participate in online activities or social media during class time. Only in an extreme

circumstance should a call or text be answered during our class time. If you have such a

situation arise that cannot wait until the end of the class, please gather your belongings and

answer your call or text AFTER leaving the room. In order to limit the distractions to your

classmates, return only at a break in the instruction.

42

Unless directly related to course activities, electronic devices should not be heard

or seen within the classroom. Many electronic gadgets can be helpful academic tools as well.

For example, devices with a calendar service can organize your deadlines. Also, there are many “Apps”

available related to our course content. However, there will rarely be a need to use such items during

class time. Unless given special permission, please use your electronics before or after class time.

CREATE YOUR MYMATHLAB ACCOUNT: MyMathLab, CourseCompass, and MathXL are all products that work together with the same online environment provided by our textbook publisher. We will use these components to access resources and

complete some graded coursework. Most often, we will refer to the group of items using one

name – MyMathLab (or MML).

When you purchased your textbook from the bookstore, you received a MyMathLab access code.

This string of letters and numbers is needed only one time - the very first time that you visit the

site. During that visit you will create your own username and password that will be used for all

future visits. Students who did not purchase their textbook through the campus bookstore can

purchase access during the registration process.

Using a computer with internet access, go to: www.mymathlab.com. On the right hand side,

under STUDENTS, click the “register” button. Then follow the on screen directions. To

register, you will need:

1. The access code under the pull tab of the packet which came with your textbook

2. Our course code: mccammon28256 This is the only time you will be asked for

this code. 3. Your Email address (use one that you use regularly. It is needed when you forget

your password) 4. Midwestern Community College’s zip code: XXXXX

TECHNOLOGY NEEDS FOR USING MYMATHLAB: Anytime you want to access MyMathLab, point your internet web browser to:

http://www.mymathlab.com/.

In order to run applications with MyMathLab, your computer must meet certain requirements

and have certain components downloaded onto it. For this reason, you may NOT be able to

access MyMathLab from every computer. For example, public computer labs often have a

block on downloading software to the machine. Please keep this in mind and plan ahead as

needed to complete your assignments during the semester. Most Midwestern Community

College computers already have these downloads completed so they are ready for your use.

When you log into MyMathLab for the first time, run the MyMathLab Browser Check to

prepare your computer. Repeat the process on all computers that you might be using during the semester.

43

LEARN HOW TO ENTER ANSWERS Before you can begin to earn points through the MML assignments, you must understand how the software expects answers to be entered. In the announcements on the first page of your MML course, you will see

a link to learn How to Enter Answers Using the MathXL Player. Click on that title to start the tour.

USEFUL MML RESOURCES Inside MML there are many helpful resources. Successful students will use the site for more than just completing homework. On the left side of the page, I suggest exploring the areas found using the buttons called

Multimedia Library and Study Plan. The Multimedia Library is where you will find helpful videos and PowerPoints to accompany your text. The Study Plan is an optional guide

to learning the sections. The data here will be updated based on your performance on

Quizzes and any optional practice that you do.

MML TECH SUPPORT At any time you need technical assistance with MyMathLab, contact the publisher’s Technical Support. I can help with the math, but not with your computer settings or other issues. Let the company help you

free of charge: ONLINE- Log into http://www.mymathlab.com . Click Help & Support

in the top right corner of the page. This is available 24 hours a day. BY PHONE Call 1-800-677-6337. Staff is available Monday-Friday, from noon to 8 p.m.

44

MATH 118 Calendar

NOTE: The schedule and procedures in this course are subject to change. The instructor and/or

the College reserve the right to change any statements, policies or scheduling as necessary.

Students will be informed promptly of any and all changes.

Un

it 1

Mon 1/9

Syllabus 2

.

1

Course Policies, Procedures Terminology and notation for sets

Un

it 2

Mon 2/6

11.1, 11.2

Fundamental Counting Principle; Factorial

Permutations, Permutations with Duplicate

Items Wed

1/11

2.2,

2.3

Subsets, Venn Diagrams,

universal set, complement,

union, intersection

Wed

2/8

1

1

.

3

Combinations; Distinguishing between

Permutations and Combinations

Mon

1/16 NO

CLA

SS

MLK day, campus closed Sun

2/12

As

sig

n:

MML Deadline: 11.1, 11.2

Wed

1/18

2.4,

2.5 Set operations with 3

sets Survey Problems

Mon

2/13

11.

4,

11.

5

Theoretical and Empirical Probability

Probability with Permutations and

Combinations Sun

1/22

Assign

:

MML Deadline: 2.1, 2.2, 2.3, 2.4 Wed

2/15 N

O

C

L

A

S

S

OUT OF CLASS ASSIGNMENT

Mon

1/23

12.1,

12.2 Population, sample,

frequency Central

Tendency

Sun

2/19

As

sig

n:

MML Deadline: 11.3, 11.4 MML Quiz 2A (Covering 11.1-11.4)

We

d

1/25

12.2,

12.3

More Central

Tendency

Dispersion

Mon

2/20

1

1

.

6

Probabilities with NOT and OR, Finding Odds;

Probabilities with AND, Conditional

Probability Sun

1/29

Assign

:

MML Deadline: 2.5, 12.1, 12.2

MML Quiz 1A (covering 2.1-2.5)

Wed

2/22

11.

7,

11.

8

Probabilities with AND Conditional

Probability Expected Value, review

Mon

1/30

12.4,

12.5 Normal Distribution, z-score,

margin of error, percentiles Sun

2/26

As

sig

n:

MML Deadline: 11.5, 11.6, 11.7

We

d

2/1

Revie

w

Review for Test 1 Mon

2/27

Re

vie

w

Review for Test 2

Su

n

2/5

Assign

:

MML Deadline: 12.3, 12.4, 12.5 MML Quiz 1B (covering 12.1-

12.5)

Tue

2/28

As

sig

n:

MML Deadline: 11.8 MML Quiz 2B (Covering 11.5-11.8)

Test 1

TAKE TEST 1 IN TESTING

CENTER

Wed 2/1 – Mon 2/6

Wed

2/29

T

es

t

2

TAKE TEST 2 IN CLASS

Spring Break March 4 - 10

Mon 3/12

6.1, 6.2

Order of Operations, Expressions,

Distributive Property,

Equations in one variable

Unit 4

Mon 4/2

8.1, 8.2

Converting between fraction, decimal, and

percent; Solve percent applications; Simple

interest

Wed

3/14

6

.

2

variation

Equations with fractions,

Proportions, variation, no solution,

infinite solutions

Wed

4/4

8.

3

Compound Interest; Effective Annual Yield

Sun

3/18

Assign

:

MML Deadline: 6.1, 6.2 Sun

4/8

As

sig

n:

MML Deadline: 8.1, 8.2

Mon

3/19

6

.

3

Applications of Linear

Equations, Literal equations

Mon

4/9

8.

4,

8.

5

Annuities, Loan payment, amortization

45

We

d

3/21

6.4,

7.1

Linear inequalities in 1 variable, 3-

part inequalities, Coordinate plane,

point plotting, function notation,

vertical line test

Wed

4/11

8.5,

Revi

ew

credit cards, Review financial problems

Sun

3/25

Assign

:

MML Deadline: 6.3, 6.4

MML Quiz 3A (Covering 6.1-6.4,

variation)

Sun

4/15

As

sig

n:

MML Deadline: 8.3, 8.4, 8.5

MML Quiz 4A (Covering

8.1-8.5) Mon

3/26

7

.

2

7.3.1

Graphing using intercepts, slope, y

mx b

,vertical and horizontal

lines Systems of

Equations by graphing

Mon

4/16

9.

1,

9.

2,

9.

3

Converting within and between US and

metric systems; Units of Length, Area

and Volume, Units of Weight and

Temperature We

d

3/28

7.4.1 Linear inequalities in 2

variables, Review for Test 3

Wed

4/18

Re

vie

w

Review for Test 4

Su

n

4/1

Assign

:

MML Deadline: 7.1, 7.2, 7.3, 7.4

MML Quiz 3B (Covering 7.1-7.4)

Sun

4/22

As

sig

n:

MML Deadline: 9.1, 9.2, 9.3

MML Quiz 4B (Covering

9.1-9.3)

Test 3

TAKE TEST 3 IN TESTING

CENTER

Wed 3/28 – Mon 4/2

Mon

4/23 T

es

t

4

TAKE TEST 4 IN CLASS

Wed 4/25 Re

vie

w

Review for Final Exam

Final Exam (in class): Wednesday May 2nd

from 10:00-

12:00

46

MIDWESTERN COMMUNITY COLLEGE

COURSE NUMBER/TITLE: ASAS 007 Pre-Algebra

COURSE SECTION: 00G

MEETING DAYS AND TIMES: MW 11:30 – 12:45

CLASSROOM/LOCATION: H226

SEMESTER: Spring YEAR: 2012

PREREQUISITE (S): Approval of the Mathematics Program Chair. Concurrent enrollment in MATH 118

is required.

DEPARTMENT: Academic Skills Advancement PROGRAM: Liberal Arts

CREDIT HOURS: 3 CONTACT HOURS: 3 weekly lecture hours

INSTRUCTOR NAME: Ryan Grossman

INSTRUCTOR PHONE NUMBER: XXX-XXX-XXXX

In case of emergency, email is the best way to reach your instructor. If email is not available to you,

you may call the office listed below in order to leave a message that will be forwarded on to your

instructor. Please note that your instructor might not receive this message until the next class

meeting.

Office of General Education (XXX) XXX-XXXX

INSTRUCTOR E-MAIL: rgrossman2@midwestern.edu

INSTRUCTOR OFFICE HOURS: MW 1:30 - 3:00; TR 12:00 - 3:00

INSTRUCTOR OFFICE LOCATION: Academic Annex (behind the Trade and Tech Building)

CATALOG DESCRIPTION: Special Topics Course: Concentrates on basic operations with fractions,

integers, exponents, proportional reasoning, basic linear and literal equations, algebraic expressions, and

linear graphs. Includes a variety of applications of these topics.

MAJOR COURSE LEARNING OBJECTIVES: Upon successful completion of this course, the student will be

expected to: 1. Demonstrate Number Sense by

a. Performing fraction and integer operations by hand and with calculator

b. Converting between fractions and decimals

c. Identifying place values and using rounding and estimation

d. Identifying perfect squares and calculating square roots

e. Using order of operations

f. Applying proportional reasoning and solving percent and proportion problems

g. Operating within and between the US customary and Metric system by dimensional analysis.

2. Demonstrate Algebraic Sense by

a. Evaluating expressions and formulas

b. Simplifying expressions

c. Solving linear equations and literal equations

3. Demonstrate Geometric Sense by a. Calculating circumference of a circle and perimeter of any 2-dimensional figure. Calculating area of a

triangle, rectangle, square, and circle. Calculating volume of a rectangular prism, cylinder and cube. 4. Demonstrate Graphing Sense by

a. Solving inequalities in one variable and graphing on a number line b. Reading and interpreting tables, line graphs and circle graphs. c. Demonstrating an understanding of and using the concept of slope d. Graphing linear equations using t-tables, intercepts, and slope-intercept form e. Graphing linear equations in slope-intercept form given slope and y-intercept or given two points.

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5. Demonstrate Competence in Mathematical Language by a. Translating verbal expressions into algebraic symbols and vice-versa b. Using relevant mathematical language, laws, and notations appropriately c. Defining variables in applications

6. Solve a variety of application problems in the above areas 7. Use a scientific calculator proficiently as related to coursework 8. Use computer technology which may include the Internet, e-mail, or computer software to enhance the

course objectives

COURSE CONTENT: Topical areas of study include -

Integers Rational numbers

Proportional reasoning Ratios and percents

Measurement systems Algebraic expressions

Solving linear inequalities Literal equations

Graphing linear equations Solving linear equations

Geometric concepts Applications

TEXT/CURRICULUM MATERIALS:

REQUIRED: MyMathLab access code

When purchased new through the Midwestern City Midwestern Community College bookstore, the book

package includes this code. Used books or books purchased elsewhere will require that you buy the access

code separately. The code is sold individually by the Midwestern Community College bookstore as well.

REQUIRED: Scientific Calculator (non-graphing)

There are many good calculators such as Texas Instruments TI-30X Multiview. If you would like help in

selecting a calculator, please contact your instructor.

REQUIRED: Frequent use of online resources

To complete graded tasks, this course requires the use of online resources. To support your learning, the

College provides access to computers in a variety of locations.

OPTIONAL: Martin-Gay, Prealgebra & Introductory Algebra, THIRD edition. Prentice Hall.

NOTE: This is a new edition compared to previous semesters. The new edition contains several

changes. Students are encouraged to purchase the new edition. However, an older edition would be

allowed if the student purchases a new MML access code and understands that he/she will need to use

the online ebook frequently to access the new material.

SUGGESTED: Earbuds/headphones

You will be assigned multimedia homework which will require you to watch videos. You will be expected

to complete these Multimedia Assignment inside and outside of class.

ACADEMIC HONESTY STATEMENT:

The College is committed to academic integrity in all its practices. The faculty value intellectual integrity and a

high standard of academic conduct. Activities that violate academic integrity undermine the quality and

diminish the value of educational achievement.

Cheating on papers, tests or other academic works is a violation of College rules. No student shall engage in

behavior that, in the judgment of the instructor of the class, may be construed as cheating. This may include,

but is not limited to, plagiarism or other forms of academic dishonesty such as the acquisition without

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permission of tests or other academic materials and/or distribution of these materials and other academic work.

This includes students who aid and abet as well as those who attempt such behavior.

The Midwestern Community College Student Handbook defines the “Scholastic Dishonesty” policy in this

way: “Any student found guilty of scholastic dishonesty, which includes plagiarism, collusion, or cheating on

any examination or test is subject to suspension from the college.”

COPYRIGHT STATEMENT:

Students shall adhere to the laws governing the use of copyrighted materials. They must insure that their

activities comply with fair use and in no way infringe on the copyright or other proprietary rights of others and

that the materials used and developed at Midwestern Community College contain nothing unlawful, unethical,

or libelous, and do not constitute any violation of any right of privacy.

ADA STATEMENT:

Midwestern Community College Community College seeks to provide effective services and accommodations

for qualified individuals with documented disabilities. The goal of Disability Support Services (DSS) is to

provide opportunities for equal access in college programs, services, and activities. DSS assists students with

disabilities in achieving their educational goals through such services as academic and career counseling,

adaptive testing, tutoring, note taking, interpreting, and test proctoring.

If you need a course accommodation because of a documented disability, you are required to register with

Disability Support Services at the beginning of the semester. You may contact this department at 800-377-4882

ext. 2282 or 812-298-2282. At the Greencastle site, you may contact Brad Johnson at 1-800-750-3007. If you

require assistance during an emergency evacuation, notify your instructor, immediately. Look for evacuation

procedures posted in your classrooms.

ATTENDANCE POLICY: In order to provide you with a quality education, it is important for you to attend class

regularly. Any student who has decided to not complete the course should withdraw him/herself from the

course. Students must complete this process by contacting an advisor or the Office of Admissions. Any

student who remains enrolled will receive zero scores for any work not completed and will also receive a final

course grade based on the total points possible for the course.

Points will be earned from in-class points and activities. Students who miss class will not have the opportunity

to earn these points. Students who miss class are responsible for making up the work missed. Contact your

instructor and/or a classmate. Make arrangements to copy notes from a classmate. Stay on track with the

syllabus deadlines. Utilize tutoring resources and/or instructor office hours as needed.

LAST DATE TO WITHDRAW: April 6, 2012

NOTE: Enrollment in MATH 118 for this special course program requires students to also

participate in ASAS 007. Students who withdraw from ASAS 007 will be required to withdraw

from MATH 118 as well.

LIBRARY STATEMENT:

The Midwestern Community College Virtual Library is available to students on and off campus. It offers full-

text journals and books and other resources essential for course assignments. It can be accessed by going to

WE CARE ABOUT YOUR SUCCESS:

In addition to your instructor and your classmates, there are several ways for you to receive assistance as

needed for the topics in this course:

Starfish This course is part of a student success project between our institution and Starfish Retention

Solutions. Throughout the term, you may receive emails from Starfish regarding your course grades or

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academic performance. Please pay careful attention to these messages and consider the recommended actions.

These are sent to you to help you be successful!

In addition your instructor may request that you schedule an appointment through Starfish or recommend that

you contact a specific campus support resource or you may be contacted directly by the staff from one of these

departments. To access Starfish, login to Blackboard, select Tools, and click on the Starfish Link. If you have

any questions about Starfish, please contact your instructor.

Online Resources (MyMathLab) MyMathLab is a website that accompanies our textbook. Online, you

will have access to an enormous wealth of information. We will use this website to complete graded work, but

you will want to use the site as a learning tool to enhance your performance in this course.

Math & Writing Study Center At the South campus in Midwestern City, we have an open computer

classroom staffed by math instructors and tutors dedicated to students completing math and/or writing

assignments. For our class, this is a great resource because students can use the computers to access

MyMathLab and other online math resources while trained staff is nearby to help as needed.

The Math & Writing Study Center is located in room H104. The Center is staffed Monday-Thursday 9:00am-

7:00pm and Friday 9:00am-4:00pm. For more information, stop by H104 or call (812) 298-2521.

Math Tutoring Students may also receive extra help on all course concepts from the peer tutors in the

Academic Enrichment Center located in room C114 on the South campus in Midwestern City. Trained

Midwestern Community College student tutors are available Monday-Thursday 8:00am-8:00pm and Friday

8:00am-4:45pm.

For more information, stop by the AEC or call (812) 298-2389. Tutoring is available at all Midwestern City

Midwestern Community College sites as well. Inquire at your local site or look for posted advertisements.

Tutoring via PRONTO (instant messaging) Online tutoring sponsored by the Academic Enrichment

Center is available through Pronto between the hours of 8:00am and 4:45pm Monday through Friday. Students

can access Pronto through their class inside Blackboard. After opening any class, go to Communications and

look for the Pronto link. Once in Pronto, look for “07 Midwestern City Ask a Tutor”.

Tutoring available through PEARSON (MyMathLab company) The Pearson Tutor Center provides

a convenient opportunity for students to speak with qualified college mathematics and statistics instructors for

valuable help during evening study hours. Once registered, students are ready to use the service in four ways:

phone, fax, email, or interactive web. Please note that this free, helpful service is NOT affiliated with the

College but rather is a service of the textbook company.

The Pearson Tutor Center is available at no additional charge with your MyMathLab subscription. To register,

contact the Tutor Center by calling 1-800-877-3016 (5pm-12am est. Sunday-Thursday) and provide your

access code, Course ID, or valid username and password of your MyMathLab account. For more information

you may call them at toll free at 1-800-877-3016 or visit their website at www.pearsontutorservices.com

Personalized Tutoring The Academic Enrichment Center will provide a tutor specifically for this class.

The tutor’s name is Chandra Hull. She will post regular times which you can meet with her. You can also

schedule time to meet with her within the parameters of her schedule. Should you need to contact Chandra,

you will need to speak to Lisa Baker. Lisa’s office is E108B. Lisa will communicate your message to

Chandra. Lisa’s phone number is 812-298-2315. Lisa’s email is lbaker30@ivytech.edu.

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CALCULATOR USE: Calculators may be used in this course, for homework, quizzes and tests.

The math department policy declares that the following types of calculators are NOT allowed:

Graphing Calculators;

Those that make noise or beep

The calculator function on a cell phone

The calculator within any hand-held device such as a Palm or other PDA

Students will be expected to solve problems with their calculator. At the same time, students will be expected

to show as much work as possible in order to receive feedback (and points). Students will learn how to add,

subtract, multiply and divide whole numbers, integers, fractions and decimals using their calculator. Towards

the end of the class, students will be expected to add, subtract, multiply and divide integers and fractions by

hand, without the use of a calculator.

METHOD(S) OF DELIVERY: Emporium with lecture

As a 3-credit hour math course, this class requires a great deal of work outside of class in order to be successful

in your learning of the material. Historically, it has been advised that the average student spend three hours

outside of class for every one instructional hour.

Therefore, the average student should spend at least 9 hours each week on ASAS 007

outside of our regular class time. If you wish to be more than just an average student, then your

schedule will require an additional time commitment.

GRADE RECORD: All scores will be recorded in the course’s online grade book inside Distance Learning. You

access this through Campus Connect. Click on Distance Learning and then select this course. Inside the

course, you will find the button called My Grades.

Inside MyMathLab, students will have access to another grade book. The scores provided in this location are

only from the activities completed in MyMathLab. These scores will periodically be transferred into your

grade book inside the course site so that all grades can be monitored in one location.

Please allow up to 1 week after an exam deadline for the grade book to reflect this score. Quizzes, in class

points, and MyMathLab scores will be updated at least once per unit.

Please check your grade book inside Distance Learning often. This is the official grade book of the

course. Please let your instructor know if you think something might be recorded incorrectly.

METHOD (S) OF EVALUATION: In-class problems/activities, 5 post-tests

GRADING PROCESS AND SCALE:

Activity Points Each Total

5 Post Tests 200 points each 1000

Problems/Activities Average of all multiplied by

2.00 to convert % to points 200

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There are 1200 points possible in the course. Your final course grade will determined using the following scale.

*NOTE: ASAS 007 does not satisfy the prerequisite requirements for any future math courses. Students who

wish to register in a math course for a future semester will be required to retake the math placement

test to determine the correct course placement.

MAKE-UP AND LATE WORK POLICY: Unless ADVANCE permission has been requested and granted, all

work not completed by the deadline date and time due will be subject to the following penalties.

DEFINITION OF LATE: MyMathLab Homework is considered late if it is not completed by 11:59pm EST

on the day it is due. Pre-Tests and Post-Tests are considered late if they are not completed by the time the

Testing Center closes the day it is due. Take note that you need to report to the Testing Center at least one hour

before they close in order to start an assessment.

MYMATHLAB: There are NO MAKE UPS for missed MyMathLab work. Students who miss these

assignments are encouraged to use the Study Plan area inside MyMathLab to practice material from the missed

sections. NOTE: Technology issues are NOT an excusable reason for not submitting work.

PROBLEMS/ACTIVITIES: There are NO MAKE UPS for points missed from in-class problems or

activities. You will not be able to make these up.

TESTS: If you did not complete the Pre-Test, Practice Test or Post-Test by the established deadline, you must

contact the instructor to make arrangements. The penalty for taking the Post-Test late is 20 points deducted

from your Post-Test score every day late (excluding Sundays) up to 7 days late. There is no penalty for a late

Pre-Test and Practice Test. A late test is defined as not completing the test by the end of the day in which it is

due.

Regardless of the policy above, ALL WORK must be completed by April 30, 2012.

What is ADVANCE permission? If you have a legitimate reason to miss class, talk with your instructor

ahead of time. If your instructor agrees, he/she will work with you to negotiate a new deadline date for that

task. This process must be complete before the deadline arrives, so plan ahead.

In the case of unforeseen emergency, you must contact your instructor as soon as possible (use

email or leave a phone message). In most cases, you will be required to provide documentation

of your emergency for your instructor to determine if an exception to the above rules would be

appropriate in that circumstance.

PROBLEMS/ACTIVITIES: Throughout the semester, class time will be spent exploring and investigating

mathematics. Work will be completed during class and/or assigned to be submitted by a given deadline.

Sometimes quizzes or other homework will be given. Your instructor will decide what methods of assessment

will be used to combine together for a total of 200 points towards your final course grade. Each assignment

will carry the same weight and be averaged together to find the 200 points. If you are absent from class, for

whatever reason, you will NOT be able to make-up the in-class points.

Overall

Grading Scale (%)

Course

Grade

Total Points

Needed

90% - 100% A 1074-1200

80% - 89% B 954-1073

70% - 79% C 834-953

60% - 69% D* 714-833

Below 60% F 0-713

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The purpose of the class time in ASAS 007 is to prepare you for upcoming MATH 118 topics. We will spend

time investigating material necessary to succeed in the 118 ideas in the coming weeks. The in-class topics

may or may not match the math skills you are learning/practicing within your modules since you are allowed to

work through those at your own pace.

MODULES: There are five modules you must complete before the end of the semester. Each module will have a pre-

test, homework, practice test and post-test. You will complete the module by completing the activities

previously listed, in this order:

PRE-TEST HOMEWORK PRACTICE TEST POST TEST

Only the Post-Tests (one Post-Test per Module) count into your final course grade. The Pre-Test helps you to

see what topics you already know and which ones require additional work. Within the homework, you will

practice all of these concepts to help fine-tune your skills. Once the homework has been completed, you take

the Practice Test to get an idea of how well you have done and if you are ready to take the graded Post-Test.

You can work through the modules at your own pace. A calendar will be given to show you a "Target"

completion date to help you stay on track in the course, but also a "Final Deadline" when you absolutely must

be finished. By working ahead of schedule, you will have more time to spend on any troublesome areas in the

future.

Should you earn 90% or better on the Pre-Test, then you may skip directly to the Practice Test. The homework

would not be required for you in this case; it would be optional. This would allow you to move ahead more

quickly towards completing your modules.

Each Post-Test is worth 200 points of your final course grade. If your post-test score is less than 70%, then you

will be required to complete a retake. If you earn 70% or higher on the Post-Test, then you have the option to

retake the Post-Test. You can retake a Post-Test as many times as you would like up until the final deadline.

All retakes require that students complete the following steps:

1. Discuss your retake plan with your instructor.

2. Complete the Study Plan for that module

3. Attend at least one tutoring session to go over your previous Practice Test, Post Test and Study Plan. Your

instructor must approve of your choice of tutoring. Your instructor will provide you with a form for your tutor

to complete.

4. Take a different version of the Post Test.

HOMEWORK: After you complete the Pre-Test for a module, you will have to complete homework. There are two

types of homework you will come across. The Multimedia Homework is a collection of videos, PowerPoints

and Interactive Activities. You must watch/complete at least 70% of each Multimedia Assignment before

you will be allowed to advance to the other homework assignments. You can come back to each

Multimedia Assignment as many times as you would like to.

The other type of homework you will be assigned contains actual math problems for you to solve. You will be

able to access the homework whenever and however many times you would like. You must earn at least 80%

on all Homework Assignments (excluding the Multimedia Assignments) before you can take the Practice

Test.

ABOUT MML HOMEWORK – Some important things to know about the homework:

You should continue practicing all homework problems and strive to achieve 100% in order to learn the

material and study for the post-test.

You can submit answers in any order and as many times as you would like (until the deadline).

You may print out homework problems to work with them offline and then return online to answer the

questions at a later time.

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There are no time limits in homework. The assignments can be completed at multiple times. This

means you can leave and come back as often as you would like.

There are many ways to get help on your homework (videos, similar problems, etc). Use these resources

with caution – they are very helpful, but might make it too easy to complete the problems without fully

learning the material.

Some homework questions give hints that you will NOT see on post-tests. For example, the homework

question might tell you which formula to use.

Follow these steps to complete a HOMEWORK assignment. Go to http://www.mymathlab.com and

log in. Once you are inside the course:

1. Click on the DO HOMEWORK tab.

2. Click on the assignment (the section) that you wish to do.

3. Click on the first question and it will open the assignment.

4. Complete the problem and click CHECK ANSWER.

a. If you are correct, it will proceed to the next question.

b. If you are incorrect, you will receive a message. Then you can try again.

c. After about three incorrect responses (or invalid answers), the correct answer will be displayed.

Then, you may either go to a SIMILAR EXERCISE (in order to try to a new problem to earn the

points) or you can choose NEXT EXERCISE (and leave this problem without earning its points).

5. You may jump from one exercise to another by clicking on the numbers at the top of your screen. On the

question numbers, the red and green marks designate your missed and correct problems.

6. To receive extra help with a problem, you can click on VIEW AN EXAMPLE or HELP ME SOLVE

THIS. Anytime you see a camera icon, you can use it to watch a video clip.

7. The SUBMIT button at the bottom is optional in the homework. Your scores automatically go into the

grade book as you work each individual problem.

TESTS: Tests contain application problems. All answers must show supporting work. Correct answers without

detailed work will NOT receive any credit. In some cases partial credit might be given. Your scrap paper will

be collected. I expect to see ALL of your work. If you do not show your work, then you will lose credit.

Prepare yourself well by studying for the Post-Tests! Students should study for the Post-Tests by reviewing

all assignments inside MyMathLab, reviewing all class notes, and reading and practicing problems from the

ebook. Use the provided MyMathLab resources and ask any questions that you might have.

To assist in your preparation for each exam, we have created Practice Tests inside MyMathLab. These do NOT

count into your score, but YOU MUST COMPLETE THE PRACTICE TEST BEFORE YOU CAN

ATTEMPT THE POST-TEST. This is a good way to practice the material from all sections within the module

at once before taking the Post-Test. The Pre-Tests, Practice Tests and Post-Tests have time

limits of exactly 90 minutes each.

Although these are very helpful ways to study, please note that the Practice Tests are NOT intended to practice

everything that might be covered on the test. Also, there may be questions on the Practice Test that you might

not see on the Post-Test.

Each post-test is worth 200 points toward your final course grade. You must take each Pre-Test and

Post-Test in the Testing Center. The tests are password protected on MyMathLab.

Your instructor will send this password to the Testing Center as well as other directions to state that you can

use your calculator and scrap paper. After you have taken the test, your scrap paper will be returned to the

instructor for grading. As long as you are working on-time with the class calendar, your test will be available

for you without any special arrangements. If you are behind in the calendar, you will need to discuss your

testing situation with the instructor.

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ABOUT TESTS – Some important things to remember about the tests:

The test must be completed within 90 minutes of starting it.

Test questions will be similar to the online homework. Practice how to enter answers using the homework sections so you can answer quizzes as well.

Unlike the homework, there are no help buttons as you solve the test problems.

Each test can be taken only one time without instructor approval. You can earn another attempt at your Post-Test if you complete the Study Plan for that module and go to tutoring from the Math and Writing Center or the Academic Enrichment Center.

Once you start a test, you must finish it completely or receive no points for unanswered questions. Starting a test or accidentally leaving during the test will count as an attempt.

Each test can be reviewed after you have completed it. This means you can see the correct answers in order to study for the retake or for the other exams. Review your tests by going to the Gradebook inside MyMathLab and clicking “Review” next to the test name.

Follow these steps to complete a TEST. Go to http://www.mymathlab.com and log in. Once

you are inside the course:

1. Click on the TAKE A TEST tab. 2. Click on the name of the Test that you wish to do. 3. You will be taken to a screen reminding you of the time limit and the number of attempts you

have remaining. To begin, click “I am ready to start.”

4. You may jump from one question to another using the buttons at the bottom. DO NOT click the Back arrow on your Browser window. If you leave the test page, the software will assume

you are finished (meaning you would receive zeros for any unanswered problems). 5. The number of questions left to answer as well as the time remaining to complete will be

displayed in the panel at the right hand side. 6. When you are finished with all of the questions, use the buttons at the top to go back and review

your work. Use your time wisely and make sure that you are satisfied with your answers. 7. When you are completely sure that you are finished, click SUBMIT TEST. Once you submit, your

score will appear on the screen and will also go into the MyMathLab Gradebook. 8. After you have completed a Test, you have the option to Review it. Any time after taking a test,

you can go to your Gradebook inside MyMathLab and click the Review button next to the test name. This allows you to see the correct answers. Point your mouse to that answer to see a pop up containing the solution you entered for that problem.

TESTING CENTER: They are located in room E112. They are open MTWR 7:30am-10pm; F 7:30 – 4:45pm; S

8:00am – 2:00pm. The phone number is 812-298-2258 or 1-800-377-4882 x 2258. You must arrive at

least one hour prior before the Testing Center closes in order to start a test. Be sure to bring a

form of photo identification with you to the Testing Center. No appointment necessary. Do NOT wear a

hoodie or any type of clothing with a hood into the Testing Center. If you wear a hoodie into the Testing

Center, you will be asked to remove it. If you do not, then you will not be able to take your test.

CLASSROOM BEHAVIOR: Our classroom should be a positive learning environment. When we work together,

we can all succeed! Therefore, behavior that infringes upon a classmate’s ability to receive instruction will not

be tolerated. Such behaviors may include (but are not limited to) talking without permission, disrespectful

comments, or inappropriate use of a computer, cell phone or other technology. If a classmate is disturbing your

learning opportunity, please notify the instructor.

Switch cell phones to silent mode and put them out of sight before entering the classroom. Students should not participate in sending or receiving text messages or participate in online activities or social

media during class time. Only in an extreme circumstance should a call or text be answered during our class

time. If you have such a situation arise that cannot wait until the end of the class, please gather your belongings

and answer your call or text AFTER leaving the room. In order to limit the distractions to your classmates,

return only at a break in the instruction.

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Unless directly related to course activities, electronic devices should not be heard or seen within

the classroom. Many electronic gadgets can be helpful academic tools as well. For example, devices with

a calendar service can organize your deadlines. Also, there are many “Apps” available related to our course

content. However, there will rarely be a need to use such items during class time. Unless given special

permission, please use your electronics before or after class time.

CREATE YOUR MYMATHLAB ACCOUNT:

When you purchased your textbook from the bookstore, you received a MyMathLab access code. This string

of letters and numbers is needed only one time - the very first time that you visit the site. During that visit you

will create your own username and password that will be used for all future visits.

Using a computer with internet access, go to: www.mymathlab.com. On the right hand side, under

STUDENTS, click the “register” button. Then follow the on screen directions. To register, you will need:

1. The access code under the pull tab of the packet which came with your textbook

2. Our course code: grossman49537 This is the only time you will be asked for this

code.

3. Your Email address (use one that you use regularly. It is needed if you forget your password)

4. Midwestern Community College’s zip code: XXXXX

TECHNOLOGY NEEDS FOR USING MYMATHLAB:

Anytime you want to access MyMathLab, point your internet web browser to: http://www.mymathlab.com/.

In order to run applications with MyMathLab, your computer must meet certain requirements and have certain

components downloaded onto it. For this reason, you may NOT be able to access MyMathLab from every

computer. For example, public computer labs often have a block on downloading software to the machine.

Please keep this in mind and plan ahead as needed to complete your assignments during the semester. Most

Midwestern Community College computers already have these downloads completed so they are ready for your

use.

When you log into MyMathLab for the first time, run the MyMathLab Browser Check to prepare your

computer. Repeat the process on all computers that you might be using during the semester.

USEFUL MML RESOURCES

Before you can begin to earn points through the MML assignments, you must understand how the software

expects answers to be entered. In the announcements on the first page of your MML course, you will see a link

to learn How to Enter Answers Using the MathXL Player. Click on that title to start the tour.

Inside MML there are many helpful resources. Successful students will use the site for more than just

completing homework.

On the left side of the page, I suggest exploring the areas found using the buttons called Multimedia Library

and Study Plan. The Multimedia Library is where you will find helpful videos and PowerPoints to

accompany your text. The Study Plan is an optional guide to learning the sections. The data here will be

updated based on your performance on Quizzes and any optional practice that you do.

MML TECH SUPPORT At any time you need technical assistance with MyMathLab, contact the publisher’s Technical Support. I can

help with the math, but not with your computer settings or other issues. Let the company help you free of

charge:

ONLINE: Log into http://www.mymathlab.com . Click Help & Support in the top right corner of the page.

This is available 24 hours a day. BY PHONE: Call 1-800-677-6337. Staff is available Monday-Friday, from

noon to 8 p.m.

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Course Calendar*

Module One

Activity Target Date Final Deadline Check-Off How to Type in Answers into MML January 9 January 10

Module 1 Pre-Test January 10 January 14

Module 1 Multimedia Homework January 13 January 14

Module 1 Homework 1 January 16 January 21

Module 1 Homework 2 January 18 January 21

Module 1 Homework 3 January 19 January 21

Module 1 Homework 4 January 21 January 28

Module 1 Practice Test January 22 January 28

Module 1 Post-Test January 25 January 28

Module Two

Activity Target Date Final Deadline Check-Off Module 2 Pre-Test January 28 February 4

Module 2 Multimedia Homework January 31 February 4

Module 2 Homework 1 February 2 February 4

Module 2 Homework 2 February 4 February 11

Module 2 Homework 3 February 6 February 11

Module 2 Practice Test February 7 February 18

Module 2 Post-Test February 11 February 18

Module Three

Activity Target Date Final Deadline Check-Off Practice Plotting Points and

Graphing

February 14 February 25

Module 3 Pre-Test February 15 February 25

Module 3 Multimedia Homework February 18 March 3

Module 3 Homework 1 February 20 March 3

Module 3 Homework 2 February 22 March 3

Module 3 Homework 3 February 24 March 17

Module 3 Homework 4 February 26 March 17

Module 3 Practice Test February 27 March 24

Module 3 Post-Test March 1 March 24

Module Four

Activity Target Date Final Deadline Check-Off Module 4 Pre-Test March 4 March 31

Module 4 Multimedia Homework March 13 March 31

Module 4 Homework 1 March 15 March 31

Module 4 Homework 2 March 17 April 7

Module 4 Homework 3 March 19 April 7

Module 4 Homework 4 March 21 April 7

Module 4 Practice Test March 22 April 14

Module 4 Post-Test March 26 April 14

Module Five

Activity Target Date Final Deadline Check-Off Module 5 Pre-Test March 29 April 14

Module 5 Multimedia Homework April 1 April 21

Module 5 Homework 1 April 3 April 21

Module 5 Homework 2 April 5 April 21

Module 5 Practice Test April 6 April 28

Module 5 Post-Test (paper-pencil) April 9 April 30

57

NOTE: All work must be completed by April 30, 2012.* NOTE: The schedule and procedures in this course are subject to change. The instructor and/or the College reserve the right to change any statements, policies or scheduling as necessary. Students will be informed promptly of any and all changes.

58

C. Attitude Survey Data

Tutorial Class Attitude Survey Data

1. Are you satisfied with the Asas 007 course content, neither satisfied nor dissatisfied with it, or

dissatisfied with it?

Extremely satisfied

Midterm

25.0%

2

Final

12.5%

1

Change

-12.5%

% Change

-50.0%

Moderately satisfied 37.5% 3 37.5% 3 0.0% 0.0%

Slightly satisfied 12.5% 1 37.5% 3 25.0% 200.0%

Neither satisfied nor 0.0% 0 12.5% 1 12.5% #DIV/0!

Slightly dissatisfied 25.0% 2 0.0% 0 -25.0% -100.0%

Moderately 0.0% 0 0.0% 0 0.0% #DIV/0!

Extremely dissatisfied 0.0% 0 0.0% 0 0.0% #DIV/0!

2. How much time do you spend studying and preparing, on average, for Asas 007 each week?

1-4 hours

Midterm

25.0%

2

Final

12.5%

1

Difference

-12.5%

% Change

-50.00%

5-8 hours 37.5% 3 62.5% 5 25.0% 66.67%

9-12 hours 12.5% 1 25.0% 2 12.5% 100.00%

13-16 hours 12.5% 1 0.0% 0 -12.5% -100.00%

17+ hours 12.5% 1 0.0% 0 -12.5% -100.00%

3. How difficult is Asas 007?

Extremely difficult

Midterm

0.0%

0

Final

25.0%

2

Change

25.0%

% Change

#DIV/0!

Moderately difficult 75.0% 6 37.5% 3 -37.5% -50.00%

Slightly difficult 25.0% 2 25.0% 2 0.0% 0.00%

Neither difficult nor 0.0% 0 12.5% 1 12.5% #DIV/0!

Slightly easy 0.0% 0 0.0% 0 0.0% #DIV/0!

Moderately easy 0.0% 0 0.0% 0 0.0% #DIV/0!

Extremely easy 0.0% 0 0.0% 0 0.0% #DIV/0!

4. Compared to your other classes, the time you put into Asas 007 is:

a lot more time

Midterm

62.5%

5

Final

37.5%

3

Change

-25.0%

% Change

60.00%

just a little more time 12.5% 1 25.0% 2 12.5% 200.00%

about the same 25.0% 2 25.0% 2 0.0% 100.00%

just little less time 0.0% 0 12.5% 1 12.5% #DIV/0!

a lot less time 0.0% 0 0.0% 0 0.0% #DIV/0!

59

5. How helpful is your Asas 007 instructor?

Extremely helpful

Midterm

50.0%

4

Final

50.0%

4

Change

0.0%

% Change

0.00%

Very helpful 25.0% 2 50.0% 4 25.0% 100.00%

Moderately helpful 25.0% 2 0.0% 0 -25.0% -100.00%

Slightly helpful 0.0% 0 0.0% 0 0.0% #DIV/0!

Not at all helpful 0.0% 0 0.0% 0 0.0% #DIV/0!

6. How easy is it to receive help from other resources besides your Asas 007 instructor?

Midterm Final Change % Change

Extremely easy 25.0% 2 25.0% 2 0.0% 0.00%

Very easy 37.5% 3 37.5% 3 0.0% 0.00%

Moderately easy 25.0% 2 37.5% 3 12.5% 50.00%

Slightly easy 12.5% 1 0.0% 0 -12.5% -100.00%

7. How useful are the Asas 007 MyMathLab multimedia assignments in helping you understand the

material?

Extremely useful

Midterm

25.0%

2

Final

12.5%

1

Change

-12.5%

% Change

-50.00%

Very useful 50.0% 4 62.5% 5 12.5% 25.00%

Moderately useful 25.0% 2 25.0% 2 0.0% 0.00%

Slightly useful 0.0% 0 0.0% 0 0.0% #DIV/0!

Not at all useful 0.0% 0 0.0% 0 0.0% #DIV/0!

8. How useful are the Asas 007 MyMathLab homework assignments in helping you understand the

material?

Extremely useful

Midterm

0.0%

0

Final

0.0%

0

Change

0.0%

% Change

#DIV/0!

Very useful 62.5% 5 62.5% 5 0.0% 0.00%

Moderately useful 25.0% 2 37.5% 3 12.5% 50.00%

Slightly useful 12.5% 1 0.0% 0 -12.5% -100.00%

Not at all useful 0.0% 0 0.0% 0 0.0% #DIV/0!

9. How helpful is Asas 007 in your overall learning of Math 118 content?

Extremely helpful

Midterm

12.5%

1

Final

0.0%

0

Change

-12.5%

% Change

-100.00%

Very helpful 25.0% 2 50.0% 4 25.0% 100.00%

Moderately helpful 25.0% 2 50.0% 4 25.0% 100.00%

Slightly helpful 25.0% 2 0.0% 0 -25.0% -100.00%

Not at all helpful 12.5% 1 0.0% 0 -12.5% -100.00%

60

Concepts in Mathematics Attitude Survey Data – Concurrently Enrolled in Tutorial Class

1. Are you satisfied with the Math 118 course content, neither satisfied nor dissatisfied with it, or

dissatisfied with it?

Extremely satisfied

Midterm

22.2%

2

Final

0.0%

0

Difference

-22.2%

% Change

-100.00%

Moderately satisfied 55.6% 5 50.0% 5 -5.6% -10.07%

Slightly satisfied 22.2% 2 10.0% 1 -12.2% -54.95%

Neither satisfied nor 0.0% 0 20.0% 2 20.0% #DIV/0!

Slightly dissatisfied 0.0% 0 10.0% 1 10.0% #DIV/0!

Moderately 0.0% 0 0.0% 0 0.0% #DIV/0!

Extremely dissatisfied 0.0% 0 10.0% 1 10.0% #DIV/0!

2. How much time do you spend studying and preparing, on average, for Math 118 each week?

1-4 hours

Midterm

22.2%

2

Final

10.0%

1

Difference

-12.2%

% Change

-54.95%

5-8 hours 33.3% 3 60.0% 6 26.7% 80.18%

9-12 hours 33.3% 3 20.0% 2 -13.3% -39.94%

13-16 hours 11.1% 1 0.0% 0 -11.1% -100.00%

17+ hours 0.0% 0 10.0% 1 10.0% #DIV/0!

3. How difficult is Math 118?

Extremely difficult

Midterm

22.2%

2

Final

30.0%

3

Difference

7.8%

% Change

35.14%

Moderately difficult 44.4% 4 60.0% 6 15.6% 35.14%

Slightly difficult 33.3% 3 10.0% 1 -23.3% -69.97%

Neither difficult nor 0.0% 0 0.0% 0 0.0% #DIV/0!

Slightly easy 0.0% 0 0.0% 0 0.0% #DIV/0!

Moderately easy 0.0% 0 0.0% 0 0.0% #DIV/0!

Extremely easy 0.0% 0 0.0% 0 0.0% #DIV/0!

4. Compared to your other classes, the time you put into Math 118 is:

a lot more time

Midterm

55.6%

5

Final

60.0%

6

Difference

4.4%

% Change

7.91%

just a little more time 11.1% 1 20.0% 2 8.9% 80.18%

about the same 22.2% 2 20.0% 2 -2.2% -9.91%

just little less time 11.1% 1 0.0% 0 -11.1% -100.00%

a lot less time 0.0% 0 0.0% 0 0.0% #DIV/0!

5. How helpful is your Math 118 instructor?

Extremely helpful

Midterm

66.7%

6

Final

40.0%

4

Difference

-26.7%

% Change

-40.03%

Very helpful 33.3% 3 40.0% 4 6.7% 20.12%

Moderately helpful 0.0% 0 20.0% 2 20.0% #DIV/0!

Slightly helpful 0.0% 0 0.0% 0 0.0% #DIV/0!

Not at all helpful 0.0% 0 0.0% 0 0.0% #DIV/0!

61

6. How easy is it to receive help from other resources besides your Math 118 instructor?

Extremely easy

Midterm

11.1%

1

Final

10.0%

1

Difference

-1.1%

% Change

-9.91%

Very easy 66.7% 6 60.0% 6 -6.7% -10.04%

Moderately easy 22.2% 2 30.0% 3 7.8% 35.14%

Slightly easy 0.0% 0 0.0% 0 0.0% #DIV/0!

Not at all easy 0.0% 0 0.0% 0 0.0% #DIV/0!

7. How useful are the Math 118 MyMathLab assignments in helping you understand the material?

Midterm Final Difference % Change

Extremely useful 11.1% 1 0.0% 0 -11.1% -100.00%

Very useful 66.7% 6 40.0% 4 -26.7% -40.03%

Moderately useful 22.2% 2 50.0% 5 27.8% 125.23%

Slightly useful 0.0% 0 10.0% 1 10.0% #DIV/0!

Not at all useful 0.0% 0 0.0% 0 0.0% #DIV/0!

8. How prepared do you feel after completing your Math 118 MyMathLab assignments?

Extremely satisfied

Midterm

11.1%

1

Final

0.0%

0

Difference

-11.1%

% Change

-100.00%

Moderately satisfied 44.4% 4 60.0% 6 15.6% 35.14%

Slightly satisfied 44.4% 4 20.0% 2 -24.4% -54.95%

Neither satisfied nor 0.0% 0 20.0% 2 20.0% #DIV/0!

Slightly dissatisfied 0.0% 0 0.0% 0 0.0% #DIV/0!

Moderately 0.0% 0 0.0% 0 0.0% #DIV/0!

Extremely dissatisfied 0.0% 0 0.0% 0 0.0% #DIV/0!

9. What grade do you expect to earn in Math 118?

A

Midterm

11.1%

1

Final

0.0%

0

Difference

-11.1%

%Change

-100.0%

B 33.3% 3 10.0% 1 -23.3% -70.0%

C 44.4% 4 50.0% 5 5.6% 12.6%

D 11.1% 1 10.0% 1 -1.1% -9.9%

F 0.0% 0 30.0% 3 30.0% #DIV/0!

62

Extremely difficult

Midterm

11.8%

4

Final

8.0%

2

Difference

-3.8%

% Change -32.20%

Moderately difficult 35.3% 12 40.0% 10 4.7% 13.31%

Slightly difficult 29.4% 10 36.0% 9 6.6% 22.45%

Neither difficult nor 11.8% 4 8.0% 2 -3.8% -32.20%

Slightly easy 2.9% 1 8.0% 2 5.1% 175.86%

Moderately easy 8.8% 3 0.0% 0 -8.8% -100.00%

Concepts in Mathematics Attitude Survey Data – NOT Concurrently Enrolled In

Tutorial Class

1. Are you satisfied with the Math 118 course content, neither satisfied nor dissatisfied with it, or

dissatisfied with it?

Extremely satisfied

Midterm

14.7%

5

Final

32.0%

8

Difference % Change

17.3% 117.69%

Moderately satisfied 32.4% 11 36.0% 9 3.6% 11.11%

Slightly satisfied 11.8% 4 12.0% 3 0.2% 1.69%

Neither satisfied nor 32.4% 11 12.0% 3 -20.4% -62.96%

Slightly dissatisfied 5.9% 2 0.0% 0 -5.9% -100.00%

Moderately 0.0% 0 4.0% 1 4.0% #DIV/0!

Extremely dissatisfied 2.9% 1 4.0% 1 1.1% 37.93%

2. How much time do you spend studying and preparing, on average, for Math 118 each week?

1-4 hours

Midterm

32.4%

11

Final

40.0%

10

Difference % Change

7.6% 23.46%

5-8 hours 52.9% 18 36.0% 9 -16.9% -31.95%

9-12 hours 14.7% 5 20.0% 5 5.3% 36.05%

13-16 hours 0.0% 0 0.0% 0 0.0% #DIV/0! 17+ hours 0.0% 0 4.0% 1 4.0% #DIV/0!

3. How difficult is Math 118?

4. Compared to your other classes, the time you put into Math 118 is:

a lot more time

Midterm

32.4%

11

Final

44.0%

11

Difference

11.6%

% Change 35.80%

just a little more time 29.4% 10 24.0% 6 -5.4% -18.37%

about the same 26.5% 9 32.0% 8 5.5% 20.75%

just little less time 5.9% 2 0.0% 0 -5.9% -100.00%

a lot less time 5.9% 2 0.0% 0 -5.9% -100.00%

5. How helpful is your Math 118 instructor?

Extremely helpful

Midterm

61.8%

21

Final

48.0%

12

Difference

-13.8%

% Change -22.33%

Very helpful 26.5% 9 40.0% 10 13.5% 50.94%

Moderately helpful 5.9% 2 8.0% 2 2.1% 35.59%

Slightly helpful 2.9% 1 0.0% 0 -2.9% -100.00%

Not at all helpful 2.9% 1 4.0% 1 1.1% 37.93%

63

6. How easy is it to receive help from other resources besides your Math 118 instructor?

Midterm Final Difference % Change Extremely easy 11.8% 4 24.0% 6 12.2% 103.39%

Very easy 47.1% 16 36.0% 9 -11.1% -23.57%

Moderately easy 32.4% 11 28.0% 7 -4.4% -13.58%

Slightly easy 5.9% 2 4.0% 1 -1.9% -32.20%

7. How useful are the Math 118 MyMathLab assignments in helping you understand the material?

Extremely useful

MiMidterm

32.4% 11

Final

24.0%

6

Difference % Change

-8.4%

-25.93%

Very useful 32.4% 11 44.0% 11 11.6% 35.80%

Moderately useful 26.5% 9 24.0% 6 -2.5% -9.43%

Slightly useful 5.9% 2 0.0% 0 -5.9% -100.00%

Not at all useful 2.9% 1 8.0% 2 5.1% 175.86%

8. How prepared do you feel after completing your Math 118 MyMathLab assignments?

Extremely satisfied

Midterm

17.6%

6

Final

12.0%

3

Difference % Change

-5.6% -31.82%

Moderately satisfied 41.2% 14 48.0% 12 6.8% 16.50%

Slightly satisfied 26.5% 9 24.0% 6 -2.5% -9.43%

Neither satisfied nor 2.9% 1 12.0% 3 9.1% 313.79%

Slightly dissatisfied 8.8% 3 4.0% 1 -4.8% -54.55%

Moderately 0.0% 0 0.0% 0 0.0% #DIV/0!

Extremely dissatisfied 2.9% 1 0.0% 0 -2.9% -100.00%

9. What grade do you expect to earn in Math 118?

A

MiMidterm

1717.6%

6

Final

12.0%

3

Difference

-5.6%

% Change

-31.818181818181800%

B 44.1% 15 36.0% 9 -8.1% -18.367346938775500%

C 38.2% 13 48.0% 12 9.8% 25.654450261780100%

D 0.0% 0 4.0% 1 4.0% #DIV/0!

F 0.0% 0 0.0% 0 0.0% #DIV/0!

64

D. SPSS Output

Table 1: Group Comparisons by Tutorial Enrollment

65

Hypothesis Test Summary

Null Hypothesis Test Sig.

Decision

1

The distribution of NumRemedial is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

2

The distribution of Num118Attempts is the same

Independent-

1

Retain the null

Samples Mann-

across categories of CoReq. Whitney U hypothesis. Test

3

The distribution of NumCredits is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

4

The distribution of Fall2011GPA is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

5

The distribution of GPACUMFall2011 is the same

Independent-

1

Retain the null

Samples Mann-

across categories of CoReq. Whitney U hypothesis. Test

6

The distribution of Spring2012GPA is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

7

The distribution of GPACUMSpring2012 is the same

Independent-

1

Retain the null

Samples Mann-

across categories of CoReq. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

(continued)

66

Hypothesis Test Summary

Null Hypothesis

Test Sig.

Decision

8

The distribution of COMPASSPreAlg is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .064

Retain the null hypothesis.

9

The distribution of COMPASSAlg is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1

Reject the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 2: Group Comparisons by Section

67

Hypothesis Test Summary

Null Hypothesis Test Sig.

Decision

1

The distribution of NumRemedial is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.927

Retain the null hypothesis.

2

The distribution of Num118Attempts is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.241

Retain the null hypothesis.

3

The distribution of NumCredits is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.154

Retain the null hypothesis.

4

The distribution of Fall2011GPA is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.507

Retain the null hypothesis.

5

The distribution of GPACUMFall2011 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.955

Retain the null hypothesis.

6

The distribution of Spring2012GPA is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.139

Retain the null hypothesis.

7

The distribution of GPACUMSpring2012 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.852

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

68

Hypothesis Test Summary

Null Hypothesis

Test Sig.

Decision

8

The distribution of COMPASSPreAlg is the same

Independent-

1

Retain the null

Samples Mann-

across categories of Section. Whitney U hypothesis. Test

9

The distribution of COMPASSAlg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 3: Group Comparisons by Gender

69

Hypothesis Test Summary

Null Hypothesis Test

Sig. Decision

1

The distribution of NumRemedial is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

2

The distribution of Num118Attempts is the same

Independent-

1

Retain the null

Samples Mann-

across categories of Gender. Whitney U hypothesis. Test

3

The distribution of NumCredits is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

4

The distribution of Fall2011GPA is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

5

The distribution of GPACUMFall2011 is the same

Independent-

1

Retain the null

Samples Mann-

across categories of Gender. Whitney U hypothesis. Test

6

The distribution of Spring2012GPA is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

7

The distribution of GPACUMSpring2012 is the same

Independent-

1

Retain the null

Samples Mann-

across categories of Gender. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

70

Hypothesis Test Summary

Null Hypothesis

Test Sig.

Decision

8

The distribution of COMPASSPreAlg is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .064

Retain the null hypothesis.

9

The distribution of COMPASSAlg is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1

Reject the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 4: Coursework Comparisons by Tutorial Enrollment (zeros included)

71

7

8

Hypothesis Test Summary

Null Hypothesis

Test

Sig.

Decision

The distribution of MMLHwAvg is

1 the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .407

Retain the null hypothesis.

The distribution of MMLQzAvg is

2 the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .053

Retain the null hypothesis.

The distribution of ProbActAvg is

3 the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .763

Retain the null hypothesis.

4 The distribution of Test1 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .108

Retain the null hypothesis.

The distribution of Test2 is the

5 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .038

Reject the null hypothesis.

6 The distribution of Test3 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .096

Retain the null hypothesis.

The distribution of Test4 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .286

Retain the null hypothesis.

The distribution of Final is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .149

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

72

Null Hypothesis

Test Sig.

Decision

9

The distribution of PercentPresent is the same

Independent-

1

Retain the null

Samples Mann-

across categories of CoReq. Whitney U hypothesis. Test

10

The distribution of CourseGrade is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 5: Coursework Comparisons by Section (zeros included)

73

Null Hypothesis

Test

Sig.

Decision

The distribution of MMLHwAvg is

1 the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.501

Retain the null hypothesis.

The distribution of MMLQzAvg is

2 the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.337

Retain the null hypothesis.

The distribution of ProbActAvg is

3 the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.286

Retain the null hypothesis.

The distribution of Test1 is the

4 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.029

Reject the null hypothesis.

The distribution of Test2 is the

5 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.809

Retain the null hypothesis.

The distribution of Test3 is the

6 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.311

Retain the null hypothesis.

The distribution of Test4 is the

7 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.134

Retain the null hypothesis.

The distribution of Final is the

8 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.164

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

74

Null Hypothesis Test

Sig.

Decision

9

The distribution of PercentPresent is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.635

Retain the null hypothesis.

10

The distribution of CourseGrade is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.144

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 6: Coursework Comparisons by Gender (zeros included) [DataSet1] C:\Users\Owner\Documents\My Documents\Classes\MATH\Proseminar Stuf

f\Grades, Attempts, GPA and Test Scores MERGED.sav

75

Hypothesis Test Summary

Null Hypothesis

Test

Sig.

Decision

The distribution of MMLHwAvg is

1 the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .407

Retain the null hypothesis.

The distribution of MMLQzAvg is

2 the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .053

Retain the null hypothesis.

The distribution of ProbActAvg is

3 the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .763

Retain the null hypothesis.

The distribution of Test1 is the

4 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .108

Retain the null hypothesis.

The distribution of Test2 is the

5 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .038

Reject the null hypothesis.

The distribution of Test3 is the

6 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .096

Retain the null hypothesis.

The distribution of Test4 is the

7 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .286

Retain the null hypothesis.

The distribution of Final is the

8 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .149

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

76

Hypothesis Test Summary

Null Hypothesis

Test Sig.

Decision

9

The distribution of PercentPresent is the same

Independent-

1

Retain the null

Samples Mann-

across categories of Gender. Whitney U hypothesis. Test

10

The distribution of CourseGrade is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 7: Coursework Comparisons by Tutorial Enrollment (zeros excluded)

77

Null Hypothesis Test Sig.

Decision

1

The distribution of MMLHwAvg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

2

The distribution of MMLQzAvg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

3

The distribution of ProbActAvg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

4

The distribution of Test1 is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

5

The distribution of Test2 is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

6

The distribution of Test3 is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

7

The distribution of Test4 is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

8

Hypothesis Test Summary

The distribution of Final is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .922

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

78

Hypothesis Test Summary

Null Hypothesis

Test

Sig. Decision

9

The distribution of PercentPresent is the same

Independent

1

Retain the null

-Samples Mann-

across categories of CoReq. Whitney U hypothesis. Test

10

The distribution of CourseGrade is the same across categories of

Independent

1

Retain the null

-Samples Mann-

CoReq. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 8: Coursework Comparisons by Section (zeros excluded)

79

Hypothesis Test Summary

Null Hypothesis

Test

Sig.

Decision

The distribution of MMLHwAvg

1 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .885

Retain the null hypothesis.

The distribution of MMLQzAvg

2 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .908

Retain the null hypothesis.

The distribution of ProbActAvg

3 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

The distribution of Test1 is the

4 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .281

Retain the null hypothesis.

The distribution of Test2 is the

5 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .622

Retain the null hypothesis.

The distribution of Test3 is the

6 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .862

Retain the null hypothesis.

The distribution of Test4 is the

7 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .523

Retain the null hypothesis.

The distribution of Final is the

8 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .622

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

80

Hypothesis Test Summary

Null Hypothesis

Test

Sig.

Decision

9

The distribution of PercentPresent is the same

Independent

1

Retain the null

-Samples Mann-

across categories of Section. Whitney U hypothesis. Test

10

The distribution of CourseGrade is the same across categories of

Independent

1

Retain the null

-Samples Mann-

Section. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 9: Coursework Comparisons by Gender (zeros excluded)

81

Null Hypothesis Test

Sig. Decision

1

The distribution of MMLHwAvg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

2

The distribution of MMLQzAvg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

3

The distribution of ProbActAvg is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

4

The distribution of Test1 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

5

The distribution of Test2 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

6

The distribution of Test3 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

7

The distribution of Test4 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

8

Hypothesis Test Summary

The distribution of Final is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .922

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

82

Hypothesis Test Summary

Null Hypothesis

Test

Sig.

Decision

9

The distribution of PercentPresent is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

10

The distribution of CourseGrade is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 10 Chi-Square AttendanceRating vs Test1

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test1Letter 44 100.0% 0 0.0% 44 100.0%

83

AttendanceRating * Test1Letter Crosstabulation

Test1Letter

F D C B A

AttendanceRating Low Count 1 0 0 1 1

Expected Count 1.3 .5 .3 .7 .3

Average Count

Expected Count

4

5.2

4

1.9

0

1.1

1

2.7

3

1.1

High Count 14 3 4 8 0

Expected Count 12.5 4.6 2.6 6.6 2.6

Total Count 19 7 4 10 4

Expected Count 19.0 7.0 4.0 10.0 4.0

AttendanceRating * Test1Letter Crosstabulation

Total

AttendanceRating Low Count

Expected Count

3

3.0

Average Count

Expected Count

12

12.0

High Count

Expected Count

29

29.0

Total Count 44

Expected Count 44.0

Chi-Square Tests

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 15.372a 8 .052

Likelihood Ratio 17.731 8 .023

N of Valid Cases 44

a. 12 cells (80.0%) have expected count less than 5. The minimum expected count is .27.

Table 11 Chi-Square AttendanceRating vs Test2

84

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test2Letter 41 100.0% 0 0.0% 41 100.0%

AttendanceRating * Test2Letter Crosstabulation

Test2Letter

Total F D C A

AttendanceRating Low Count 1 0 0 1 2

Expected Count 1.2 .4 .2 .1 2.0

Average Count

Expected Count

5

7.3

3

2.6

2

1.2

2

.9

12

12.0

High Count 19 6 2 0 27

Expected Count 16.5 5.9 2.6 2.0 27.0

Total Count 25 9 4 3 41

Expected Count 25.0 9.0 4.0 3.0 41.0

Chi-Square Tests

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 10.979a 6 .089

Likelihood Ratio 10.345 6 .111

N of Valid Cases 41

a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .15.

Table 12 Chi-Square AttendanceRating vs Test3

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test3Letter

39 100.0% 0 0.0% 39 100.0%

85

AttendanceRating * Test3Letter Crosstabulation

Test3Letter

F D C B A

AttendanceRating Low Count 1 0 1 1 0

Expected Count .9 .3 .9 .5 .3

Average Count

Expected Count

3

3.4

0

1.1

4

3.4

1

2.0

3

1.1

High Count 8 4 7 5 1

Expected Count 7.7 2.6 7.7 4.5 2.6

Total Count 12 4 12 7 4

Expected Count 12.0 4.0 12.0 7.0 4.0

AttendanceRating * Test3Letter Crosstabulation

Total

AttendanceRating Low Count

Expected Count

3

3.0

Average Count

Expected Count

11

11.0

High Count

Expected Count

25

25.0

Total Count 39

Expected Count 39.0

Chi-Square Tests

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 7.785a 8 .455

Likelihood Ratio 8.742 8 .365

N of Valid Cases 39

a. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .31.

Table 13 Chi-Square AttendanceRating vs Test4

86

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test4Letter 38 100.0% 0 0.0% 38 100.0%

AttendanceRating * Test4Letter Crosstabulation

Test4Letter

F D C B A

AttendanceRating Low Count 0 0 1 0 1

Expected Count .8 .4 .6 .2 .1

Average Count

Expected Count

6

4.3

1

2.0

4

3.2

0

1.2

0

.3

High Count 9 6 6 4 0

Expected Count 9.9 4.6 7.2 2.6 .7

Total Count 15 7 11 4 1

Expected Count 15.0 7.0 11.0 4.0 1.0

AttendanceRating * Test4Letter Crosstabulation

Total

AttendanceRating Low Count

Expected Count

2

2.0

Average Count

Expected Count

11

11.0

High Count

Expected Count

25

25.0

Total Count 38

Expected Count 38.0

Chi-Square Tests

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 23.616a 8 .003

Likelihood Ratio 13.892 8 .085

N of Valid Cases 38

a. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .05.

Table 14 Chi-Square AttendanceRating vs Final

87

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

FinalLetter 38 100.0% 0 0.0% 38 100.0%

AttendanceRating * FinalLetter Crosstabulation

FinalLetter

F D C B A

AttendanceRating Low Count 0 0 1 0 1

Expected Count .6 .2 .9 .2 .2

Average Count

Expected Count

3

3.2

1

1.2

5

4.9

1

.9

1

.9

High Count 8 3 11 2 1

Expected Count 7.2 2.6 11.2 2.0 2.0

Total Count 11 4 17 3 3

Expected Count 11.0 4.0 17.0 3.0 3.0

AttendanceRating * FinalLetter Crosstabulation

Total

AttendanceRating Low Count

Expected Count

2

2.0

Average Count

Expected Count

11

11.0

High Count

Expected Count

25

25.0

Total Count 38

Expected Count 38.0

Chi-Square Tests

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 6.140a 8 .632

Likelihood Ratio 4.705 8 .789

N of Valid Cases 38

a. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .16.

88

Table 15 Chi-Square AttendanceRating vs CourseGrade

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

CourseGradeLetter 38 100.0% 0 0.0% 38 100.0%

AttendanceRating * CourseGradeLetter Crosstabulation

CourseGradeLetter

F D C B A

AttendanceRating Low Count 0 0 1 0 1

Expected Count .4 .5 .4 .6 .1

Average Count

Expected Count

2

2.3

2

2.6

2

2.3

4

3.2

1

.6

High Count 6 7 5 7 0

Expected Count 5.3 5.9 5.3 7.2 1.3

Total Count 8 9 8 11 2

Expected Count 8.0 9.0 8.0 11.0 2.0

AttendanceRating * CourseGradeLetter Crosstabulation

Total

AttendanceRating Low Count

Expected Count

2

2.0

Average Count

Expected Count

11

11.0

High Count

Expected Count

25

25.0

Total Count 38

Expected Count 38.0

89

Chi-Square Tests

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 12.253a 8 .140

Likelihood Ratio 9.857 8 .275

N of Valid Cases 38

a. 11 cells (73.3%) have expected count less than 5. The minimum expected count is .11.

Table 16 Chi-Square AttendanceRating vs Test1 by Tutorial Class Enrollment

CoReq = No

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test1Letter 35 100.0% 0 0.0% 35 100.0%

a. CoReq = No

AttendanceRating * Test1Letter Crosstabulationa

Test1Letter

F D C B A

AttendanceRating Low Count 1 0 0 1 1

Expected Count 1.2 .6 .2 .7 .3

Average Count

Expected Count

2

4.0

4

2.0

0

.6

1

2.3

3

1.1

High Count 11 3 2 6 0

Expected Count 8.8 4.4 1.3 5.0 2.5

Total Count 14 7 2 8 4

Expected Count 14.0 7.0 2.0 8.0 4.0

90

AttendanceRating * Test1Letter Crosstabulationa

Total

AttendanceRating Low Count

Expected Count

3

3.0

Average Count

Expected Count

10

10.0

High Count

Expected Count

22

22.0

Total Count 35

Expected Count 35.0

a. CoReq = No

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 13.657b 8 .091

Likelihood Ratio 16.028 8 .042

N of Valid Cases 35

a. CoReq = No

b. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .17.

CoReq = Yes

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test1Letter 9 100.0% 0 0.0% 9 100.0%

a. CoReq = Yes

91

AttendanceRating * Test1Letter Crosstabulationa

Test1Letter

Total F C B

AttendanceRating Average Count 2 0 0 2

Expected Count 1.1 .4 .4 2.0

High Count 3 2 2 7

Expected Count 3.9 1.6 1.6 7.0

Total Count 5 2 2 9

Expected Count 5.0 2.0 2.0 9.0

a. CoReq = Yes

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 2.057b 2 .358

Likelihood Ratio 2.805 2 .246

N of Valid Cases 9

a. CoReq = Yes

b. 6 cells (100.0%) have expected count less than 5. The minimum expected count is .44.

Table 17 Chi-Square AttendanceRating vs Test2 by Tutorial Class Enrollment

CoReq = No

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test2Letter 33 100.0% 0 0.0% 33 100.0%

a. CoReq = No

92

AttendanceRating * Test2Letter Crosstabulationa

Test2Letter

Total F D C A

AttendanceRating Low Count 1 0 0 1 2

Expected Count 1.1 .5 .2 .2 2.0

Average Count

Expected Count

3

5.5

3

2.4

2

1.2

2

.9

10

10.0

High Count 14 5 2 0 21

Expected Count 11.5 5.1 2.5 1.9 21.0

Total Count 18 8 4 3 33

Expected Count 18.0 8.0 4.0 3.0 33.0

a. CoReq = No

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 10.072b 6 .122

Likelihood Ratio 10.558 6 .103

N of Valid Cases 33

a. CoReq = No

b. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .18.

CoReq = Yes

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test2Letter 8 100.0% 0 0.0% 8 100.0%

a. CoReq = Yes

93

AttendanceRating * Test2Letter Crosstabulationa

Test2Letter

Total F D

AttendanceRating Average Count 2 0 2

Expected Count 1.8 .3 2.0

High Count 5 1 6

Expected Count 5.3 .8 6.0

Total Count 7 1 8

Expected Count 7.0 1.0 8.0

a. CoReq = Yes

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Exact Sig. (2-

sided)

Exact Sig. (1-

sided)

Pearson Chi-Square .381b 1 .537

1.000

.750

Continuity Correctionc .000 1 1.000

Likelihood Ratio .622 1 .430

Fisher's Exact Test

N of Valid Cases 8

a. CoReq = Yes

b. 3 cells (75.0%) have expected count less than 5. The minimum expected count is .25.

c. Computed only for a 2x2 table

Table 18 Chi-Square AttendanceRating vs Test3 by Tutorial Class Enrollment

CoReq = No

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test3Letter 33 100.0% 0 0.0% 33 100.0%

a. CoReq = No

94

AttendanceRating * Test3Letter Crosstabulationa

Test3Letter

F D C B A

AttendanceRating Low Count 1 0 1 1 0

Expected Count .9 .3 1.0 .5 .4

Average Count

Expected Count

2

3.0

0

.9

4

3.3

1

1.5

3

1.2

High Count 7 3 6 3 1

Expected Count 6.1 1.8 6.7 3.0 2.4

Total Count 10 3 11 5 4

Expected Count 10.0 3.0 11.0 5.0 4.0

AttendanceRating * Test3Letter Crosstabulationa

Total

AttendanceRating Low Count

Expected Count

3

3.0

Average Count

Expected Count

10

10.0

High Count

Expected Count

20

20.0

Total Count 33

Expected Count 33.0

a. CoReq = No

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 7.323b 8 .502

Likelihood Ratio 8.097 8 .424

N of Valid Cases 33

a. CoReq = No

b. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .27.

CoReq = Yes

95

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test3Letter 6 100.0% 0 0.0% 6 100.0%

a. CoReq = Yes

AttendanceRating * Test3Letter Crosstabulationa

Test3Letter

Total F D C B

AttendanceRating Average Count 1 0 0 0 1

Expected Count .3 .2 .2 .3 1.0

High Count 1 1 1 2 5

Expected Count 1.7 .8 .8 1.7 5.0

Total Count 2 1 1 2 6

Expected Count 2.0 1.0 1.0 2.0 6.0

a. CoReq = Yes

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 2.400b 3 .494

Likelihood Ratio 2.634 3 .452

N of Valid Cases 6

a. CoReq = Yes

b. 8 cells (100.0%) have expected count less than 5. The minimum expected count is .17.

Table 19 Chi-Square AttendanceRating vs Test4 by Tutorial Class Enrollment

CoReq = No

96

AttendanceRating * Test4Letter Crosstabulationa

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test4Letter

32 100.0% 0 0.0% 32 100.0%

a. CoReq = No

Test4Letter

F D C B A

AttendanceRating Low Count 0 0 1 0 1

Expected Count .8 .3 .7 .1 .1

Average Count

Expected Count

5

4.1

1

1.6

4

3.4

0

.6

0

.3

High Count 8 4 6 2 0

Expected Count 8.1 3.1 6.9 1.3 .6

Total Count 13 5 11 2 1

Expected Count 13.0 5.0 11.0 2.0 1.0

AttendanceRating * Test4Letter Crosstabulationa

Total

AttendanceRating Low Count

Expected Count

2

2.0

Average Count

Expected Count

10

10.0

High Count

Expected Count

20

20.0

Total Count 32

Expected Count 32.0

a. CoReq = No

97

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 18.336b 8 .019

Likelihood Ratio 10.664 8 .221

N of Valid Cases 32

a. CoReq = No

b. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .06.

CoReq = Yes

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

Test4Letter 6 100.0% 0 0.0% 6 100.0%

a. CoReq = Yes

AttendanceRating * Test4Letter Crosstabulationa

Test4Letter

Total F D B

AttendanceRating Average Count 1 0 0 1

Expected Count .3 .3 .3 1.0

High Count 1 2 2 5

Expected Count 1.7 1.7 1.7 5.0

Total Count 2 2 2 6

Expected Count 2.0 2.0 2.0 6.0

a. CoReq = Yes

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 2.400b 2 .301

Likelihood Ratio 2.634 2 .268

N of Valid Cases 6

a. CoReq = Yes

b. 6 cells (100.0%) have expected count less than 5. The minimum expected count is .33.

98

Table 20 Chi-Square AttendanceRating vs Final by Tutorial Class Enrollment

CoReq = No

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

FinalLetter 32 100.0% 0 0.0% 32 100.0%

a. CoReq = No

AttendanceRating * FinalLetter Crosstabulationa

FinalLetter

F D C B A

AttendanceRating Low Count 0 0 1 0 1

Expected Count .6 .1 .9 .2 .2

Average Count

Expected Count

2

3.1

1

.6

5

4.4

1

.9

1

.9

High Count 8 1 8 2 1

Expected Count 6.3 1.3 8.8 1.9 1.9

Total Count 10 2 14 3 3

Expected Count 10.0 2.0 14.0 3.0 3.0

AttendanceRating * FinalLetter Crosstabulationa

Total

AttendanceRating Low Count

Expected Count

2

2.0

Average Count

Expected Count

10

10.0

High Count

Expected Count

20

20.0

Total Count 32

Expected Count 32.0

a. CoReq = No

99

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 6.225b 8 .622

Likelihood Ratio 5.434 8 .710

N of Valid Cases 32

a. CoReq = No

b. 13 cells (86.7%) have expected count less than 5. The minimum expected count is .13.

CoReq = Yes

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

FinalLetter 6 100.0% 0 0.0% 6 100.0%

a. CoReq = Yes

AttendanceRating * FinalLetter Crosstabulationa

FinalLetter

Total F D C

AttendanceRating Average Count 1 0 0 1

Expected Count .2 .3 .5 1.0

High Count 0 2 3 5

Expected Count .8 1.7 2.5 5.0

Total Count 1 2 3 6

Expected Count 1.0 2.0 3.0 6.0

a. CoReq = Yes

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 6.000b 2 .050

Likelihood Ratio 5.407 2 .067

N of Valid Cases 6

a. CoReq = Yes

b. 6 cells (100.0%) have expected count less than 5. The minimum expected count is .17.

100

Table 21 Chi-Square AttendanceRating vs CourseGrade by Tutorial Class Enrollment

CoReq = No

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

CourseGradeLetter 32 100.0% 0 0.0% 32 100.0%

a. CoReq = No

AttendanceRating * CourseGradeLetter Crosstabulationa

CourseGradeLetter

F D C B A

AttendanceRating Low Count 0 0 1 0 1

Expected Count .4 .4 .4 .6 .1

Average Count

Expected Count

1

2.2

2

2.2

2

2.2

4

2.8

1

.6

High Count 6 5 4 5 0

Expected Count 4.4 4.4 4.4 5.6 1.3

Total Count 7 7 7 9 2

Expected Count 7.0 7.0 7.0 9.0 2.0

AttendanceRating * CourseGradeLetter Crosstabulationa

Total

AttendanceRating Low Count

Expected Count

2

2.0

Average Count

Expected Count

10

10.0

High Count

Expected Count

20

20.0

Total Count 32

Expected Count 32.0

a. CoReq = No

101

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 11.733b 8 .164

Likelihood Ratio 10.518 8 .231

N of Valid Cases 32

a. CoReq = No

b. 14 cells (93.3%) have expected count less than 5. The minimum expected count is .13.

CoReq = Yes

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

AttendanceRating *

CourseGradeLetter 6 100.0% 0 0.0% 6 100.0%

a. CoReq = Yes

AttendanceRating * CourseGradeLetter Crosstabulationa

CourseGradeLetter

Total F D C B

AttendanceRating Average Count 1 0 0 0 1

Expected Count .2 .3 .2 .3 1.0

High Count 0 2 1 2 5

Expected Count .8 1.7 .8 1.7 5.0

Total Count 1 2 1 2 6

Expected Count 1.0 2.0 1.0 2.0 6.0

a. CoReq = Yes

Chi-Square Testsa

Value

df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 6.000b 3 .112

Likelihood Ratio 5.407 3 .144

N of Valid Cases 6

a. CoReq = Yes

b. 8 cells (100.0%) have expected count less than 5. The minimum expected count is .17.

102

Table 22: Correlations Tutorial and Non-Tutorial Students Combined

Descriptive Statistics

Mean Std. Deviation N

NumRemedial 1.72 2.029 46

Num118Attempts .41 .652 46

NumCredits 10.99 3.167 46

Fall2011GPA 2.622641 1.0975402 39

GPACUMFall2011 2.926692 .6822241 39

Spring2012GPA 1.991137 1.2446893 46

GPACUMSpring2012 2.389457 .9300505 46

COMPASSPreAlg 41.79 14.685 24

COMPASSAlg 25.97 9.934 38

PercentPresent 80.2099 20.70797 46

CourseGrade 60.67 23.918 46

Correlations

NumRemedial

Num118Atte

mpts

NumCredits

NumRemedial Pearson Correlation 1 .140 -.326*

Sig. (2-tailed) .352 .027

Sum of Squares and 185.326 8.370 -94.141

Cross-products Covariance 4.118 .186 -2.092

N 46 46 46

Num118Attempts Pearson Correlation .140 1 -.342*

Sig. (2-tailed) .352 .020

Sum of Squares and 8.370 19.152 -31.793

Cross-products Covariance .186 .426 -.707

N 46 46 46

NumCredits Pearson Correlation -.326* -.342

* 1

Sig. (2-tailed) .027 .020

Sum of Squares and -94.141 -31.793 451.245

Cross-products Covariance -2.092 -.707 10.028

N 46 46 46

Fall2011GPA Pearson Correlation -.087 -.017 .268

Sig. (2-tailed) .599 .918 .099

103

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

NumRemedial Pearson Correlation -.087 .021 -.013

Sig. (2-tailed) .599 .901 .932

Sum of Squares and -7.516 1.113 -1.480

Cross-products Covariance -.198 .029 -.033

N 39 39 46

Num118Attempts Pearson Correlation -.017 .217 .376*

Sig. (2-tailed) .918 .185 .010

Sum of Squares and -.485 3.836 13.735

Cross-products Covariance -.013 .101 .305

N 39 39 46

NumCredits Pearson Correlation .268 .242 .203

Sig. (2-tailed) .099 .138 .176

Sum of Squares and 37.019 20.761 35.980

Cross-products Covariance .974 .546 .800

N 39 39 46

Fall2011GPA Pearson Correlation 1 .543**

.282

Sig. (2-tailed) .000 .082

104

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

NumRemedial Pearson Correlation .258 .087 .114

Sig. (2-tailed) .083 .687 .497

Sum of Squares and 21.952 67.000 87.895

Cross-products Covariance .488 2.913 2.376

N 46 24 38

Num118Attempts Pearson Correlation .334* .233 -.248

Sig. (2-tailed) .023 .273 .133

Sum of Squares and 9.110 46.292 -48.658

Cross-products Covariance .202 2.013 -1.315

N 46 24 38

NumCredits Pearson Correlation -.016 .235 .126

Sig. (2-tailed) .918 .270 .449

Sum of Squares and -2.064 301.917 150.250

Cross-products Covariance -.046 13.127 4.061

N 46 24 38

Fall2011GPA Pearson Correlation .286 .063 .079

Sig. (2-tailed) .077 .787 .667

105

PercentPrese

nt

CourseGrade

NumRemedial Pearson Correlation .021 .059

Sig. (2-tailed) .891 .695

Sum of Squares and 39.280 129.761

Cross-products Covariance .873 2.884

N 46 46

Num118Attempts Pearson Correlation -.034 .373*

Sig. (2-tailed) .824 .011

Sum of Squares and -20.540 262.196

Cross-products Covariance -.456 5.827

N 46 46

NumCredits Pearson Correlation .153 .067

Sig. (2-tailed) .309 .658

Sum of Squares and 452.174 228.837

Cross-products Covariance 10.048 5.085

N 46 46

Fall2011GPA Pearson Correlation -.153 .218

Sig. (2-tailed) .353 .182

106

NumRemedial

Num118Atte

mpts

NumCredits

Sum of Squares and -7.516 -.485 37.019 Cross-products Covariance -.198 -.013 .974

N 39 39 39

GPACUMFall2011 Pearson Correlation .021 .217 .242

Sig. (2-tailed) .901 .185 .138

Sum of Squares and 1.113 3.836 20.761

Cross-products

.029

.101

.546 Covariance

N 39 39 39

Spring2012GPA Pearson Correlation -.013 .376* .203

Sig. (2-tailed) .932 .010 .176

Sum of Squares and -1.480 13.735 35.980

Cross-products Covariance -.033 .305 .800

N 46 46 46

GPACUMSpring2012 Pearson Correlation .258 .334* -.016

Sig. (2-tailed) .083 .023 .918

Sum of Squares and 21.952 9.110 -2.064

Cross-products Covariance .488 .202 -.046

N 46 46 46

COMPASSPreAlg Pearson Correlation .087 .233 .235

Sig. (2-tailed) .687 .273 .270

Sum of Squares and 67.000 46.292 301.917 Cross-products Covariance 2.913 2.013 13.127

N 24 24 24

COMPASSAlg Pearson Correlation .114 -.248 .126

Sig. (2-tailed) .497 .133 .449

Sum of Squares and 87.895 -48.658 150.250

Cross-products Covariance 2.376 -1.315 4.061

N 38 38 38

PercentPresent Pearson Correlation .021 -.034 .153

Sig. (2-tailed) .891 .824 .309

Sum of Squares and 39.280 -20.540 452.174

Cross-products Covariance .873 -.456 10.048

N 46 46 46

107

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

Sum of Squares and 45.775 15.455 14.461 Cross-products Covariance 1.205 .407 .381

N 39 39 39

GPACUMFall2011 Pearson Correlation .543**

1 .340*

Sig. (2-tailed) .000 .034

Sum of Squares and 15.455 17.686 10.860

Cross-products

.407

.465

.286 Covariance

N 39 39 39

Spring2012GPA Pearson Correlation .282 .340* 1

Sig. (2-tailed) .082 .034

Sum of Squares and 14.461 10.860 69.716

Cross-products Covariance .381 .286 1.549

N 39 39 46

GPACUMSpring2012 Pearson Correlation .286 .632**

.766**

Sig. (2-tailed) .077 .000 .000

Sum of Squares and 9.947 13.659 39.893

Cross-products Covariance .262 .359 .887

N 39 39 46

COMPASSPreAlg Pearson Correlation .063 .083 .391

Sig. (2-tailed) .787 .719 .059

Sum of Squares and 25.392 18.489 167.875 Cross-products Covariance 1.270 .924 7.299

N 21 21 24

COMPASSAlg Pearson Correlation .079 -.183 -.073

Sig. (2-tailed) .667 .316 .663

Sum of Squares and 26.256 -36.147 -32.163

Cross-products Covariance .847 -1.166 -.869

N 32 32 38

PercentPresent Pearson Correlation -.153 -.078 -.037

Sig. (2-tailed) .353 .639 .808

Sum of Squares and -137.803 -43.454 -42.716

Cross-products Covariance -3.626 -1.144 -.949

N 39 39 46

108

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

Sum of Squares and 9.947 25.392 26.256 Cross-products Covariance .262 1.270 .847

N 39 21 32

GPACUMFall2011 Pearson Correlation .632**

.083 -.183

Sig. (2-tailed) .000 .719 .316

Sum of Squares and 13.659 18.489 -36.147

Cross-products

.359

.924

-1.166 Covariance

N 39 21 32

Spring2012GPA Pearson Correlation .766**

.391 -.073

Sig. (2-tailed) .000 .059 .663

Sum of Squares and 39.893 167.875 -32.163

Cross-products Covariance .887 7.299 -.869

N 46 24 38

GPACUMSpring2012 Pearson Correlation 1 .327 -.189

Sig. (2-tailed) .118 .256

Sum of Squares and 38.925 103.773 -61.197

Cross-products Covariance .865 4.512 -1.654

N 46 24 38

COMPASSPreAlg Pearson Correlation .327 1 .278

Sig. (2-tailed) .118 .189

Sum of Squares and 103.773 4959.958 354.625 Cross-products Covariance 4.512 215.650 15.418

N 24 24 24

COMPASSAlg Pearson Correlation -.189 .278 1

Sig. (2-tailed) .256 .189

Sum of Squares and -61.197 354.625 3650.974

Cross-products Covariance -1.654 15.418 98.675

N 38 24 38

PercentPresent Pearson Correlation -.232 -.102 .308

Sig. (2-tailed) .121 .636 .060

Sum of Squares and -200.933 -894.684 2528.040

Cross-products Covariance -4.465 -38.899 68.325

N 46 24 38

109

PercentPrese

nt

CourseGrade

Sum of Squares and -137.803 212.835 Cross-products Covariance -3.626 5.601

N 39 39

GPACUMFall2011 Pearson Correlation -.078 .347*

Sig. (2-tailed) .639 .030

Sum of Squares and -43.454 210.367

Cross-products

-1.144

5.536 Covariance

N 39 39

Spring2012GPA Pearson Correlation -.037 .793**

Sig. (2-tailed) .808 .000

Sum of Squares and -42.716 1062.539

Cross-products Covariance -.949 23.612

N 46 46

GPACUMSpring2012 Pearson Correlation -.232 .645**

Sig. (2-tailed) .121 .000

Sum of Squares and -200.933 646.004

Cross-products Covariance -4.465 14.356

N 46 46

COMPASSPreAlg Pearson Correlation -.102 .366

Sig. (2-tailed) .636 .079

Sum of Squares and -894.684 3098.583 Cross-products Covariance -38.899 134.721

N 24 24

COMPASSAlg Pearson Correlation .308 .047

Sig. (2-tailed) .060 .777

Sum of Squares and 2528.040 410.316

Cross-products Covariance 68.325 11.090

N 38 38

PercentPresent Pearson Correlation 1 .079

Sig. (2-tailed) .602

Sum of Squares and 19296.903 1761.769

Cross-products Covariance 428.820 39.150

N 46 46

110

NumRemedial

Num118Atte

mpts

NumCredits

CourseGrade Pearson Correlation .059 .373* .067

Sig. (2-tailed) .695 .011 .658

Sum of Squares and 129.761 262.196 228.837

Cross-products Covariance 2.884 5.827 5.085

N 46 46 46

Correlations

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

CourseGrade Pearson Correlation .218 .347* .793

**

Sig. (2-tailed) .182 .030 .000

Sum of Squares and 212.835 210.367 1062.539

Cross-products Covariance 5.601 5.536 23.612

N 39 39 46

Correlations

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

CourseGrade Pearson Correlation .645**

.366 .047

Sig. (2-tailed) .000 .079 .777

Sum of Squares and 646.004 3098.583 410.316

Cross-products Covariance 14.356 134.721 11.090

N 46 24 38

Correlations

PercentPrese

nt

CourseGrade

CourseGrade Pearson Correlation .079 1

Sig. (2-tailed) .602

Sum of Squares and 1761.769 25742.109

Cross-products Covariance 39.150 572.047

N 46 46

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

111

Table 23: Correlations by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

NumRemedial 1.69 1.546 36

Num118Attempts .50 .697 36

NumCredits 10.88 2.970 36

Fall2011GPA 2.729688 1.0189351 32

GPACUMFall2011 2.943406 .6153698 32

Spring2012GPA 2.116147 1.2474085 36

GPACUMSpring2012 2.424250 .8885642 36

COMPASSPreAlg 46.79 16.674 14

COMPASSAlg 28.14 10.452 28

PercentPresent 79.8851 20.06445 36

CourseGrade 64.31 21.330 36

a. CoReq = No

112

NumRemedial

Num118Atte

mpts

NumCredits

NumRemedial Pearson Correlation 1 .252 -.152

Sig. (2-tailed) .138 .377

Sum of Squares and 83.639 9.500 -24.375

Cross-products Covariance 2.390 .271 -.696

N 36 36 36

Num118Attempts Pearson Correlation .252 1 -.424**

Sig. (2-tailed) .138 .010

Sum of Squares and 9.500 17.000 -30.750

Cross-products Covariance .271 .486 -.879

N 36 36 36

NumCredits Pearson Correlation -.152 -.424**

1

Sig. (2-tailed) .377 .010

Sum of Squares and -24.375 -30.750 308.688

Cross-products Covariance -.696 -.879 8.820

N 36 36 36

Fall2011GPA Pearson Correlation -.369* -.030 .339

Sig. (2-tailed) .038 .870 .058

Sum of Squares and -17.850 -.682 33.011

Cross-products Covariance -.576 -.022 1.065

N 32 32 32

GPACUMFall2011 Pearson Correlation -.037 .216 .261

Sig. (2-tailed) .842 .236 .149

Sum of Squares and -1.073 2.952 15.380 Cross-products Covariance -.035 .095 .496

N 32 32 32

Spring2012GPA Pearson Correlation .122 .388* .130

Sig. (2-tailed) .478 .019 .450

Sum of Squares and 8.235 11.809 16.865

Cross-products Covariance .235 .337 .482

N 36 36 36

GPACUMSpring2012 Pearson Correlation .253 .367* .053

Sig. (2-tailed) .137 .028 .760

Sum of Squares and 12.140 7.945 4.861

Cross-products

113

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

NumRemedial Pearson Correlation -.369* -.037 .122

Sig. (2-tailed) .038 .842 .478

Sum of Squares and -17.850 -1.073 8.235

Cross-products Covariance -.576 -.035 .235

N 32 32 36

Num118Attempts Pearson Correlation -.030 .216 .388*

Sig. (2-tailed) .870 .236 .019

Sum of Squares and -.682 2.952 11.809

Cross-products Covariance -.022 .095 .337

N 32 32 36

NumCredits Pearson Correlation .339 .261 .130

Sig. (2-tailed) .058 .149 .450

Sum of Squares and 33.011 15.380 16.865

Cross-products Covariance 1.065 .496 .482

N 32 32 36

Fall2011GPA Pearson Correlation 1 .499**

.238

Sig. (2-tailed) .004 .189

Sum of Squares and 32.185 9.709 9.327

Cross-products Covariance 1.038 .313 .301

N 32 32 32

GPACUMFall2011 Pearson Correlation .499**

1 .367*

Sig. (2-tailed) .004 .039

Sum of Squares and 9.709 11.739 8.670 Cross-products Covariance .313 .379 .280

N 32 32 32

Spring2012GPA Pearson Correlation .238 .367* 1

Sig. (2-tailed) .189 .039

Sum of Squares and 9.327 8.670 54.461

Cross-products Covariance .301 .280 1.556

N 32 32 36

GPACUMSpring2012 Pearson Correlation .157 .581**

.832**

Sig. (2-tailed) .391 .000 .000

Sum of Squares and 4.132 9.241 32.266

Cross-products

114

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

NumRemedial Pearson Correlation .253 -.002 -.063

Sig. (2-tailed) .137 .994 .752

Sum of Squares and 12.140 -.571 -26.714

Cross-products Covariance .347 -.044 -.989

N 36 14 28

Num118Attempts Pearson Correlation .367* .039 -.425

*

Sig. (2-tailed) .028 .895 .024

Sum of Squares and 7.945 5.143 -68.714

Cross-products Covariance .227 .396 -2.545

N 36 14 28

NumCredits Pearson Correlation .053 .434 .241

Sig. (2-tailed) .760 .121 .217

Sum of Squares and 4.861 352.000 205.214

Cross-products Covariance .139 27.077 7.601

N 36 14 28

Fall2011GPA Pearson Correlation .157 .071 .005

Sig. (2-tailed) .391 .809 .981

Sum of Squares and 4.132 20.572 1.305

Cross-products Covariance .133 1.582 .054

N 32 14 25

GPACUMFall2011 Pearson Correlation .581**

.332 -.244

Sig. (2-tailed) .000 .247 .240

Sum of Squares and 9.241 42.703 -34.746 Cross-products Covariance .298 3.285 -1.448

N 32 14 25

Spring2012GPA Pearson Correlation .832**

.333 -.245

Sig. (2-tailed) .000 .245 .208

Sum of Squares and 32.266 87.556 -81.459

Cross-products Covariance .922 6.735 -3.017

N 36 14 28

GPACUMSpring2012 Pearson Correlation 1 .474 -.416*

Sig. (2-tailed) .087 .028

Sum of Squares and 27.634 78.765 -94.049

Cross-products

115

PercentPrese

nt

CourseGrade

NumRemedial Pearson Correlation .003 .071

Sig. (2-tailed) .988 .683

Sum of Squares and 2.874 81.361

Cross-products Covariance .082 2.325

N 36 36

Num118Attempts Pearson Correlation -.056 .353*

Sig. (2-tailed) .744 .035

Sum of Squares and -27.586 183.500

Cross-products Covariance -.788 5.243

N 36 36

NumCredits Pearson Correlation .013 -.028

Sig. (2-tailed) .941 .871

Sum of Squares and 26.724 -62.125

Cross-products Covariance .764 -1.775

N 36 36

Fall2011GPA Pearson Correlation -.192 .110

Sig. (2-tailed) .292 .549

Sum of Squares and -126.334 75.013

Cross-products Covariance -4.075 2.420

N 32 32

GPACUMFall2011 Pearson Correlation -.097 .294

Sig. (2-tailed) .598 .102

Sum of Squares and -38.481 121.083 Cross-products Covariance -1.241 3.906

N 32 32

Spring2012GPA Pearson Correlation -.186 .827**

Sig. (2-tailed) .277 .000

Sum of Squares and -162.938 770.589

Cross-products Covariance -4.655 22.017

N 36 36

GPACUMSpring2012 Pearson Correlation -.304 .719**

Sig. (2-tailed) .071 .000

Sum of Squares and -189.850 476.920

Cross-products

116

NumRemedial

Num118Atte

mpts

NumCredits

COMPASSPreAlg

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and Cross-

products

Covariance

N

.347

36

.227

36

.139

36

-.002 .039 .434

.994 .895 .121

-.571 5.143 352.000

-.044

.396

27.077

14 14 14

COMPASSAlg Pearson Correlation -.063 -.425* .241

Sig. (2-tailed) .752 .024 .217

Sum of Squares and -26.714 -68.714 205.214

Cross-products Covariance -.989 -2.545 7.601

N 28 28 28

PercentPresent Pearson Correlation .003 -.056 .013

Sig. (2-tailed) .988 .744 .941

Sum of Squares and 2.874 -27.586 26.724

Cross-products Covariance .082 -.788 .764

N 36 36 36

CourseGrade Pearson Correlation .071 .353* -.028

Sig. (2-tailed) .683 .035 .871

Sum of Squares and 81.361 183.500 -62.125

Cross-products Covariance 2.325 5.243 -1.775

N 36 36 36

117

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

COMPASSPreAlg

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and Cross-

products

Covariance

N

.133

32

.298

32

.922

36

.071 .332 .333

.809 .247 .245

20.572 42.703 87.556

1.582

3.285

6.735

14 14 14

COMPASSAlg Pearson Correlation .005 -.244 -.245

Sig. (2-tailed) .981 .240 .208

Sum of Squares and 1.305 -34.746 -81.459

Cross-products Covariance .054 -1.448 -3.017

N 25 25 28

PercentPresent Pearson Correlation -.192 -.097 -.186

Sig. (2-tailed) .292 .598 .277

Sum of Squares and -126.334 -38.481 -162.938

Cross-products Covariance -4.075 -1.241 -4.655

N 32 32 36

CourseGrade Pearson Correlation .110 .294 .827**

Sig. (2-tailed) .549 .102 .000

Sum of Squares and 75.013 121.083 770.589

Cross-products Covariance 2.420 3.906 22.017

N 32 32 36

118

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

COMPASSPreAlg

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and Cross-

products

Covariance

N

.790

36

6.059

14

-3.483

28

.474 1 .196

.087 .501

78.765 3614.357 132.857

6.059

278.027

10.220

14 14 14

COMPASSAlg Pearson Correlation -.416* .196 1

Sig. (2-tailed) .028 .501

Sum of Squares and -94.049 132.857 2949.429

Cross-products Covariance -3.483 10.220 109.238

N 28 14 28

PercentPresent Pearson Correlation -.304 -.146 .364

Sig. (2-tailed) .071 .618 .057

Sum of Squares and -189.850 -865.271 2272.906

Cross-products Covariance -5.424 -66.559 84.182

N 36 14 28

CourseGrade Pearson Correlation .719**

.319 -.194

Sig. (2-tailed) .000 .267 .321

Sum of Squares and 476.920 1102.429 -1070.857

Cross-products Covariance 13.626 84.802 -39.661

N 36 14 28

119

PercentPrese

nt

CourseGrade

COMPASSPreAlg

Covariance

N

Pearson Correlation

Sig. (2-tailed)

Sum of Squares and Cross-

products

Covariance

N

-5.424

36

13.626

36

-.146 .319

.618 .267

-865.271 1102.429

-66.559

84.802

14 14

COMPASSAlg Pearson Correlation .364 -.194

Sig. (2-tailed) .057 .321

Sum of Squares and 2272.906 -1070.857

Cross-products Covariance 84.182 -39.661

N 28 28

PercentPresent Pearson Correlation 1 -.186

Sig. (2-tailed) .278

Sum of Squares and 14090.369 -2785.632

Cross-products Covariance 402.582 -79.589

N 36 36

CourseGrade Pearson Correlation -.186 1

Sig. (2-tailed) .278

Sum of Squares and -2785.632 15923.639

Cross-products Covariance -79.589 454.961

N 36 36

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

a. CoReq = No

CoReq = Yes

120

Descriptive Statisticsa

Mean Std. Deviation N

NumRemedial 1.80 3.360 10

Num118Attempts .10 .316 10

NumCredits 11.40 3.950 10

Fall2011GPA 2.133286 1.3872355 7

GPACUMFall2011 2.850286 .9914170 7

Spring2012GPA 1.541100 1.1863768 10

GPACUMSpring2012 2.264200 1.1100619 10

COMPASSPreAlg 34.80 7.510 10

COMPASSAlg 19.90 4.725 10

PercentPresent 81.3793 24.01171 10

CourseGrade 47.60 29.125 10

a. CoReq = Yes

Correlationsa

NumRemedial

Num118Atte

mpts

NumCredits

NumRemedial Pearson Correlation 1 -.084 -.588

Sig. (2-tailed) .818 .074

Sum of Squares and 101.600 -.800 -70.200

Cross-products Covariance 11.289 -.089 -7.800

N 10 10 10

Num118Attempts Pearson Correlation -.084 1 .053

Sig. (2-tailed) .818 .884

Sum of Squares and -.800 .900 .600

Cross-products Covariance -.089 .100 .067

N 10 10 10

NumCredits Pearson Correlation -.588 .053 1

Sig. (2-tailed) .074 .884

Sum of Squares and -70.200 .600 140.400

Cross-products Covariance -7.800 .067 15.600

N 10 10 10

Fall2011GPA Pearson Correlation .406 -.360 .114

Sig. (2-tailed) .366 .427 .807

Sum of Squares and 12.934 -1.133 4.268

Cross-products Covariance 2.156 -.189 .711

N 7 7 7

121

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

NumRemedial Pearson Correlation .406 .114 -.258

Sig. (2-tailed) .366 .808 .473

Sum of Squares and 12.934 2.592 -9.240

Cross-products Covariance 2.156 .432 -1.027

N 7 7 10

Num118Attempts Pearson Correlation -.360 .301 .037

Sig. (2-tailed) .427 .513 .919

Sum of Squares and -1.133 .676 .126

Cross-products Covariance -.189 .113 .014

N 7 7 10

NumCredits Pearson Correlation .114 .203 .509

Sig. (2-tailed) .807 .662 .133

Sum of Squares and 4.268 5.421 21.479

Cross-products Covariance .711 .904 2.387

N 7 7 10

Fall2011GPA Pearson Correlation 1 .658 .303

Sig. (2-tailed) .108 .509

Sum of Squares and 11.547 5.427 2.783

Cross-products Covariance 1.924 .905 .464

N 7 7 7

122

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

NumRemedial Pearson Correlation .296 .192 .748*

Sig. (2-tailed) .406 .595 .013

Sum of Squares and 9.944 43.600 106.800

Cross-products Covariance 1.105 4.844 11.867

N 10 10 10

Num118Attempts Pearson Correlation .210 -.084 .007

Sig. (2-tailed) .560 .817 .984

Sum of Squares and .665 -1.800 .100

Cross-products Covariance .074 -.200 .011

N 10 10 10

NumCredits Pearson Correlation -.159 .179 -.254

Sig. (2-tailed) .661 .621 .479

Sum of Squares and -6.267 47.800 -42.600

Cross-products Covariance -.696 5.311 -4.733

N 10 10 10

Fall2011GPA Pearson Correlation .761* -.231 .195

Sig. (2-tailed) .047 .619 .675

Sum of Squares and 5.693 -16.664 8.668

Cross-products Covariance .949 -2.777 1.445

N 7 7 7

123

PercentPrese

nt

CourseGrade

NumRemedial Pearson Correlation .048 .071

Sig. (2-tailed) .894 .846

Sum of Squares and 35.172 62.200

Cross-products Covariance 3.908 6.911

N 10 10

Num118Attempts Pearson Correlation .172 .318

Sig. (2-tailed) .636 .370

Sum of Squares and 11.724 26.400

Cross-products Covariance 1.303 2.933

N 10 10

NumCredits Pearson Correlation .491 .347

Sig. (2-tailed) .149 .325

Sum of Squares and 419.310 359.600

Cross-products Covariance 46.590 39.956

N 10 10

Fall2011GPA Pearson Correlation -.034 .364

Sig. (2-tailed) .942 .422

Sum of Squares and -7.567 89.483

Cross-products Covariance -1.261 14.914

N 7 7

124

NumRemedial

Num118Atte

mpts

NumCredits

GPACUMFall2011 Pearson Correlation .114 .301 .203

Sig. (2-tailed) .808 .513 .662

Sum of Squares and 2.592 .676 5.421

Cross-products Covariance .432 .113 .904

N 7 7 7

Spring2012GPA Pearson Correlation -.258 .037 .509

Sig. (2-tailed) .473 .919 .133

Sum of Squares and -9.240 .126 21.479

Cross-products Covariance -1.027 .014 2.387

N 10 10 10

GPACUMSpring2012 Pearson Correlation .296 .210 -.159

Sig. (2-tailed) .406 .560 .661

Sum of Squares and 9.944 .665 -6.267

Cross-products Covariance 1.105 .074 -.696

N 10 10 10

COMPASSPreAlg Pearson Correlation .192 -.084 .179

Sig. (2-tailed) .595 .817 .621

Sum of Squares and 43.600 -1.800 47.800

Cross-products Covariance 4.844 -.200 5.311

N 10 10 10

COMPASSAlg Pearson Correlation .748* .007 -.254

Sig. (2-tailed) .013 .984 .479

Sum of Squares and 106.800 .100 -42.600 Cross-products Covariance 11.867 .011 -4.733

N 10 10 10

PercentPresent Pearson Correlation .048 .172 .491

Sig. (2-tailed) .894 .636 .149

Sum of Squares and 35.172 11.724 419.310

Cross-products Covariance 3.908 1.303 46.590

N 10 10 10

CourseGrade Pearson Correlation .071 .318 .347

Sig. (2-tailed) .846 .370 .325

Sum of Squares and 62.200 26.400 359.600

Cross-products

125

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

GPACUMFall2011 Pearson Correlation .658 1 .278

Sig. (2-tailed) .108 .546

Sum of Squares and 5.427 5.897 1.823

Cross-products Covariance .905 .983 .304

N 7 7 7

Spring2012GPA Pearson Correlation .303 .278 1

Sig. (2-tailed) .509 .546

Sum of Squares and 2.783 1.823 12.667

Cross-products Covariance .464 .304 1.407

N 7 7 10

GPACUMSpring2012 Pearson Correlation .761* .822

* .583

Sig. (2-tailed) .047 .023 .077

Sum of Squares and 5.693 4.399 6.906

Cross-products Covariance .949 .733 .767

N 7 7 10

COMPASSPreAlg Pearson Correlation -.231 -.680 .162

Sig. (2-tailed) .619 .092 .654

Sum of Squares and -16.664 -35.110 13.006

Cross-products Covariance -2.777 -5.852 1.445

N 7 7 10

COMPASSAlg Pearson Correlation .195 -.124 .195

Sig. (2-tailed) .675 .791 .590

Sum of Squares and 8.668 -3.935 9.822 Cross-products Covariance 1.445 -.656 1.091

N 7 7 10

PercentPresent Pearson Correlation -.034 -.027 .495

Sig. (2-tailed) .942 .954 .146

Sum of Squares and -7.567 -4.363 126.946

Cross-products Covariance -1.261 -.727 14.105

N 7 7 10

CourseGrade Pearson Correlation .364 .466 .697*

Sig. (2-tailed) .422 .292 .025

Sum of Squares and 89.483 81.736 216.768

Cross-products

126

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

GPACUMFall2011 Pearson Correlation .822* -.680 -.124

Sig. (2-tailed) .023 .092 .791

Sum of Squares and 4.399 -35.110 -3.935

Cross-products Covariance .733 -5.852 -.656

N 7 7 7

Spring2012GPA Pearson Correlation .583 .162 .195

Sig. (2-tailed) .077 .654 .590

Sum of Squares and 6.906 13.006 9.822

Cross-products Covariance .767 1.445 1.091

N 10 10 10

GPACUMSpring2012 Pearson Correlation 1 -.142 .410

Sig. (2-tailed) .696 .239

Sum of Squares and 11.090 -10.656 19.354

Cross-products Covariance 1.232 -1.184 2.150

N 10 10 10

COMPASSPreAlg Pearson Correlation -.142 1 .610

Sig. (2-tailed) .696 .061

Sum of Squares and -10.656 507.600 194.800

Cross-products Covariance -1.184 56.400 21.644

N 10 10 10

COMPASSAlg Pearson Correlation .410 .610 1

Sig. (2-tailed) .239 .061

Sum of Squares and 19.354 194.800 200.900 Cross-products Covariance 2.150 21.644 22.322

N 10 10 10

PercentPresent Pearson Correlation -.038 .464 .380

Sig. (2-tailed) .916 .177 .278

Sum of Squares and -9.211 752.414 388.276

Cross-products Covariance -1.023 83.602 43.142

N 10 10 10

CourseGrade Pearson Correlation .509 .142 .283

Sig. (2-tailed) .133 .695 .428

Sum of Squares and 148.159 280.200 350.600

Cross-products

127

PercentPrese

nt

CourseGrade

GPACUMFall2011 Pearson Correlation -.027 .466

Sig. (2-tailed) .954 .292

Sum of Squares and -4.363 81.736

Cross-products Covariance -.727 13.623

N 7 7

Spring2012GPA Pearson Correlation .495 .697*

Sig. (2-tailed) .146 .025

Sum of Squares and 126.946 216.768

Cross-products Covariance 14.105 24.085

N 10 10

GPACUMSpring2012 Pearson Correlation -.038 .509

Sig. (2-tailed) .916 .133

Sum of Squares and -9.211 148.159

Cross-products Covariance -1.023 16.462

N 10 10

COMPASSPreAlg Pearson Correlation .464 .142

Sig. (2-tailed) .177 .695

Sum of Squares and 752.414 280.200

Cross-products Covariance 83.602 31.133

N 10 10

COMPASSAlg Pearson Correlation .380 .283

Sig. (2-tailed) .278 .428

Sum of Squares and 388.276 350.600 Cross-products Covariance 43.142 38.956

N 10 10

PercentPresent Pearson Correlation 1 .754*

Sig. (2-tailed) .012

Sum of Squares and 5189.061 4742.759

Cross-products Covariance 576.562 526.973

N 10 10

CourseGrade Pearson Correlation .754* 1

Sig. (2-tailed) .012

Sum of Squares and 4742.759 7634.400

Cross-products

128

NumRemedial

Num118Atte

mpts

NumCredits

Covariance

N

6.911

10

2.933

10

39.956

10

Correlationsa

Fall2011GPA

GPACUMFall

2011

Spring2012G

PA

Covariance

N

14.914

7

13.623

7

24.085

10

Correlationsa

GPACUMSpri

ng2012

COMPASSPr

eAlg

COMPASSAlg

Covariance

N

16.462

10

31.133

10

38.956

10

Correlationsa

PercentPrese

nt

CourseGrade

Covariance

N

526.973

10

848.267

10

*. Correlation is significant at the 0.05 level (2-tailed).

a. CoReq = Yes

Table 24: Correlations Tutorial and Non-Tutorial Students Combined

129

Descriptive Statistics

Mean Std. Deviation N

MMLHwAvg 19.1087 6.08907 46

MMLQzAvg 13.5652 6.25976 46

ProbActAvg 37.2174 11.80002 46

Test1 61.5217 22.74471 46

Test2 48.2935 23.34870 46

Test3 58.7391 30.11934 46

Test4 49.9130 29.94426 46

Final 56.3696 29.66956 46

PercentPresent 80.2099 20.70797 46

CourseGrade 60.67 23.918 46

Correlations

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

MMLHwAvg Pearson Correlation 1 .827**

.784**

.557**

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 1668.457 1418.174 2534.913 3471.391

Cross-products Covariance 37.077 31.515 56.331 77.142

N 46 46 46 46

MMLQzAvg Pearson Correlation .827**

1 .682**

.709**

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 1418.174 1763.304 2266.348 4539.435

Cross-products Covariance 31.515 39.185 50.363 100.876

N 46 46 46 46

ProbActAvg Pearson Correlation .784**

.682**

1 .486**

Sig. (2-tailed) .000 .000 .001

Sum of Squares and 2534.913 2266.348 6265.826 5869.783

Cross-products Covariance 56.331 50.363 139.241 130.440

N 46 46 46 46

Test1 Pearson Correlation .557**

.709**

.486**

1

Sig. (2-tailed) .000 .000 .001

Sum of Squares and 3471.391 4539.435 5869.783 23279.478

Cross-products Covariance 77.142 100.876 130.440 517.322

N 46 46 46 46

Test2 Pearson Correlation .534**

.593**

.445**

.472**

Sig. (2-tailed) .000 .000 .002 .001

130

Test2

Test3

Test4

Final

MMLHwAvg Pearson Correlation .534**

.611**

.689**

.692**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 3415.533 5043.304 5649.435 5623.152

Cross-products Covariance 75.901 112.073 125.543 124.959

N 46 46 46 46

MMLQzAvg Pearson Correlation .593**

.773**

.827**

.828**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 3897.370 6554.783 6978.261 6917.391

Cross-products Covariance 86.608 145.662 155.072 153.720

N 46 46 46 46

ProbActAvg Pearson Correlation .445**

.688**

.737**

.640**

Sig. (2-tailed) .002 .000 .000 .000

Sum of Squares and 5521.065 11006.609 11710.870 10090.304

Cross-products Covariance 122.690 244.591 260.242 224.229

N 46 46 46 46

Test1 Pearson Correlation .472**

.562**

.627**

.632**

Sig. (2-tailed) .001 .000 .000 .000

Sum of Squares and 11286.957 17336.261 19208.087 19203.130

Cross-products Covariance 250.821 385.250 426.846 426.736

N 46 46 46 46

Test2 Pearson Correlation 1 .596**

.518**

.662**

Sig. (2-tailed) .000 .000 .000

131

PercentPrese

nt

CourseGrade

MMLHwAvg Pearson Correlation .203 .773**

Sig. (2-tailed) .175 .000

Sum of Squares and 1154.123 5066.630

Cross-products Covariance 25.647 112.592

N 46 46

MMLQzAvg Pearson Correlation .035 .890**

Sig. (2-tailed) .817 .000

Sum of Squares and 204.198 5994.478

Cross-products Covariance 4.538 133.211

N 46 46

ProbActAvg Pearson Correlation .220 .755**

Sig. (2-tailed) .142 .000

Sum of Squares and 2418.591 9593.261

Cross-products Covariance 53.746 213.184

N 46 46

Test1 Pearson Correlation .017 .740**

Sig. (2-tailed) .911 .000

Sum of Squares and 361.169 18124.826

Cross-products Covariance 8.026 402.774

N 46 46

Test2 Pearson Correlation .077 .696**

Sig. (2-tailed) .611 .000

132

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

Sum of Squares and 3415.533 3897.370 5521.065 11286.957

Cross-products Covariance 75.901 86.608 122.690 250.821

N 46 46 46 46

Test3 Pearson Correlation .611**

.773**

.688**

.562**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 5043.304 6554.783 11006.609 17336.261

Cross-products

112.073

145.662

244.591

385.250 Covariance

N 46 46 46 46

Test4 Pearson Correlation .689**

.827**

.737**

.627**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 5649.435 6978.261 11710.870 19208.087

Cross-products Covariance 125.543 155.072 260.242 426.846

N 46 46 46 46

Final Pearson Correlation .692**

.828**

.640**

.632**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 5623.152 6917.391 10090.304 19203.130

Cross-products Covariance 124.959 153.720 224.229 426.736

N 46 46 46 46

PercentPresent Pearson Correlation .203 .035 .220 .017

Sig. (2-tailed) .175 .817 .142 .911

Sum of Squares and 1154.123 204.198 2418.591 361.169

Cross-products Covariance 25.647 4.538 53.746 8.026

N 46 46 46 46

CourseGrade Pearson Correlation .773**

.890**

.755**

.740**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 5066.630 5994.478 9593.261 18124.826

Cross-products Covariance 112.592 133.211 213.184 402.774

N 46 46 46 46

133

Test2

Test3

Test4

Final

Sum of Squares and 24532.288 18866.022 16305.174 20632.011

Cross-products Covariance 545.162 419.245 362.337 458.489

N 46 46 46 46

Test3 Pearson Correlation .596**

1 .819**

.882**

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 18866.022 40822.870 33240.957 35460.435

Cross-products

419.245

907.175

738.688

788.010 Covariance

N 46 46 46 46

Test4 Pearson Correlation .518**

.819**

1 .903**

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 16305.174 33240.957 40349.652 36107.478

Cross-products Covariance 362.337 738.688 896.659 802.388

N 46 46 46 46

Final Pearson Correlation .662**

.882**

.903**

1

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 20632.011 35460.435 36107.478 39612.717

Cross-products Covariance 458.489 788.010 802.388 880.283

N 46 46 46 46

PercentPresent Pearson Correlation .077 -.037 .090 .074

Sig. (2-tailed) .611 .808 .551 .623

Sum of Squares and 1674.063 -1034.033 2517.391 2057.121

Cross-products Covariance 37.201 -22.979 55.942 45.714

N 46 46 46 46

CourseGrade Pearson Correlation .696**

.910**

.921**

.966**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 17491.902 29505.087 29674.696 30849.543

Cross-products Covariance 388.709 655.669 659.438 685.545

N 46 46 46 46

134

PercentPrese

nt

CourseGrade

Sum of Squares and 1674.063 17491.902

Cross-products Covariance 37.201 388.709

N 46 46

Test3 Pearson Correlation -.037 .910**

Sig. (2-tailed) .808 .000

Sum of Squares and -1034.033 29505.087

Cross-products

-22.979

655.669 Covariance

N 46 46

Test4 Pearson Correlation .090 .921**

Sig. (2-tailed) .551 .000

Sum of Squares and 2517.391 29674.696

Cross-products Covariance 55.942 659.438

N 46 46

Final Pearson Correlation .074 .966**

Sig. (2-tailed) .623 .000

Sum of Squares and 2057.121 30849.543

Cross-products Covariance 45.714 685.545

N 46 46

PercentPresent Pearson Correlation 1 .079

Sig. (2-tailed) .602

Sum of Squares and 19296.903 1761.769

Cross-products Covariance 428.820 39.150

N 46 46

CourseGrade Pearson Correlation .079 1

Sig. (2-tailed) .602

Sum of Squares and 1761.769 25742.109

Cross-products Covariance 39.150 572.047

N 46 46

**. Correlation is significant at the 0.01 level (2-tailed).

Table 25: Correlations by Tutorial Enrollment

135

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

MMLHwAvg 19.6667 5.58058 36

MMLQzAvg 14.6111 5.69851 36

ProbActAvg 37.4444 10.76089 36

Test1 64.7500 20.43579 36

Test2 51.8611 22.81873 36

Test3 63.4722 26.63884 36

Test4 53.3889 27.45935 36

Final 60.8333 26.66994 36

PercentPresent 79.8851 20.06445 36

CourseGrade 64.31 21.330 36

a. CoReq = No

Correlationsa

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

MMLHwAvg Pearson Correlation 1 .873**

.724**

.507**

Sig. (2-tailed) .000 .000 .002

Sum of Squares and 1090.000 971.333 1522.333 2022.000

Cross-products Covariance 31.143 27.752 43.495 57.771

N 36 36 36 36

MMLQzAvg Pearson Correlation .873**

1 .659**

.629**

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 971.333 1136.556 1415.222 2563.500

Cross-products Covariance 27.752 32.473 40.435 73.243

N 36 36 36 36

ProbActAvg Pearson Correlation .724**

.659**

1 .407*

Sig. (2-tailed) .000 .000 .014

Sum of Squares and 1522.333 1415.222 4052.889 3131.000

Cross-products Covariance 43.495 40.435 115.797 89.457

N 36 36 36 36

Test1 Pearson Correlation .507**

.629**

.407* 1

Sig. (2-tailed) .002 .000 .014

136

Test2

Test3

Test4

Final

MMLHwAvg Pearson Correlation .500**

.587**

.695**

.699**

Sig. (2-tailed) .002 .000 .000 .000

Sum of Squares and 2230.333 3052.667 3725.667 3642.000

Cross-products Covariance 63.724 87.219 106.448 104.057

N 36 36 36 36

MMLQzAvg Pearson Correlation .555**

.701**

.794**

.789**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 2524.056 3726.611 4348.444 4195.667

Cross-products Covariance 72.116 106.475 124.241 119.876

N 36 36 36 36

ProbActAvg Pearson Correlation .414* .662

** .730

** .581

**

Sig. (2-tailed) .012 .000 .000 .000

Sum of Squares and 3560.222 6640.444 7545.778 5831.667

Cross-products Covariance 101.721 189.727 215.594 166.619

N 36 36 36 36

Test1 Pearson Correlation .454**

.544**

.620**

.643**

Sig. (2-tailed) .005 .001 .000 .000

137

PercentPrese

nt

CourseGrade

MMLHwAvg Pearson Correlation -.079 .767**

Sig. (2-tailed) .646 .000

Sum of Squares and -310.345 3194.667

Cross-products Covariance -8.867 91.276

N 36 36

MMLQzAvg Pearson Correlation -.219 .855**

Sig. (2-tailed) .200 .000

Sum of Squares and -874.713 3635.278

Cross-products Covariance -24.992 103.865

N 36 36

ProbActAvg Pearson Correlation -.092 .712**

Sig. (2-tailed) .595 .000

Sum of Squares and -691.954 5720.111

Cross-products Covariance -19.770 163.432

N 36 36

Test1 Pearson Correlation -.293 .731**

Sig. (2-tailed) .083 .000

138

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

Sum of Squares and 2022.000 2563.500 3131.000 14616.750

Cross-products Covariance 57.771 73.243 89.457 417.621

N 36 36 36 36

Test2 Pearson Correlation .500**

.555**

.414* .454

**

Sig. (2-tailed) .002 .000 .012 .005

Sum of Squares and 2230.333 2524.056 3560.222 7407.750

Cross-products

63.724

72.116

101.721

211.650 Covariance

N 36 36 36 36

Test3 Pearson Correlation .587**

.701**

.662**

.544**

Sig. (2-tailed) .000 .000 .000 .001

Sum of Squares and 3052.667 3726.611 6640.444 10361.250

Cross-products Covariance 87.219 106.475 189.727 296.036

N 36 36 36 36

Test4 Pearson Correlation .695**

.794**

.730**

.620**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 3725.667 4348.444 7545.778 12172.500

Cross-products Covariance 106.448 124.241 215.594 347.786

N 36 36 36 36

Final Pearson Correlation .699**

.789**

.581**

.643**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 3642.000 4195.667 5831.667 12260.500

Cross-products Covariance 104.057 119.876 166.619 350.300

N 36 36 36 36

PercentPresent Pearson Correlation -.079 -.219 -.092 -.293

Sig. (2-tailed) .646 .200 .595 .083

Sum of Squares and -310.345 -874.713 -691.954 -4208.621

Cross-products Covariance -8.867 -24.992 -19.770 -120.246

N 36 36 36 36

CourseGrade Pearson Correlation .767**

.855**

.712**

.731**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 3194.667 3635.278 5720.111 11150.750

Cross-products Covariance 91.276 103.865 163.432 318.593

N 36 36 36 36

139

Test2

Test3

Test4

Final

Sum of Squares and 7407.750 10361.250 12172.500 12260.500

Cross-products Covariance 211.650 296.036 347.786 350.300

N 36 36 36 36

Test2 Pearson Correlation 1 .575**

.549**

.713**

Sig. (2-tailed) .000 .001 .000

Sum of Squares and 18224.306 12242.361 12036.944 15180.167

Cross-products

520.694

349.782

343.913

433.719 Covariance

N 36 36 36 36

Test3 Pearson Correlation .575**

1 .759**

.819**

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 12242.361 24836.972 19429.389 20363.833

Cross-products Covariance 349.782 709.628 555.125 581.824

N 36 36 36 36

Test4 Pearson Correlation .549**

.759**

1 .871**

Sig. (2-tailed) .001 .000 .000

Sum of Squares and 12036.944 19429.389 26390.556 22314.333

Cross-products Covariance 343.913 555.125 754.016 637.552

N 36 36 36 36

Final Pearson Correlation .713**

.819**

.871**

1

Sig. (2-tailed) .000 .000 .000

Sum of Squares and 15180.167 20363.833 22314.333 24895.000

Cross-products Covariance 433.719 581.824 637.552 711.286

N 36 36 36 36

PercentPresent Pearson Correlation -.053 -.284 -.095 -.115

Sig. (2-tailed) .758 .093 .583 .504

Sum of Squares and -852.299 -5320.115 -1825.287 -2155.172

Cross-products Covariance -24.351 -152.003 -52.151 -61.576

N 36 36 36 36

CourseGrade Pearson Correlation .733**

.879**

.905**

.962**

Sig. (2-tailed) .000 .000 .000 .000

Sum of Squares and 12481.528 17486.806 18554.722 19144.833

Cross-products Covariance 356.615 499.623 530.135 546.995

N 36 36 36 36

140

Correlationsa

PercentPrese

nt

CourseGrade

Sum of Squares and -4208.621 11150.750

Cross-products Covariance -120.246 318.593

N 36 36

Test2 Pearson Correlation -.053 .733**

Sig. (2-tailed) .758 .000

Sum of Squares and -852.299 12481.528

Cross-products

-24.351

356.615 Covariance

N 36 36

Test3 Pearson Correlation -.284 .879**

Sig. (2-tailed) .093 .000

Sum of Squares and -5320.115 17486.806

Cross-products Covariance -152.003 499.623

N 36 36

Test4 Pearson Correlation -.095 .905**

Sig. (2-tailed) .583 .000

Sum of Squares and -1825.287 18554.722

Cross-products Covariance -52.151 530.135

N 36 36

Final Pearson Correlation -.115 .962**

Sig. (2-tailed) .504 .000

Sum of Squares and -2155.172 19144.833

Cross-products Covariance -61.576 546.995

N 36 36

PercentPresent Pearson Correlation 1 -.186

Sig. (2-tailed) .278

Sum of Squares and 14090.369 -2785.632

Cross-products Covariance 402.582 -79.589

N 36 36

CourseGrade Pearson Correlation -.186 1

Sig. (2-tailed) .278

Sum of Squares and -2785.632 15923.639

Cross-products Covariance -79.589 454.961

N 36 36

141

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

a. CoReq = No

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

MMLHwAvg 17.1000 7.65143 10

MMLQzAvg 9.8000 7.03641 10

ProbActAvg 36.4000 15.65035 10

Test1 49.9000 27.76268 10

Test2 35.4500 21.60305 10

Test3 41.7000 36.93252 10

Test4 37.4000 36.45149 10

Final 40.3000 35.61850 10

PercentPresent 81.3793 24.01171 10

CourseGrade 47.60 29.125 10

a. CoReq = Yes

Correlationsa

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

MMLHwAvg Pearson Correlation 1 .723* .920

** .602

Sig. (2-tailed) .018 .000 .065

Sum of Squares and 526.900 350.200 991.600 1151.100

Cross-products Covariance 58.544 38.911 110.178 127.900

N 10 10 10 10

MMLQzAvg Pearson Correlation .723* 1 .819

** .806

**

Sig. (2-tailed) .018 .004 .005

Sum of Squares and 350.200 445.600 811.800 1416.800

Cross-products Covariance 38.911 49.511 90.200 157.422

N 10 10 10 10

ProbActAvg Pearson Correlation .920**

.819**

1 .669*

Sig. (2-tailed) .000 .004 .034

Sum of Squares and 991.600 811.800 2204.400 2617.400

Cross-products Covariance 110.178 90.200 244.933 290.822

N 10 10 10 10

142

Test2

Test3

Test4

Final

MMLHwAvg Pearson Correlation .575 .611 .638* .640

*

Sig. (2-tailed) .082 .061 .047 .046

Sum of Squares and 855.550 1553.300 1602.600 1568.700

Cross-products Covariance 95.061 172.589 178.067 174.300

N 10 10 10 10

MMLQzAvg Pearson Correlation .552 .859**

.878**

.864**

Sig. (2-tailed) .098 .001 .001 .001

Sum of Squares and 755.400 2008.400 2027.800 1948.600

Cross-products Covariance 83.933 223.156 225.311 216.511

N 10 10 10 10

ProbActAvg Pearson Correlation .600 .805**

.786**

.815**

Sig. (2-tailed) .067 .005 .007 .004

Sum of Squares and 1826.700 4188.200 4034.400 4090.800

Cross-products Covariance 202.967 465.356 448.267 454.533

N 10 10 10 10

Correlationsa

PercentPrese

nt

CourseGrade

MMLHwAvg Pearson Correlation .904**

.766**

Sig. (2-tailed) .000 .010

Sum of Squares and 1494.483 1536.400

Cross-products Covariance 166.054 170.711

N 10 10

MMLQzAvg Pearson Correlation .747* .938

**

Sig. (2-tailed) .013 .000

Sum of Squares and 1135.172 1730.200

Cross-products Covariance 126.130 192.244

N 10 10

ProbActAvg Pearson Correlation .923**

.911**

Sig. (2-tailed) .000 .000

Sum of Squares and 3122.759 3736.600

Cross-products Covariance 346.973 415.178

N 10 10

143

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

Test1 Pearson Correlation .602 .806**

.669* 1

Sig. (2-tailed) .065 .005 .034

Sum of Squares and 1151.100 1416.800 2617.400 6936.900

Cross-products Covariance 127.900 157.422 290.822 770.767

N 10 10 10 10

Test2 Pearson Correlation .575 .552 .600 .365

Sig. (2-tailed) .082 .098 .067 .299

Sum of Squares and 855.550 755.400 1826.700 1971.950

Cross-products Covariance 95.061 83.933 202.967 219.106

N 10 10 10 10

Test3 Pearson Correlation .611 .859**

.805**

.482

Sig. (2-tailed) .061 .001 .005 .159

Sum of Squares and 1553.300 2008.400 4188.200 4444.700

Cross-products Covariance 172.589 223.156 465.356 493.856

N 10 10 10 10

Test4 Pearson Correlation .638* .878

** .786

** .568

Sig. (2-tailed) .047 .001 .007 .086

Sum of Squares and 1602.600 2027.800 4034.400 5177.400

Cross-products Covariance 178.067 225.311 448.267 575.267

N 10 10 10 10

Final Pearson Correlation .640* .864

** .815

** .512

Sig. (2-tailed) .046 .001 .004 .130

Sum of Squares and 1568.700 1948.600 4090.800 4556.300

Cross-products Covariance 174.300 216.511 454.533 506.256

N 10 10 10 10

PercentPresent Pearson Correlation .904**

.747* .923

** .791

**

Sig. (2-tailed) .000 .013 .000 .006

Sum of Squares and 1494.483 1135.172 3122.759 4743.448

Cross-products Covariance 166.054 126.130 346.973 527.050

N 10 10 10 10

CourseGrade Pearson Correlation .766**

.938**

.911**

.692*

Sig. (2-tailed) .010 .000 .000 .027

Sum of Squares and 1536.400 1730.200 3736.600 5032.600

Cross-products

144

Test2

Test3

Test4

Final

Test1 Pearson Correlation .365 .482 .568 .512

Sig. (2-tailed) .299 .159 .086 .130

Sum of Squares and 1971.950 4444.700 5177.400 4556.300

Cross-products Covariance 219.106 493.856 575.267 506.256

N 10 10 10 10

Test2 Pearson Correlation 1 .533 .312 .406

Sig. (2-tailed) .113 .379 .244

Sum of Squares and 4200.225 3827.350 2214.700 2814.650

Cross-products Covariance 466.692 425.261 246.078 312.739

N 10 10 10 10

Test3 Pearson Correlation .533 1 .915**

.980**

Sig. (2-tailed) .113 .000 .000

Sum of Squares and 3827.350 12276.100 11087.200 11597.900

Cross-products Covariance 425.261 1364.011 1231.911 1288.656

N 10 10 10 10

Test4 Pearson Correlation .312 .915**

1 .961**

Sig. (2-tailed) .379 .000 .000

Sum of Squares and 2214.700 11087.200 11958.400 11223.800

Cross-products Covariance 246.078 1231.911 1328.711 1247.089

N 10 10 10 10

Final Pearson Correlation .406 .980**

.961**

1

Sig. (2-tailed) .244 .000 .000

Sum of Squares and 2814.650 11597.900 11223.800 11418.100

Cross-products Covariance 312.739 1288.656 1247.089 1268.678

N 10 10 10 10

PercentPresent Pearson Correlation .582 .569 .575 .578

Sig. (2-tailed) .077 .086 .082 .080

Sum of Squares and 2718.276 4540.690 4529.655 4452.414

Cross-products Covariance 302.031 504.521 503.295 494.713

N 10 10 10 10

CourseGrade Pearson Correlation .506 .947**

.945**

.966**

Sig. (2-tailed) .136 .000 .000 .000

Sum of Squares and 2864.800 9171.800 9029.600 9020.200

Cross-products

145

Correlationsa

PercentPrese

nt

CourseGrade

Test1 Pearson Correlation .791**

.692*

Sig. (2-tailed) .006 .027

Sum of Squares and 4743.448 5032.600

Cross-products Covariance 527.050 559.178

N 10 10

Test2 Pearson Correlation .582 .506

Sig. (2-tailed) .077 .136

Sum of Squares and 2718.276 2864.800

Cross-products Covariance 302.031 318.311

N 10 10

Test3 Pearson Correlation .569 .947**

Sig. (2-tailed) .086 .000

Sum of Squares and 4540.690 9171.800

Cross-products Covariance 504.521 1019.089

N 10 10

Test4 Pearson Correlation .575 .945**

Sig. (2-tailed) .082 .000

Sum of Squares and 4529.655 9029.600

Cross-products Covariance 503.295 1003.289

N 10 10

Final Pearson Correlation .578 .966**

Sig. (2-tailed) .080 .000

Sum of Squares and 4452.414 9020.200

Cross-products Covariance 494.713 1002.244

N 10 10

PercentPresent Pearson Correlation 1 .754*

Sig. (2-tailed) .012

Sum of Squares and 5189.061 4742.759

Cross-products Covariance 576.562 526.973

N 10 10

CourseGrade Pearson Correlation .754* 1

Sig. (2-tailed) .012

Sum of Squares and 4742.759 7634.400

Cross-products

146

Correlationsa

MMLHwAvg

MMLQzAvg

ProbActAvg

Test1

Covariance

N

170.711

10

192.244

10

415.178

10

559.178

10

Correlationsa

Test2

Test3

Test4

Final

Covariance

N

318.311

10

1019.089

10

1003.289

10

1002.244

10

Correlationsa

PercentPrese

nt

CourseGrade

Covariance

N

526.973

10

848.267

10

Table 26: Test 1 Item Analysis by Tutorial Enrollment

147

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the

1 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.720

Retain the null hypothesis.

The distribution of q2 is the

2 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.795

Retain the null hypothesis.

The distribution of q3 is the

3 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.825

Retain the null hypothesis.

The distribution of q4 is the

4 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.417

Retain the null hypothesis.

The distribution of q5 is the

5 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.983

Retain the null hypothesis.

The distribution of q6 is the

6 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.234

Retain the null hypothesis.

The distribution of q7 is the

7 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.057

Retain the null hypothesis.

The distribution of q8 is the

8 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.889

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

148

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q9 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.236

Retain the null hypothesis.

The distribution of q10 is the

10 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.798

Retain the null hypothesis.

The distribution of q11 is the

11 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.225

Retain the null hypothesis.

The distribution of q12 is the

12 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.060

Retain the null hypothesis.

The distribution of q13 is the

13 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.426

Retain the null hypothesis.

The distribution of q14 is the

14 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.015

Reject the null hypothesis.

The distribution of q15 is the

15 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.367

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

(continued)

149

Null Hypothesis

Test

Sig.

Decision

16

The distribution of q16 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.645

Retain the null hypothesis.

17

The distribution of q17 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.340

Retain the null hypothesis.

18

The distribution of q18 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.296

Retain the null hypothesis.

19

The distribution of q19 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.635

Retain the null hypothesis.

20

The distribution of q20 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.295

Retain the null hypothesis.

21

The distribution of q21 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.419

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

150

Null Hypothesis Test

Sig. Decision

22

The distribution of q22 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.130

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.123

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 27: Test 1 Item Analysis by Section

151

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the

1 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.940

Retain the null hypothesis.

The distribution of q2 is the

2 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.664

Retain the null hypothesis.

The distribution of q3 is the

3 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.354

Retain the null hypothesis.

The distribution of q4 is the

4 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.095

Retain the null hypothesis.

The distribution of q5 is the

5 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.339

Retain the null hypothesis.

The distribution of q6 is the

6 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.979

Retain the null hypothesis.

The distribution of q7 is the

7 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.774

Retain the null hypothesis.

The distribution of q8 is the

8 same across categories of Class.

Independent- Samples Mann- Whitney U Test

.519

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

152

Null Hypothesis Test

Sig.

Decision

9

The distribution of q9 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.534

Retain the null hypothesis.

10

The distribution of q10 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.557

Retain the null hypothesis.

11

The distribution of q11 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.695

Retain the null hypothesis.

12

The distribution of q12 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.373

Retain the null hypothesis.

13

The distribution of q13 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.735

Retain the null hypothesis.

14

The distribution of q14 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.993

Retain the null hypothesis.

15

The distribution of q15 is the same across categories of Class.

Independent -Samples Mann- Whitney U Test

.450

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

153

Null Hypothesis

Test

Sig.

Decision

16

The distribution of q16 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.523

Retain the null hypothesis.

17

Independent-

The distribution of q17 is the same Samples

across categories of Class.

Mann-

Test

.130

Retain the null hypothesis.

18

Independent-

The distribution of q18 is the same Samples

across categories of Class.

Mann-

Test

.532

Retain the null hypothesis.

19

Independent-

The distribution of q19 is the same Samples

across categories of Class.

Mann-

Test

.057

Retain the null hypothesis.

20

Independent-

The distribution of q20 is the same Samples

across categories of Class.

Mann-

Test

.022

Reject the null hypothesis.

21

Independent-

The distribution of q21 is the same Samples

across categories of Class.

Mann-

Test

.017

Reject the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

154

Null Hypothesis Test Sig.

Decision

22

Independent-

The distribution of q22 is the same Samples

across categories of Class.

Mann-

Test

.001

Reject the null hypothesis.

23

The distribution of TestScore is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.189

Retain the null hypothesis.

Whitney U

Asymptotic significances are displayed. The significance level is .05.

Table 28: Test 1 Item Analysis by Gender

155

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.146

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.228

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.918

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.039

Reject the null hypothesis.

The distribution of q5 is the same

5 across categories of Gender.

Independent- Samples Mann- Whitney U Test

.569

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.529

Retain the null hypothesis.

The distribution of q7 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.772

Retain the null hypothesis.

The distribution of q8 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.518

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

156

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q9 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.730

Retain the null hypothesis.

The distribution of q10 is the

10 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.348

Retain the null hypothesis.

The distribution of q11 is the

11 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.929

Retain the null hypothesis.

The distribution of q12 is the

12 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.357

Retain the null hypothesis.

The distribution of q13 is the

13 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.851

Retain the null hypothesis.

The distribution of q14 is the

14 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.196

Retain the null hypothesis.

The distribution of q15 is the

15 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.395

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

157

Null Hypothesis Test

Sig.

Decision

16

The distribution of q16 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.961

Retain the null hypothesis.

17

The distribution of q17 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.302

Retain the null hypothesis.

18

The distribution of q18 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.128

Retain the null hypothesis.

19

The distribution of q19 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.413

Retain the null hypothesis.

20

The distribution of q20 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.065

Retain the null hypothesis.

21

The distribution of q21 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.345

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

158

Null Hypothesis

Test

Sig.

Decision

22

The distribution of q22 is the same across categories of Gender.

Independent -Samples Mann- Whitney U Test

.036

Reject the null hypothesis.

23

The distribution of TestScore is the same across categories of Gender.

Independent -Samples Mann- Whitney U Test

.664

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 29: Test 1 Item Analysis by Tutorial Enrollment (Test scores of zero excluded)

159

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.720

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.795

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.825

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.417

Retain the null hypothesis.

The distribution of q5 is the same

5 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.983

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.234

Retain the null hypothesis.

The distribution of q7 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.057

Retain the null hypothesis.

The distribution of q8 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.889

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

160

Independent-

Samples Reject the Mann- .015 null Whitney U hypothesis. Test

Independent-

Samples Retain the Mann- .367 null Whitney U hypothesis. Test

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q9 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.236

Retain the null hypothesis.

10 The distribution of q10 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.798

Retain the null hypothesis.

11 The distribution of q11 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.225

Retain the null hypothesis.

12 The distribution of q12 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.060

Retain the null hypothesis.

The distribution of q13 is the same

13 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.426

Retain the null hypothesis.

14 The distribution of q14 is the same across categories of CoReq.

The distribution of q15 is the same across categories of CoReq.

Asymptotic significances are displayed. The significance level is .05.

161

Null Hypothesis

Test Sig.

Decision

16

The distribution of q16 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.645

Retain the null hypothesis.

17

The distribution of q17 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.340

Retain the null hypothesis.

18

The distribution of q18 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.296

Retain the null hypothesis.

19

The distribution of q19 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.635

Retain the null hypothesis.

20

The distribution of q20 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.295

Retain the null hypothesis.

21

The distribution of q21 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.419

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

162

Null Hypothesis

Test

Sig.

Decision

22

The distribution of q22 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.130

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.179

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 30:Test 1 Item Analysis by Section (Test scores of zero excluded)

163

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the

1 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.940

Retain the null hypothesis.

The distribution of q2 is the

2 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.664

Retain the null hypothesis.

The distribution of q3 is the

3 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.354

Retain the null hypothesis.

The distribution of q4 is the

4 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.095

Retain the null hypothesis.

The distribution of q5 is the

5 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.339

Retain the null hypothesis.

The distribution of q6 is the

6 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.979

Retain the null hypothesis.

The distribution of q7 is the

7 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.774

Retain the null hypothesis.

The distribution of q8 is the

8 same across categories of Class.

Independent -Samples Mann- Whitney U Test

.519

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

164

Independent-

Samples Retain the Mann- .450 null Whitney U hypothesis. Test

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q9 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.534

Retain the null hypothesis.

10 The distribution of q10 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.557

Retain the null hypothesis.

11 The distribution of q11 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.695

Retain the null hypothesis.

12 The distribution of q12 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.373

Retain the null hypothesis.

The distribution of q13 is the same

13 across categories of Class.

Independent- Samples Mann- Whitney U Test

.735

Retain the null hypothesis.

14 The distribution of q14 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.993

Retain the null hypothesis.

The distribution of q15 is the same across categories of Class.

Asymptotic significances are displayed. The significance level is .05.

165

Null Hypothesis

Test

Sig.

Decision

16

The distribution of q16 is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.523

Retain the null hypothesis.

17

Independent-

The distribution of q17 is the same Samples

across categories of Class.

Mann-

Test

.130

Retain the null hypothesis.

18

Independent-

The distribution of q18 is the same Samples

across categories of Class.

Mann-

Test

.532

Retain the null hypothesis.

19

Independent-

The distribution of q19 is the same Samples

across categories of Class.

Mann-

Test

.057

Retain the null hypothesis.

20

Independent-

The distribution of q20 is the same Samples

across categories of Class.

Mann-

Test

.022

Reject the null hypothesis.

21

Independent-

The distribution of q21 is the same Samples

across categories of Class.

Mann-

Test

.017

Reject the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

166

Null Hypothesis Test

Sig. Decision

22

Independent-

The distribution of q22 is the same Samples

across categories of Class.

Mann-

Test

.001

Reject the null hypothesis.

23

Whitney U

The distribution of TestScore is the same across categories of Class.

Independent- Samples Mann- Whitney U Test

.384

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 31: Test 1 Item Analysis by Gender (Test scores of zero excluded)

167

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the

1 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.146

Retain the null hypothesis.

The distribution of q2 is the

2 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.228

Retain the null hypothesis.

The distribution of q3 is the

3 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.918

Retain the null hypothesis.

The distribution of q4 is the

4 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.039

Reject the null hypothesis.

The distribution of q5 is the

5 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.569

Retain the null hypothesis.

The distribution of q6 is the

6 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.529

Retain the null hypothesis.

The distribution of q7 is the

7 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.772

Retain the null hypothesis.

The distribution of q8 is the

8 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.518

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

168

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q9 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.730

Retain the null hypothesis.

The distribution of q10 is the

10 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.348

Retain the null hypothesis.

The distribution of q11 is the

11 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.929

Retain the null hypothesis.

The distribution of q12 is the

12 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.357

Retain the null hypothesis.

The distribution of q13 is the

13 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.851

Retain the null hypothesis.

The distribution of q14 is the

14 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.196

Retain the null hypothesis.

The distribution of q15 is the

15 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.395

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

169

Null Hypothesis

Test Sig.

Decision

16

The distribution of q16 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.961

Retain the null hypothesis.

17

The distribution of q17 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.302

Retain the null hypothesis.

18

The distribution of q18 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.128

Retain the null hypothesis.

19

The distribution of q19 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.413

Retain the null hypothesis.

20

The distribution of q20 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.065

Retain the null hypothesis.

21

The distribution of q21 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.345

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

170

Null Hypothesis

Test

Sig. Decision

22

The distribution of q22 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.036

Reject the null hypothesis.

23

The distribution of TestScore is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.457

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 32: Test 2 Item Analysis by Tutorial Enrollment

171

Null Hypothesis

Test

Sig.

Decision

The distribution of q1to8 is the

1 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .661

Retain the null hypothesis.

2 The distribution of q9 is the same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .793

Retain the null hypothesis.

The distribution of q10a is the

3 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .766

Retain the null hypothesis.

The distribution of q10b is the

4 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .388

Retain the null hypothesis.

The distribution of q10cd is the

5 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .611

Retain the null hypothesis.

The distribution of q11 is the

6 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .820

Retain the null hypothesis.

The distribution of q12 is the

7 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .120

Retain the null hypothesis.

The distribution of q13 is the

8 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .930

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

172

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the

9 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .930

Retain the null hypothesis.

The distribution of q15 is the

10 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .369

Retain the null hypothesis.

The distribution of q16 is the

11 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .539

Retain the null hypothesis.

The distribution of q17 is the

12 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .332

Retain the null hypothesis.

The distribution of q18 is the

13 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .408

Retain the null hypothesis.

The distribution of q19 is the

14 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .847

Retain the null hypothesis.

The distribution of q20 is the

15 same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1 .058

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

173

Null Hypothesis

Test

Sig.

Decision

16

The distribution of q21a is the same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

1

Reject the null hypothesis.

17

The distribution of q21b is the same across categories of

Independent

1

Retain the null

-Samples Mann-

CoReq. Whitney U hypothesis. Test

18

The distribution of q21c is the same across categories of

Independent

1

Retain the null

-Samples Mann-

CoReq. Whitney U hypothesis. Test

19

The distribution of q22 is the same across categories of

Independent

1

Retain the null

-Samples Mann-

CoReq. Whitney U hypothesis. Test

20

The distribution of TestScore is the same across categories of CoReq.

Independent -Samples Mann- Whitney U Test

.150

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 33: Test 2 Item Analysis by Section

174

Independent

-Samples Retain the Mann- .456 null Whitney U hypothesis. Test

Null Hypothesis

Test

Sig.

Decision

The distribution of q1to8 is the

1 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.680

Retain the null hypothesis.

2 The distribution of q9 is the same across categories of Section.

The distribution of q10a is the 3 same across categories of

Section.

Independent -Samples Mann- Whitney U Test

.667

Retain the null hypothesis.

The distribution of q10b is the

4 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.509

Retain the null hypothesis.

The distribution of q10cd is the

5 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.012

Reject the null hypothesis.

The distribution of q11 is the

6 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.916

Retain the null hypothesis.

The distribution of q12 is the

7 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.778

Retain the null hypothesis.

The distribution of q13 is the

8 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.314

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

175

Independent-

Samples Retain the Mann- .623 null Whitney U hypothesis. Test

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.876

Retain the null hypothesis.

10 The distribution of q15 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.240

Retain the null hypothesis.

11 The distribution of q16 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.139

Retain the null hypothesis.

12 The distribution of q17 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.223

Retain the null hypothesis.

The distribution of q18 is the same

13 across categories of Section.

Independent- Samples Mann- Whitney U Test

.522

Retain the null hypothesis.

14 The distribution of q19 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.148

Retain the null hypothesis.

The distribution of q20 is the same across categories of Section.

Asymptotic significances are displayed. The significance level is .05.

176

Null Hypothesis Test Sig.

Decision

16

The distribution of q21a is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.257

Retain the null hypothesis.

17

The distribution of q21b is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.665

Retain the null hypothesis.

18

The distribution of q21c is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.158

Retain the null hypothesis.

19

The distribution of q22 is the sam across categories of Section.

Independent-

e Samples Mann- Whitney U Test

.717

Retain the null hypothesis.

20

Independent-

The distribution of TestScore is the Samples same across categories of Mann- Section. Whitney U

Test

.686

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 34: Test 2 Item Analysis by Gender

177

Null Hypothesis Test Sig.

Decision

1

The distribution of q1to8 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

2

The distribution of q9 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

3

The distribution of q10a is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

4

The distribution of q10b is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

5

The distribution of q10cd is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

6

The distribution of q11 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

7

The distribution of q12 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

8

The distribution of q13 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .498

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

178

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the

9 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .498

Retain the null hypothesis.

The distribution of q15 is the

10 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .652

Retain the null hypothesis.

The distribution of q16 is the

11 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .282

Retain the null hypothesis.

The distribution of q17 is the

12 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .632

Retain the null hypothesis.

The distribution of q18 is the

13 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .365

Retain the null hypothesis.

The distribution of q19 is the

14 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .071

Retain the null hypothesis.

The distribution of q20 is the

15 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .019

Reject the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

179

Null Hypothesis

Test

Sig. Decision

16

The distribution of q21a is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

17

The distribution of q21b is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

18

The distribution of q21c is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

19

The distribution of q22 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

20

The distribution of TestScore is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.596

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 35: Test 2 Item Analysis by Tutorial Enrollment (Test scores of zero excluded)

180

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1to8 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .661

Retain the null hypothesis.

2 The distribution of q9 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .793

Retain the null hypothesis.

3 The distribution of q10a is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .766

Retain the null hypothesis.

4 The distribution of q10b is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .388

Retain the null hypothesis.

The distribution of q10cd is the

5 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .611

Retain the null hypothesis.

6 The distribution of q11 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .820

Retain the null hypothesis.

The distribution of q12 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .120

Retain the null hypothesis.

The distribution of q13 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .930

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

181

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .930

Retain the null hypothesis.

10 The distribution of q15 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .369

Retain the null hypothesis.

11 The distribution of q16 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .539

Retain the null hypothesis.

12 The distribution of q17 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .332

Retain the null hypothesis.

The distribution of q18 is the same

13 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .408

Retain the null hypothesis.

14 The distribution of q19 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .847

Retain the null hypothesis.

The distribution of q20 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .058

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

182

Null Hypothesis Test Sig.

Decision

16

The distribution of q21a is the same across categories of CoReq.

Independent- Samples Mann-

Test

1

Reject the null hypothesis.

17

The distribution of q21b is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

18

The distribution of q21c is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .408

Retain the null hypothesis.

19

The distribution of q22 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .208

Retain the null hypothesis.

20

The distribution of TestScore is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .760

Retain the null hypothesis.

Whitney U

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 36: Test 2 Item Analysis by Section (Test scores of zero excluded)

183

Independent-

Samples Retain the Mann- .456 null Whitney U hypothesis. Test

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1to8 is the

1 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.680

Retain the null hypothesis.

2 The distribution of q9 is the same across categories of Section.

The distribution of q10a is the 3 same across categories of

Section.

Independent- Samples Mann- Whitney U Test

.667

Retain the null hypothesis.

The distribution of q10b is the

4 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.509

Retain the null hypothesis.

The distribution of q10cd is the

5 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.012

Reject the null hypothesis.

6 The distribution of q11 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.916

Retain the null hypothesis.

The distribution of q12 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.778

Retain the null hypothesis.

The distribution of q13 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.314

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

184

Independent-

Samples Retain the Mann- .623 null Whitney U hypothesis. Test

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.876

Retain the null hypothesis.

10 The distribution of q15 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.240

Retain the null hypothesis.

11 The distribution of q16 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.139

Retain the null hypothesis.

12 The distribution of q17 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.223

Retain the null hypothesis.

The distribution of q18 is the same

13 across categories of Section.

Independent- Samples Mann- Whitney U Test

.522

Retain the null hypothesis.

14 The distribution of q19 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.148

Retain the null hypothesis.

The distribution of q20 is the same across categories of Section.

Asymptotic significances are displayed. The significance level is .05.

185

Null Hypothesis

Test

Sig.

Decision

16

The distribution of q21a is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.257

Retain the null hypothesis.

17

The distribution of q21b is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.665

Retain the null hypothesis.

18

The distribution of q21c is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.158

Retain the null hypothesis.

19

The distribution of q22 is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.717

Retain the null hypothesis.

20

The distribution of TestScore is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.913

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 37: Test 2 Item Analysis by Gender (Test scores of zero excluded

186

Null Hypothesis

Test

Sig.

Decision

The distribution of q1to8 is the

1 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .062

Retain the null hypothesis.

2 The distribution of q9 is the same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .535

Retain the null hypothesis.

The distribution of q10a is the

3 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .756

Retain the null hypothesis.

The distribution of q10b is the

4 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .652

Retain the null hypothesis.

The distribution of q10cd is the

5 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .632

Retain the null hypothesis.

The distribution of q11 is the

6 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .350

Retain the null hypothesis.

The distribution of q12 is the

7 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .553

Retain the null hypothesis.

The distribution of q13 is the

8 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .498

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

187

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the

9 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .498

Retain the null hypothesis.

The distribution of q15 is the

10 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .652

Retain the null hypothesis.

The distribution of q16 is the

11 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .282

Retain the null hypothesis.

The distribution of q17 is the

12 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .632

Retain the null hypothesis.

The distribution of q18 is the

13 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .365

Retain the null hypothesis.

The distribution of q19 is the

14 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .071

Retain the null hypothesis.

The distribution of q20 is the

15 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .019

Reject the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

188

Null Hypothesis

Test

Sig. Decision

16

The distribution of q21a is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

17

The distribution of q21b is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

18

The distribution of q21c is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

19

The distribution of q22 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

20

The distribution of TestScore is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Gender. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 38: Test 3 Item Analysis by CoReq

189

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .496

Retain the null hypothesis.

2 The distribution of q2a is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

3 The distribution of q2b is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .804

Retain the null hypothesis.

4 The distribution of q2c is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .055

Retain the null hypothesis.

The distribution of q3 is the same

5 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .209

Retain the null hypothesis.

6 The distribution of q4 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .396

Retain the null hypothesis.

The distribution of q5 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .144

Retain the null hypothesis.

The distribution of q6 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .908

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

190

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q7 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .025

Reject the null hypothesis.

10 The distribution of q8 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

11 The distribution of q9 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .342

Retain the null hypothesis.

The distribution of q10 is the

12 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .987

Retain the null hypothesis.

The distribution of q11 is the

13 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .728

Retain the null hypothesis.

The distribution of q12 is the

14 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .164

Retain the null hypothesis.

The distribution of q13 is the

15 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

191

Null Hypothesis

Test Sig.

Decision

16

The distribution of q14 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

17

The distribution of q15 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

18

The distribution of q16 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

19

The distribution of q17 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

20

The distribution of q18 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

21

The distribution of q19 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .908

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

192

Null Hypothesis

Test

Sig.

Decision

22

The distribution of q20 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1

.703

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

.149

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 39: Test 3 Item Analysis by Section

193

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.712

Retain the null hypothesis.

The distribution of q2a is the

2 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.706

Retain the null hypothesis.

The distribution of q2b is the

3 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.725

Retain the null hypothesis.

The distribution of q2c is the

4 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.484

Retain the null hypothesis.

The distribution of q3 is the same

5 across categories of Section.

Independent- Samples Mann- Whitney U Test

.459

Retain the null hypothesis.

6 The distribution of q4 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.761

Retain the null hypothesis.

The distribution of q5 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.451

Retain the null hypothesis.

The distribution of q6 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.089

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

194

Independent-

Samples Retain the Mann- 1.000 null Whitney U hypothesis. Test

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q7 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.651

Retain the null hypothesis.

10 The distribution of q8 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.196

Retain the null hypothesis.

11 The distribution of q9 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.633

Retain the null hypothesis.

12 The distribution of q10 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.843

Retain the null hypothesis.

The distribution of q11 is the same

13 across categories of Section.

Independent- Samples Mann- Whitney U Test

.182

Retain the null hypothesis.

14 The distribution of q12 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.127

Retain the null hypothesis.

The distribution of q13 is the same across categories of Section.

Asymptotic significances are displayed. The significance level is .05.

195

16

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.752

Retain the null hypothesis.

17 The distribution of q15 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.014

Reject the null hypothesis.

18 The distribution of q16 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.962

Retain the null hypothesis.

19 The distribution of q17 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.366

Retain the null hypothesis.

The distribution of q18 is the same

20 across categories of Section.

Independent- Samples Mann- Whitney U Test

.803

Retain the null hypothesis.

21 The distribution of q19 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.607

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

196

Null Hypothesis Test

Sig. Decision

22

The distribution of q20 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.363

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.623

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 40: Test 3 Item Analysis by Gender

197

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .396

Retain the null hypothesis.

2 The distribution of q2a is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .914

Retain the null hypothesis.

3 The distribution of q2b is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .724

Retain the null hypothesis.

4 The distribution of q2c is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .939

Retain the null hypothesis.

The distribution of q3 is the same

5 across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .818

Retain the null hypothesis.

6 The distribution of q4 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .488

Retain the null hypothesis.

The distribution of q5 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .528

Retain the null hypothesis.

The distribution of q6 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .890

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

198

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q7 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .939

Retain the null hypothesis.

10 The distribution of q8 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .331

Retain the null hypothesis.

11 The distribution of q9 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .488

Retain the null hypothesis.

The distribution of q10 is the

12 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .233

Retain the null hypothesis.

The distribution of q11 is the

13 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .590

Retain the null hypothesis.

The distribution of q12 is the

14 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .167

Retain the null hypothesis.

The distribution of q13 is the

15 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

199

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the

16 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .988

Retain the null hypothesis.

The distribution of q15 is the

17 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .020

Reject the null hypothesis.

The distribution of q16 is the

18 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .488

Retain the null hypothesis.

The distribution of q17 is the

19 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .331

Retain the null hypothesis.

The distribution of q18 is the

20 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .414

Retain the null hypothesis.

The distribution of q19 is the

21 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .678

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

200

Null Hypothesis

Test

Sig.

Decision

22

The distribution of q20 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1

.842

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

.488

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 41: Test 3 Item Analysis by Tutorial Enrollment (Test scores of zero excluded)

201

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .496

Retain the null hypothesis.

2 The distribution of q2a is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

3 The distribution of q2b is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .804

Retain the null hypothesis.

4 The distribution of q2c is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .055

Retain the null hypothesis.

The distribution of q3 is the same

5 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .209

Retain the null hypothesis.

6 The distribution of q4 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .396

Retain the null hypothesis.

The distribution of q5 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .144

Retain the null hypothesis.

The distribution of q6 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .908

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

202

Independent-

Samples Mann- .342 Whitney U Test

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q7 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .025

Reject the null hypothesis.

10 The distribution of q8 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

11 The distribution of q9 is the same across categories of CoReq.

1 Retain the null hypothesis.

The distribution of q10 is the

12 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .987

Retain the null hypothesis.

The distribution of q11 is the

13 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .728

Retain the null hypothesis.

The distribution of q12 is the

14 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .164

Retain the null hypothesis.

The distribution of q13 is the

15 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

203

Null Hypothesis

Test Sig.

Decision

16

The distribution of q14 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

17

The distribution of q15 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

18

The distribution of q16 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

19

The distribution of q17 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

20

The distribution of q18 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

21

The distribution of q19 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .908

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

204

Null Hypothesis Test

Sig. Decision

22

The distribution of q20 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .703

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .235

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 42: Test 3 Item Analysis by Section (Test scores of zero excluded)

205

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.712

Retain the null hypothesis.

The distribution of q2a is the

2 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.706

Retain the null hypothesis.

The distribution of q2b is the

3 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.725

Retain the null hypothesis.

The distribution of q2c is the

4 same across categories of Section.

Independent- Samples Mann- Whitney U Test

.484

Retain the null hypothesis.

The distribution of q3 is the same

5 across categories of Section.

Independent- Samples Mann- Whitney U Test

.459

Retain the null hypothesis.

6 The distribution of q4 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.761

Retain the null hypothesis.

The distribution of q5 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.451

Retain the null hypothesis.

The distribution of q6 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.089

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

206

Independent-

Samples Retain the Mann- 1.000 null Whitney U hypothesis. Test

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q7 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.651

Retain the null hypothesis.

10 The distribution of q8 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.196

Retain the null hypothesis.

11 The distribution of q9 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.633

Retain the null hypothesis.

12 The distribution of q10 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.843

Retain the null hypothesis.

The distribution of q11 is the same

13 across categories of Section.

Independent- Samples Mann- Whitney U Test

.182

Retain the null hypothesis.

14 The distribution of q12 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.127

Retain the null hypothesis.

The distribution of q13 is the same across categories of Section.

Asymptotic significances are displayed. The significance level is .05.

207

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the

16 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.752

Retain the null hypothesis.

The distribution of q15 is the

17 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.014

Reject the null hypothesis.

The distribution of q16 is the

18 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.962

Retain the null hypothesis.

The distribution of q17 is the

19 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.366

Retain the null hypothesis.

The distribution of q18 is the

20 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.803

Retain the null hypothesis.

The distribution of q19 is the

21 same across categories of Section.

Independent -Samples Mann- Whitney U Test

.607

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

208

Null Hypothesis

Test

Sig. Decision

22

The distribution of q20 is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.363

Retain the null hypothesis.

23

The distribution of TestScore is the same across categories of Section.

Independent -Samples Mann- Whitney U Test

.786

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

Table 43: Test 3 Item Analysis by Gender (Test scores of zero excluded)

209

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .396

Retain the null hypothesis.

The distribution of q2a is the

2 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .914

Retain the null hypothesis.

The distribution of q2b is the

3 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .724

Retain the null hypothesis.

The distribution of q2c is the

4 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .939

Retain the null hypothesis.

The distribution of q3 is the same

5 across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .818

Retain the null hypothesis.

6 The distribution of q4 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .488

Retain the null hypothesis.

The distribution of q5 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .528

Retain the null hypothesis.

The distribution of q6 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .890

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

210

9

Null Hypothesis

Test

Sig.

Decision

The distribution of q7 is the same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .939

Retain the null hypothesis.

10 The distribution of q8 is the same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .331

Retain the null hypothesis.

11 The distribution of q9 is the same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .488

Retain the null hypothesis.

The distribution of q10 is the

12 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .233

Retain the null hypothesis.

The distribution of q11 is the

13 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .590

Retain the null hypothesis.

The distribution of q12 is the

14 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 .167

Retain the null hypothesis.

The distribution of q13 is the

15 same across categories of Gender.

Independent -Samples Mann- Whitney U Test

1 1.000

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

211

16

Null Hypothesis

Test

Sig.

Decision

The distribution of q14 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .988

Retain the null hypothesis.

17 The distribution of q15 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .020

Reject the null hypothesis.

18 The distribution of q16 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .488

Retain the null hypothesis.

19 The distribution of q17 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .331

Retain the null hypothesis.

The distribution of q18 is the same

20 across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .414

Retain the null hypothesis.

21 The distribution of q19 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .678

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

212

Null Hypothesis

Test Sig.

Decision

22

The distribution of q20 is the same across categories of

Independent

1

Retain the null

-Samples Mann-

Gender. Whitney U hypothesis. Test

23

The distribution of TestScore is the same across categories of

Independent

1

Retain the null

-Samples Mann-

Gender. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 44: Test 4 Item Analysis by Tutorial Enrollment

213

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .555

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .797

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .685

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .107

Retain the null hypothesis.

The distribution of q5 is the same

5 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .883

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .854

Retain the null hypothesis.

The distribution of q7 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .555

Retain the null hypothesis.

The distribution of q8a is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .530

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

214

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q8bc is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .395

Retain the null hypothesis.

10 The distribution of q9 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .416

Retain the null hypothesis.

11 The distribution of q10 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .530

Retain the null hypothesis.

12 The distribution of q11 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .265

Retain the null hypothesis.

The distribution of q12ab is the

13 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .912

Retain the null hypothesis.

14 The distribution of q12c is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .282

Retain the null hypothesis.

The distribution of q12d is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .167

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

215

16

22

Null Hypothesis

Test

Sig.

Decision

The distribution of q12e is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .580

Retain the null hypothesis.

17 The distribution of q12f is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .883

Retain the null hypothesis.

18 The distribution of q13 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .606

Retain the null hypothesis.

19 The distribution of q14 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .912

Retain the null hypothesis.

The distribution of q15 is the same

20 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .825

Retain the null hypothesis.

21 The distribution of q16a is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .825

Retain the null hypothesis.

The distribution of q16b is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .606

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

216

Null Hypothesis

Test

Sig.

Decision

23

The distribution of q17 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

24

The distribution of q18 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

25

The distribution of q19a is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

26

The distribution of q19b is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

27

The distribution of TestScore is the same across categories of

Independent-

1

Retain the null

Samples Mann-

CoReq. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 45: Test 4 Item Analysis by Section

217

1

7

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .954

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .728

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .039

Reject the null hypothesis.

The distribution of q5 is the same

5 across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .772

Retain the null hypothesis.

The distribution of q7 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .416

Retain the null hypothesis.

The distribution of q8a is the

8 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .706

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

218

Independent-

Samples Mann- .581 Whitney U Test

Null Hypothesis

Test

Sig.

Decision

The distribution of q8bc is the

9 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .064

Retain the null hypothesis.

10 The distribution of q9 is the same across categories of Section.

1 Retain the null hypothesis.

The distribution of q10 is the

11 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .486

Retain the null hypothesis.

The distribution of q11 is the

12 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .416

Retain the null hypothesis.

The distribution of q12ab is the

13 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .750

Retain the null hypothesis.

The distribution of q12c is the

14 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .232

Retain the null hypothesis.

The distribution of q12d is the

15 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .170

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

219

Null Hypothesis

Test

Sig.

Decision

The distribution of q12e is the

16 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .107

Retain the null hypothesis.

The distribution of q12f is the

17 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .622

Retain the null hypothesis.

The distribution of q13 is the

18 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

The distribution of q14 is the

19 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .839

Retain the null hypothesis.

The distribution of q15 is the

20 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .189

Retain the null hypothesis.

The distribution of q16a is the

21 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .144

Retain the null hypothesis.

The distribution of q16b is the

22 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .885

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

220

Null Hypothesis Test Sig.

Decision

23

The distribution of q17 is the same across categories of Section.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

24

The distribution of q18 is the same across categories of Section.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

25

The distribution of q19a is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

26

The distribution of q19b is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

27

The distribution of TestScore is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

.945

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 46: Test 4 Item Analysis by Gender

221

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .293

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .840

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .840

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .636

Retain the null hypothesis.

The distribution of q5 is the same

5 across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .521

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .973

Retain the null hypothesis.

The distribution of q7 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .026

Reject the null hypothesis.

The distribution of q8a is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .397

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

222

Null Hypothesis

Test

Sig.

Decision

The distribution of q8bc is the

9 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .184

Retain the null hypothesis.

10 The distribution of q9 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .866

Retain the null hypothesis.

The distribution of q10 is the

11 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .636

Retain the null hypothesis.

The distribution of q11 is the

12 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .813

Retain the null hypothesis.

The distribution of q12ab is the

13 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .946

Retain the null hypothesis.

The distribution of q12c is the

14 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .248

Retain the null hypothesis.

The distribution of q12d is the

15 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .457

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

223

Null Hypothesis

Test

Sig.

Decision

The distribution of q12e is the

16 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .736

Retain the null hypothesis.

The distribution of q12f is the

17 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .277

Retain the null hypothesis.

The distribution of q13 is the

18 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .457

Retain the null hypothesis.

The distribution of q14 is the

19 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .919

Retain the null hypothesis.

The distribution of q15 is the

20 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .184

Retain the null hypothesis.

The distribution of q16a is the

21 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .636

Retain the null hypothesis.

The distribution of q16b is the

22 same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .325

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

224

Null Hypothesis Test

Sig. Decision

23

The distribution of q17 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

24

The distribution of q18 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

25

The distribution of q19a is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

26

The distribution of q19b is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

27

The distribution of TestScore is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 47: Test 4 Item Analysis by Tutorial Enrollment (Test scores of zero excluded)

225

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .555

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .797

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .685

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .107

Retain the null hypothesis.

The distribution of q5 is the same

5 across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .883

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .854

Retain the null hypothesis.

The distribution of q7 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .555

Retain the null hypothesis.

The distribution of q8a is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .530

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

226

9

15

Null Hypothesis

Test

Sig.

Decision

The distribution of q8bc is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .395

Retain the null hypothesis.

10 The distribution of q9 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .416

Retain the null hypothesis.

11 The distribution of q10 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .530

Retain the null hypothesis.

12 The distribution of q11 is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .265

Retain the null hypothesis.

The distribution of q12ab is the

13 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .912

Retain the null hypothesis.

14 The distribution of q12c is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .282

Retain the null hypothesis.

The distribution of q12d is the same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .167

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

227

Null Hypothesis

Test

Sig.

Decision

The distribution of q12e is the

16 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .580

Retain the null hypothesis.

The distribution of q12f is the

17 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .883

Retain the null hypothesis.

The distribution of q13 is the

18 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .606

Retain the null hypothesis.

The distribution of q14 is the

19 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .912

Retain the null hypothesis.

The distribution of q15 is the

20 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .825

Retain the null hypothesis.

The distribution of q16a is the

21 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .825

Retain the null hypothesis.

The distribution of q16b is the

22 same across categories of CoReq.

Independent- Samples Mann- Whitney U Test

1 .606

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

228

Null Hypothesis Test

Sig. Decision

23

The distribution of q17 is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

24

The distribution of q18 is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

25

The distribution of q19a is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

26

The distribution of q19b is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

27

The distribution of TestScore is the same across categories of CoReq.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 48: Test 4 Item Analysis by Section (Test scores of zero excluded)

229

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .954

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .728

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .039

Reject the null hypothesis.

The distribution of q5 is the same

5 across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .772

Retain the null hypothesis.

The distribution of q7 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .416

Retain the null hypothesis.

The distribution of q8a is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .706

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

230

Null Hypothesis

Test

Sig.

Decision

The distribution of q8bc is the

9 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .064

Retain the null hypothesis.

10 The distribution of q9 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .581

Retain the null hypothesis.

11 The distribution of q10 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .486

Retain the null hypothesis.

12 The distribution of q11 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .416

Retain the null hypothesis.

The distribution of q12ab is the

13 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .750

Retain the null hypothesis.

The distribution of q12c is the

14 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .232

Retain the null hypothesis.

The distribution of q12d is the

15 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .170

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

231

Null Hypothesis

Test

Sig.

Decision

The distribution of q12e is the

16 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .107

Retain the null hypothesis.

The distribution of q12f is the

17 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .622

Retain the null hypothesis.

18 The distribution of q13 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .816

Retain the null hypothesis.

19 The distribution of q14 is the same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .839

Retain the null hypothesis.

The distribution of q15 is the same

20 across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .189

Retain the null hypothesis.

The distribution of q16a is the

21 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .144

Retain the null hypothesis.

The distribution of q16b is the

22 same across categories of Section.

Independent- Samples Mann- Whitney U Test

1 .885

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

232

Null Hypothesis

Test

Sig.

Decision

23

The distribution of q17 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

24

The distribution of q18 is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

25

The distribution of q19a is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

26

The distribution of q19b is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

27

The distribution of TestScore is the same across categories of

Independent-

1

Retain the null

Samples Mann-

Section. Whitney U hypothesis. Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 49: Test 4 Item Analysis by Gender (Test scores of zero excluded)

233

1

7

8

Null Hypothesis

Test

Sig.

Decision

The distribution of q1 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .293

Retain the null hypothesis.

2 The distribution of q2 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .840

Retain the null hypothesis.

3 The distribution of q3 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .840

Retain the null hypothesis.

4 The distribution of q4 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .636

Retain the null hypothesis.

The distribution of q5 is the same

5 across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .521

Retain the null hypothesis.

6 The distribution of q6 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .973

Retain the null hypothesis.

The distribution of q7 is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .026

Reject the null hypothesis.

The distribution of q8a is the same across categories of Gender.

Independent- Samples Mann- Whitney U Test

1 .397

Retain the null hypothesis.

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

234

Null Hypothesis Test

Sig. Decision

9

The distribution of q8bc is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

10

The distribution of q9 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

11

The distribution of q10 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

12

The distribution of q11 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

13

The distribution of q12ab is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

14

The distribution of q12c is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

15

The distribution of q12d is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

235

Null Hypothesis Test

Sig. Decision

16

The distribution of q12e is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

17

The distribution of q12f is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

18

The distribution of q13 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

19

The distribution of q14 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

20

The distribution of q15 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

21

The distribution of q16a is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

22

The distribution of q16b is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

236

Null Hypothesis Test Sig.

Decision

23

The distribution of q17 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

24

The distribution of q18 is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

25

The distribution of q19a is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

26

The distribution of q19b is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

27

The distribution of TestScore is the same across categories of Gender.

Independent-

1

Retain the null hypothesis.

Samples Mann- Whitney U Test

Asymptotic significances are displayed. The significance level is .05.

1 Exact significance is displayed for this test.

Table 50: Regression Model 1

237

Descriptive Statistics

Mean Std. Deviation N

CourseGrade 61.44 23.392 39

NumRemedial 1.95 2.077 39

Num118Attempts .46 .682 39

NumCredits 10.79 3.310 39

GPACUMFall2011 2.926692 .6822241 39

Correlations

CourseGrade

NumRemedial

Num118Atte

mpts

NumCredits

Pearson Correlation CourseGrade 1.000 -.004 .366 .141

NumRemedial -.004 1.000 .054 -.315

Num118Attempts .366 .054 1.000 -.341

NumCredits .141 -.315 -.341 1.000

GPACUMFall2011 .347 .021 .217 .242

Sig. (1-tailed) CourseGrade . .491 .011 .197

NumRemedial .491 . .371 .025

Num118Attempts .011 .371 . .017

NumCredits .197 .025 .017 .

GPACUMFall2011 .015 .450 .092 .069

N CourseGrade 39 39 39 39

NumRemedial 39 39 39 39

Num118Attempts 39 39 39 39

NumCredits 39 39 39 39

GPACUMFall2011 39 39 39 39

238

Correlations

GPACUMFall

2011

Pearson Correlation CourseGrade .347

NumRemedial .021

Num118Attempts .217

NumCredits .242

GPACUMFall2011 1.000

Sig. (1-tailed) CourseGrade .015

NumRemedial .450

Num118Attempts .092

NumCredits .069

GPACUMFall2011 .

N CourseGrade 39

NumRemedial 39

Num118Attempts 39

NumCredits 39

GPACUMFall2011 39

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 GPACUMFall

2011,

NumRemedial

,

Num118Atte

mpts,

NumCreditsb

. Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .501a .251 .163 21.397 .251 2.855 4

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 34 .038

a. Predictors: (Constant), GPACUMFall2011, NumRemedial, Num118Attempts, NumCredits

239

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 5227.752 4 1306.938 2.855 .038b

Residual 15565.838 34 457.819

Total 20793.590 38

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), GPACUMFall2011, NumRemedial, Num118Attempts, NumCredits

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 15.312 18.500

.048

.828 .414

NumRemedial .535 1.780 .301 .765

Num118Attempts 13.864 5.759 .404 2.407 .022

NumCredits 1.735 1.264 .246 1.373 .179

GPACUMFall2011 6.817 5.601 .199 1.217 .232

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 (Constant)

.881

1.134 NumRemedial

Num118Attempts .780 1.282

NumCredits .688 1.453

GPACUMFall2011 .825 1.212

a. Dependent Variable: CourseGrade

Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition

Index

Variance Proportions

(Constant)

NumRemedial

Num118Atte

mpts

1 1 3.816 1.000 .00 .02 .02

2 .639 2.445 .00 .02 .72

3 .486 2.802 .00 .77 .00

4 .035 10.398 .04 .14 .26

5 .024 12.719 .95 .06 .00

240

Collinearity Diagnosticsa

Model

Dimension

Variance Proportions

NumCredits

GPACUMFall

2011

1 1 .00 .00

2 .01 .00

3 .01 .00

4 .84 .49

5 .14 .51

a. Dependent Variable: CourseGrade

Table 51: Regression Model 1 separated by Tutorial Enrollment [DataSet1] C:\Users\Owner\Documents\My Documents\Classes\MATH\Proseminar Stuf

f\Grades, Attempts, GPA and Test Scores MERGED.sav

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade 63.97 21.573 32

NumRemedial 1.81 1.533 32

Num118Attempts .53 .718 32

NumCredits 10.78 3.087 32

GPACUMFall2011 2.943406 .6153698 32

a. CoReq = No

241

Correlationsa

CourseGrade

NumRemedial

Num118Atte

mpts

NumCredits

Pearson Correlation CourseGrade 1.000 .032 .347 -.055

NumRemedial .032 1.000 .181 -.152

Num118Attempts .347 .181 1.000 -.441

NumCredits -.055 -.152 -.441 1.000

GPACUMFall2011 .294 -.037 .216 .261

Sig. (1-tailed) CourseGrade . .431 .026 .383

NumRemedial .431 . .160 .203

Num118Attempts .026 .160 . .006

NumCredits .383 .203 .006 .

GPACUMFall2011 .051 .421 .118 .074

N CourseGrade 32 32 32 32

NumRemedial 32 32 32 32

Num118Attempts 32 32 32 32

NumCredits 32 32 32 32

GPACUMFall2011 32 32 32 32

Correlationsa

GPACUMFall

2011

Pearson Correlation CourseGrade .294

NumRemedial -.037

Num118Attempts .216

NumCredits .261

GPACUMFall2011 1.000

Sig. (1-tailed) CourseGrade .051

NumRemedial .421

Num118Attempts .118

NumCredits .074

GPACUMFall2011 .

N CourseGrade 32

NumRemedial 32

Num118Attempts 32

NumCredits 32

GPACUMFall2011 32

a. CoReq = No

242

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 GPACUMFall

2011,

NumRemedial

,

Num118Atte

mpts,

NumCreditsc

. Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .414b .171 .049 21.041 .171 1.396 4

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 27 .262

a. CoReq = No

b. Predictors: (Constant), GPACUMFall2011, NumRemedial, Num118Attempts, NumCredits

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 2473.041 4 618.260 1.396 .262c

Residual 11953.928 27 442.738

Total 14426.969 31

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), GPACUMFall2011, NumRemedial, Num118Attempts, NumCredits

243

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 34.828 21.693

-.013

1.605 .120

NumRemedial -.183 2.518 -.073 .943

Num118Attempts 9.378 6.408 .312 1.463 .155

NumCredits .162 1.495 .023 .108 .915

GPACUMFall2011 7.728 6.892 .220 1.121 .272

Coefficientsa,b

Model

Collinearity Statistics

Tolerance VIF

1 (Constant)

.958

1.044 NumRemedial

Num118Attempts .675 1.481

NumCredits .671 1.491

GPACUMFall2011 .794 1.259

a. CoReq = No

b. Dependent Variable: CourseGrade

Collinearity Diagnosticsa,b

Model

Dimension

Eigenvalue

Condition

Index

Variance Proportions

(Constant)

NumRemedial

Num118Atte

mpts

1 1 4.002 1.000 .00 .02 .01

2 .603 2.577 .00 .00 .58

3 .346 3.402 .00 .93 .06

4 .030 11.573 .13 .01 .35

5 .019 14.410 .86 .04 .01

Collinearity Diagnosticsa,b

Model

Dimension

Variance Proportions

NumCredits

GPACUMFall

2011

1 1 .00 .00

2 .01 .00

3 .01 .01

4 .96 .28

5 .02 .71

244

a. CoReq = No

b. Dependent Variable: CourseGrade

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade 49.86 29.504 7

NumRemedial 2.57 3.823 7

Num118Attempts .14 .378 7

NumCredits 10.86 4.488 7

GPACUMFall2011 2.850286 .9914170 7

a. CoReq = Yes

Correlationsa

CourseGrade

NumRemedial

Num118Atte

mpts

NumCredits

Pearson Correlation CourseGrade 1.000 .032 .361 .671

NumRemedial .032 1.000 -.181 -.587

Num118Attempts .361 -.181 1.000 .112

NumCredits .671 -.587 .112 1.000

GPACUMFall2011 .466 .114 .301 .203

Sig. (1-tailed) CourseGrade . .473 .213 .050

NumRemedial .473 . .349 .083

Num118Attempts .213 .349 . .405

NumCredits .050 .083 .405 .

GPACUMFall2011 .146 .404 .256 .331

N CourseGrade 7 7 7 7

NumRemedial 7 7 7 7

Num118Attempts 7 7 7 7

NumCredits 7 7 7 7

GPACUMFall2011 7 7 7 7

245

Correlationsa

GPACUMFall

2011

Pearson Correlation CourseGrade .466

NumRemedial .114

Num118Attempts .301

NumCredits .203

GPACUMFall2011 1.000

Sig. (1-tailed) CourseGrade .146

NumRemedial .404

Num118Attempts .256

NumCredits .331

GPACUMFall2011 .

N CourseGrade 7

NumRemedial 7

Num118Attempts 7

NumCredits 7

GPACUMFall2011 7

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 GPACUMFall

2011,

NumRemedial

,

Num118Atte

mpts,

NumCreditsc

. Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .930b .865 .595 18.784 .865 3.201 4

246

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 2 .252

a. CoReq = Yes

b. Predictors: (Constant), GPACUMFall2011, NumRemedial, Num118Attempts, NumCredits

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 4517.189 4 1129.297 3.201 .252c

Residual 705.668 2 352.834

Total 5222.857 6

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), GPACUMFall2011, NumRemedial, Num118Attempts, NumCredits

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) -46.671 31.125

.683

-1.499 .273

NumRemedial 5.269 2.675 1.969 .188

Num118Attempts 27.105 21.992 .347 1.232 .343

NumCredits 6.684 2.255 1.017 2.964 .097

GPACUMFall2011 2.294 8.810 .077 .260 .819

Coefficientsa,b

Model

Collinearity Statistics

Tolerance VIF

1 (Constant)

.562

1.779 NumRemedial

Num118Attempts .851 1.175

NumCredits .574 1.742

GPACUMFall2011 .771 1.297

a. CoReq = Yes

b. Dependent Variable: CourseGrade

247

Collinearity Diagnosticsa,b

Model

Dimension

Eigenvalue

Condition

Index

Variance Proportions

(Constant)

NumRemedial

Num118Atte

mpts

1 1 3.421 1.000 .00 .01 .02

2 .928 1.920 .00 .14 .49

3 .563 2.464 .00 .30 .37

4 .053 8.056 .04 .24 .12

5 .035 9.899 .95 .31 .00

Collinearity Diagnosticsa,b

Model

Dimension

Variance Proportions

NumCredits

GPACUMFall

2011

1 1 .01 .01

2 .00 .00

3 .03 .00

4 .32 .94

5 .65 .05

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 52: Regression Model 2

Descriptive Statistics

Mean Std. Deviation N

CourseGrade 60.67 23.918 46

Test1 61.5217 22.74471 46

Final 56.3696 29.66956 46

248

Correlations

CourseGrade Test1 Final

Pearson Correlation CourseGrade 1.000 .740 .966

Test1 .740 1.000 .632

Final .966 .632 1.000

Sig. (1-tailed) CourseGrade . .000 .000

Test1 .000 . .000

Final .000 .000 .

N CourseGrade 46 46 46

Test1 46 46 46

Final 46 46 46

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 Final, Test1b . Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .980a .961 .959 4.817 .961 533.114 2

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 43 .000

a. Predictors: (Constant), Final, Test1

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 24744.197 2 12372.099 533.114 .000b

Residual 997.911 43 23.207

Total 25742.109 45

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), Final, Test1

249

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 9.016 2.076

.216

4.343 .000

Test1 .227 .041 5.567 .000

Final .669 .031 .830 21.405 .000

a. Dependent Variable: CourseGrade

Table 53: Regression Model 2 separated by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade 64.31 21.330 36

Test1 64.7500 20.43579 36

Final 60.8333 26.66994 36

a. CoReq = No

Correlationsa

CourseGrade Test1 Final

Pearson Correlation CourseGrade 1.000 .731 .962

Test1 .731 1.000 .643

Final .962 .643 1.000

Sig. (1-tailed) CourseGrade . .000 .000

Test1 .000 . .000

Final .000 .000 .

N CourseGrade 36 36 36

Test1 36 36 36

Final 36 36 36

a. CoReq = No

250

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Final, Test1c . Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .973b .946 .943 5.090 .946 290.762 2

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 33 .000

a. CoReq = No

b. Predictors: (Constant), Final, Test1

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 15068.539 2 7534.269 290.762 .000c

Residual 855.100 33 25.912

Total 15923.639 35

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Final, Test1

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 10.539 2.868

.192

3.674 .001

Test1 .201 .055 3.653 .001

Final .670 .042 .838 15.913 .000

a. CoReq = No

b. Dependent Variable: CourseGrade

251

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade 47.60 29.125 10

Test1 49.9000 27.76268 10

Final 40.3000 35.61850 10

a. CoReq = Yes

Correlationsa

CourseGrade Test1 Final

Pearson Correlation CourseGrade 1.000 .692 .966

Test1 .692 1.000 .512

Final .966 .512 1.000

Sig. (1-tailed) CourseGrade . .013 .000

Test1 .013 . .065

Final .000 .065 .

N CourseGrade 10 10 10

Test1 10 10 10

Final 10 10 10

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Final, Test1c . Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .993b .986 .982 3.914 .986 245.621 2

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 7 .000

252

a. CoReq = Yes

b. Predictors: (Constant), Final, Test1

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 7527.141 2 3763.571 245.621 .000c

Residual 107.259 7 15.323

Total 7634.400 9

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Final, Test1

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 6.295 2.671

.267

2.357 .051

Test1 .280 .055 5.117 .001

Final .678 .043 .829 15.905 .000

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 54: Regression Model 3

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

Test1

60.67

61.5217

23.918

22.74471

46

46

253

Correlations

CourseGrade Test1

Pearson Correlation CourseGrade 1.000 .740

Test1 .740 1.000

Sig. (1-tailed) CourseGrade . .000

Test1 .000 .

N CourseGrade 46 46

Test1 46 46

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 Test1b . Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .740a .548 .538 16.258 .548 53.386 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 44 .000

a. Predictors: (Constant), Test1

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 14111.541 1 14111.541 53.386 .000b

Residual 11630.567 44 264.331

Total 25742.109 45

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), Test1

254

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test1

12.775

.779

6.980

.107

.740

1.830

7.307

.074

.000

a. Dependent Variable: CourseGrade

Table 55: Regression Model 3 separated by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test1

64.31

64.7500

21.330

20.43579

36

36

a. CoReq = No

Correlationsa

CourseGrade Test1

Pearson Correlation CourseGrade 1.000 .731

Test1 .731 1.000

Sig. (1-tailed) CourseGrade . .000

Test1 .000 .

N CourseGrade 36 36

Test1 36 36

a. CoReq = No

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test1c . Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

255

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .731b .534 .521 14.770 .534 38.995 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 34 .000

a. CoReq = No

b. Predictors: (Constant), Test1

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 8506.626 1 8506.626 38.995 .000c

Residual 7417.013 34 218.147

Total 15923.639 35

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test1

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test1

14.909

.763

8.284

.122

.731

1.800

6.245

.081

.000

a. CoReq = No

b. Dependent Variable: CourseGrade

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test1

47.60

49.9000

29.125

27.76268

10

10

a. CoReq = Yes

256

Correlationsa

CourseGrade Test1

Pearson Correlation CourseGrade 1.000 .692

Test1 .692 1.000

Sig. (1-tailed) CourseGrade . .013

Test1 .013 .

N CourseGrade 10 10

Test1 10 10

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test1c . Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .692b .478 .413 22.314 .478 7.333 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 8 .027

a. CoReq = Yes

b. Predictors: (Constant), Test1

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 3651.064 1 3651.064 7.333 .027c

Residual 3983.336 8 497.917

Total 7634.400 9

a. CoReq = Yes

b. Dependent Variable: CourseGrade

257

c. Predictors: (Constant), Test1

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test1

11.398

.725

15.117

.268

.692

.754

2.708

.472

.027

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 56: Regression Model 4

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

Test2

69.76

54.1579

13.536

18.70327

38

38

Correlations

CourseGrade Test2

Pearson Correlation CourseGrade 1.000 .501

Test2 .501 1.000

Sig. (1-tailed) CourseGrade . .001

Test2 .001 .

N CourseGrade 38 38

Test2 38 38

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 Test2b . Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

258

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .501a .251 .230 11.875 .251 12.075 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 36 .001

a. Predictors: (Constant), Test2

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 1702.658 1 1702.658 12.075 .001b

Residual 5076.211 36 141.006

Total 6778.868 37

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), Test2

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test2

50.120

.363

5.972

.104

.501

8.393

3.475

.000

.001

a. Dependent Variable: CourseGrade

Table 57: Regression Model 4 Separated by Tutorial Enrollment

CoReq = No

259

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test2

70.03

56.3438

13.909

17.38113

32

32

a. CoReq = No

Correlationsa

CourseGrade Test2

Pearson Correlation CourseGrade 1.000 .589

Test2 .589 1.000

Sig. (1-tailed) CourseGrade . .000

Test2 .000 .

N CourseGrade 32 32

Test2 32 32

a. CoReq = No

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test2c . Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .589b .347 .326 11.421 .347 15.976 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 30 .000

a. CoReq = No

b. Predictors: (Constant), Test2

260

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 2083.847 1 2083.847 15.976 .000c

Residual 3913.121 30 130.437

Total 5996.969 31

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test2

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test2

43.453

.472

6.949

.118

.589

6.253

3.997

.000

.000

a. CoReq = No

b. Dependent Variable: CourseGrade

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test2

68.33

42.5000

12.388

22.84513

6

6

a. CoReq = Yes

Correlationsa

CourseGrade Test2

Pearson Correlation CourseGrade 1.000 .112

Test2 .112 1.000

Sig. (1-tailed) CourseGrade . .417

Test2 .417 .

N CourseGrade 6 6

Test2 6 6

a. CoReq = Yes

261

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test2c . Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .112b .012 -.234 13.764 .012 .050 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 4 .833

a. CoReq = Yes

b. Predictors: (Constant), Test2

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 9.567 1 9.567 .050 .833c

Residual 757.767 4 189.442

Total 767.333 5

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test2

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test2

65.760

.061

12.755

.269

.112

5.155

.225

.007

.833

a. CoReq = Yes

b. Dependent Variable: CourseGrade

262

Table 58: Regression Model 5

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

Test3

69.76

69.6316

13.536

18.12482

38

38

Correlations

CourseGrade Test3

Pearson Correlation CourseGrade 1.000 .803

Test3 .803 1.000

Sig. (1-tailed) CourseGrade . .000

Test3 .000 .

N CourseGrade 38 38

Test3 38 38

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 Test3b . Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .803a .644 .634 8.187 .644 65.136 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 36 .000

a. Predictors: (Constant), Test3

263

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 4365.883 1 4365.883 65.136 .000b

Residual 2412.985 36 67.027

Total 6778.868 37

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), Test3

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test3

28.031

.599

5.339

.074

.803

5.251

8.071

.000

.000

a. Dependent Variable: CourseGrade

Table 59: Regression Model 5 separated by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test3

70.03

69.6563

13.909

19.23641

32

32

a. CoReq = No

Correlationsa

CourseGrade Test3

Pearson Correlation CourseGrade 1.000 .815

Test3 .815 1.000

Sig. (1-tailed) CourseGrade . .000

Test3 .000 .

N CourseGrade 32 32

Test3 32 32

a. CoReq = No

264

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test3c . Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .815b .664 .653 8.198 .664 59.222 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 30 .000

a. CoReq = No

b. Predictors: (Constant), Test3

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 3980.544 1 3980.544 59.222 .000c

Residual 2016.424 30 67.214

Total 5996.969 31

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test3

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test3

28.999

.589

5.525

.077

.815

5.248

7.696

.000

.000

a. CoReq = No

b. Dependent Variable: CourseGrade

265

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test3

68.33

69.5000

12.388

11.69188

6

6

a. CoReq = Yes

Correlationsa

CourseGrade Test3

Pearson Correlation CourseGrade 1.000 .726

Test3 .726 1.000

Sig. (1-tailed) CourseGrade . .051

Test3 .051 .

N CourseGrade 6 6

Test3 6 6

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test3c . Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .726b .528 .409 9.520 .528 4.466 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 4 .102

a. CoReq = Yes

b. Predictors: (Constant), Test3

266

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 404.793 1 404.793 4.466 .102c

Residual 362.540 4 90.635

Total 767.333 5

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test3

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test3

14.848

.770

25.605

.364

.726

.580

2.113

.593

.102

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 60: Regression Model 6

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

Test4

69.76

60.4211

13.536

20.93959

38

38

Correlations

CourseGrade Test4

Pearson Correlation CourseGrade 1.000 .840

Test4 .840 1.000

Sig. (1-tailed) CourseGrade . .000

Test4 .000 .

N CourseGrade 38 38

Test4 38 38

267

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 Test4b . Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .840a .705 .697 7.452 .705 86.069 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 36 .000

a. Predictors: (Constant), Test4

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 4779.675 1 4779.675 86.069 .000b

Residual 1999.193 36 55.533

Total 6778.868 37

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), Test4

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test4

36.967

.543

3.736

.059

.840

9.895

9.277

.000

.000

a. Dependent Variable: CourseGrade

Table 61: Regression Model 6 separated by Tutorial Enrollment

268

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test4

70.03

60.0625

13.909

20.91756

32

32

a. CoReq = No

Correlationsa

CourseGrade Test4

Pearson Correlation CourseGrade 1.000 .837

Test4 .837 1.000

Sig. (1-tailed) CourseGrade . .000

Test4 .000 .

N CourseGrade 32 32

Test4 32 32

a. CoReq = No

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test4c . Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .837b .701 .691 7.734 .701 70.255 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 30 .000

269

a. CoReq = No

b. Predictors: (Constant), Test4

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 4202.454 1 4202.454 70.255 .000c

Residual 1794.515 30 59.817

Total 5996.969 31

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test4

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test4

36.599

.557

4.216

.066

.837

8.680

8.382

.000

.000

a. CoReq = No

b. Dependent Variable: CourseGrade

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Test4

68.33

62.3333

12.388

22.94922

6

6

a. CoReq = Yes

Correlationsa

CourseGrade Test4

Pearson Correlation CourseGrade 1.000 .897

Test4 .897 1.000

Sig. (1-tailed) CourseGrade . .008

Test4 .008 .

N CourseGrade 6 6

Test4 6 6

a. CoReq = Yes

270

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Test4c . Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .897b .805 .756 6.117 .805 16.505 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 4 .015

a. CoReq = Yes

b. Predictors: (Constant), Test4

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 617.649 1 617.649 16.505 .015c

Residual 149.685 4 37.421

Total 767.333 5

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Test4

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Test4

38.145

.484

7.839

.119

.897

4.866

4.063

.008

.015

a. CoReq = Yes

b. Dependent Variable: CourseGrade

271

Table 62: Regression Model 7

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

Final

60.67

56.3696

23.918

29.66956

46

46

Correlations

CourseGrade Final

Pearson Correlation CourseGrade 1.000 .966

Final .966 1.000

Sig. (1-tailed) CourseGrade . .000

Final .000 .

N CourseGrade 46 46

Final 46 46

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 Finalb . Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .966a .933 .932 6.247 .933 615.616 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 44 .000

a. Predictors: (Constant), Final

272

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 24024.970 1 24024.970 615.616 .000b

Residual 1717.139 44 39.026

Total 25742.109 45

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), Final

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Final

16.774

.779

1.995

.031

.966

8.410

24.812

.000

.000

a. Dependent Variable: CourseGrade

Table 63: Regression Model 7 separated by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Final

64.31

60.8333

21.330

26.66994

36

36

a. CoReq = No

Correlationsa

CourseGrade Final

Pearson Correlation CourseGrade 1.000 .962

Final .962 1.000

Sig. (1-tailed) CourseGrade . .000

Final .000 .

N CourseGrade 36 36

Final 36 36

a. CoReq = No

273

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Finalc . Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .962b .925 .922 5.943 .925 416.863 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 34 .000

a. CoReq = No

b. Predictors: (Constant), Final

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 14722.822 1 14722.822 416.863 .000c

Residual 1200.817 34 35.318

Total 15923.639 35

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Final

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Final

17.523

.769

2.496

.038

.962

7.020

20.417

.000

.000

a. CoReq = No

b. Dependent Variable: CourseGrade

274

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

Final

47.60

40.3000

29.125

35.61850

10

10

a. CoReq = Yes

Correlationsa

CourseGrade Final

Pearson Correlation CourseGrade 1.000 .966

Final .966 1.000

Sig. (1-tailed) CourseGrade . .000

Final .000 .

N CourseGrade 10 10

Final 10 10

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 Finalc . Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .966b .933 .925 7.973 .933 112.104 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 8 .000

a. CoReq = Yes

b. Predictors: (Constant), Final

275

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 7125.880 1 7125.880 112.104 .000c

Residual 508.520 8 63.565

Total 7634.400 9

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), Final

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

Final

15.763

.790

3.924

.075

.966

4.017

10.588

.004

.000

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 64: Regression Model 8

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

COMPASSAlg

61.32

25.97

23.519

9.934

38

38

Correlations

CourseGrade COMPASSAlg

Pearson Correlation CourseGrade 1.000 .047

COMPASSAlg .047 1.000

Sig. (1-tailed) CourseGrade . .389

COMPASSAlg .389 .

N CourseGrade 38 38

COMPASSAlg 38 38

276

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 COMPASSAlgb

. Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .047a .002 -.025 23.816 .002 .081 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 36 .777

a. Predictors: (Constant), COMPASSAlg

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 46.113 1 46.113 .081 .777b

Residual 20420.097 36 567.225

Total 20466.211 37

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), COMPASSAlg

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

COMPASSAlg

58.397

.112

10.943

.394

.047

5.337

.285

.000

.777

a. Dependent Variable: CourseGrade

Table 65: Regression Model 9

277

Descriptive Statistics

Mean Std. Deviation N

CourseGrade

PercentPresent

60.67

80.2099

23.918

20.70797

46

46

Correlations

CourseGrade

PercentPrese

nt

Pearson Correlation CourseGrade 1.000 .079

PercentPresent .079 1.000

Sig. (1-tailed) CourseGrade . .301

PercentPresent .301 .

N CourseGrade 46 46

PercentPresent 46 46

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 PercentPrese

ntb

. Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .079a .006 -.016 24.112 .006 .277 1

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 44 .602

a. Predictors: (Constant), PercentPresent

278

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 160.846 1 160.846 .277 .602b

Residual 25581.263 44 581.392

Total 25742.109 45

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), PercentPresent

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

PercentPresent

53.351

.091

14.369

.174

.079

3.713

.526

.001

.602

a. Dependent Variable: CourseGrade

Table 66: Regression Model 9 Separated by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

PercentPresent

70.03

80.0647

13.909

17.59884

32

32

a. CoReq = No

Correlationsa

CourseGrade

PercentPrese

nt

Pearson Correlation CourseGrade 1.000 -.310

PercentPresent -.310 1.000

Sig. (1-tailed) CourseGrade . .042

PercentPresent .042 .

N CourseGrade 32 32

PercentPresent 32 32

a. CoReq = No

279

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 PercentPrese

ntc

. Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .310b .096 .066 13.442 .096 3.190 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 30 .084

a. CoReq = No

b. Predictors: (Constant), PercentPresent

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 576.397 1 576.397 3.190 .084c

Residual 5420.572 30 180.686

Total 5996.969 31

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), PercentPresent

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

PercentPresent

89.648

-.245

11.238

.137

-.310

7.978

-1.786

.000

.084

a. CoReq = No

b. Dependent Variable: CourseGrade

280

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade

PercentPresent

68.33

91.3793

12.388

8.08692

6

6

a. CoReq = Yes

Correlationsa

CourseGrade

PercentPrese

nt

Pearson Correlation CourseGrade 1.000 .991

PercentPresent .991 1.000

Sig. (1-tailed) CourseGrade . .000

PercentPresent .000 .

N CourseGrade 6 6

PercentPresent 6 6

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 PercentPrese

ntc

. Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .991b .983 .978 1.823 .983 226.830 1

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 4 .000

a. CoReq = Yes

b. Predictors: (Constant), PercentPresent

281

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 754.036 1 754.036 226.830 .000c

Residual 13.297 4 3.324

Total 767.333 5

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), PercentPresent

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant)

PercentPresent

-70.430

1.519

9.244

.101

.991

-7.619

15.061

.002

.000

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 67: Regression Model 10

Descriptive Statistics

Mean Std. Deviation N

CourseGrade 69.76 12.696 33

Num118Attempts .55 .711 33

GPACUMFall2011 3.044909 .5805924 33

282

Correlations

CourseGrade

Num118Atte

mpts

GPACUMFall

2011

Pearson Correlation CourseGrade 1.000 .250 .170

Num118Attempts .250 1.000 .129

GPACUMFall2011 .170 .129 1.000

Sig. (1-tailed) CourseGrade . .080 .173

Num118Attempts .080 . .237

GPACUMFall2011 .173 .237 .

N CourseGrade 33 33 33

Num118Attempts 33 33 33

GPACUMFall2011 33 33 33

Variables Entered/Removeda

Model

Variables

Entered

Variables

Removed

Method

1 GPACUMFall

2011,

Num118Atte

mptsb

. Enter

a. Dependent Variable: CourseGrade

b. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .286a .082 .021 12.564 .082 1.338 2

Model Summary

Model

Change Statistics

df2

Sig. F Change

1 30 .278

a. Predictors: (Constant), GPACUMFall2011, Num118Attempts

283

ANOVAa

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 422.374 2 211.187 1.338 .278b

Residual 4735.687 30 157.856

Total 5158.061 32

a. Dependent Variable: CourseGrade

b. Predictors: (Constant), GPACUMFall2011, Num118Attempts

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 58.202 11.853

.232

4.910 .000

Num118Attempts 4.150 3.150 1.318 .198

GPACUMFall2011 3.052 3.858 .140 .791 .435

a. Dependent Variable: CourseGrade

Table 68: Regression Model 10 separated by Tutorial Enrollment

CoReq = No

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade 70.46 12.897 28

Num118Attempts .61 .737 28

GPACUMFall2011 3.015679 .5709125 28

a. CoReq = No

284

Correlationsa

CourseGrade

Num118Atte

mpts

GPACUMFall

2011

Pearson Correlation CourseGrade 1.000 .219 .216

Num118Attempts .219 1.000 .152

GPACUMFall2011 .216 .152 1.000

Sig. (1-tailed) CourseGrade . .132 .135

Num118Attempts .132 . .221

GPACUMFall2011 .135 .221 .

N CourseGrade 28 28 28

Num118Attempts 28 28 28

GPACUMFall2011 28 28 28

a. CoReq = No

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 GPACUMFall

2011,

Num118Atte

mptsc

. Enter

a. CoReq = No

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .286b .082 .009 12.841 .082 1.117 2

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 25 .343

a. CoReq = No

b. Predictors: (Constant), GPACUMFall2011, Num118Attempts

285

ANOVAa,b

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 368.364 2 184.182 1.117 .343c

Residual 4122.601 25 164.904

Total 4490.964 27

a. CoReq = No

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), GPACUMFall2011, Num118Attempts

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 55.686 13.278

.190

4.194 .000

Num118Attempts 3.326 3.391 .981 .336

GPACUMFall2011 4.231 4.379 .187 .966 .343

a. CoReq = No

b. Dependent Variable: CourseGrade

CoReq = Yes

Descriptive Statisticsa

Mean Std. Deviation N

CourseGrade 65.80 11.987 5

Num118Attempts .20 .447 5

GPACUMFall2011 3.208600 .6761093 5

a. CoReq = Yes

286

Correlations

CourseGrade

Num118Atte

mpts

GPACUMFall

2011

Pearson Correlation CourseGrade 1.000 .382 .026

Num118Attempts .382 1.000 .262

GPACUMFall2011 .026 .262 1.000

Sig. (1-tailed) CourseGrade . .263 .483

Num118Attempts .263 . .335

GPACUMFall2011 .483 .335 .

N CourseGrade 5 5 5

Num118Attempts 5 5 5

GPACUMFall2011 5 5 5

a. CoReq = Yes

Variables Entered/Removeda,b

Model

Variables

Entered

Variables

Removed

Method

1 GPACUMFall

2011,

Num118Atte

mptsc

. Enter

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. All requested variables entered.

Model Summarya

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F Change

df1

1 .390b .152 -.696 15.611 .152 .179 2

Model Summarya

Model

Change Statistics

df2

Sig. F Change

1 2 .848

a. CoReq = Yes

b. Predictors: (Constant), GPACUMFall2011, Num118Attempts

287

Model

Sum of

Squares

df

Mean Square

F

Sig.

1 Regression 87.424 2 43.712 .179 .848c

Residual 487.376 2 243.688

Total 574.800 4

a. CoReq = Yes

b. Dependent Variable: CourseGrade

c. Predictors: (Constant), GPACUMFall2011, Num118Attempts

Coefficientsa,b

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig. B Std. Error Beta

1 (Constant) 68.155 38.242

.403

1.782 .217

Num118Attempts 10.809 18.087 .598 .611

GPACUMFall2011 -1.408 11.964 -.079 -.118 .917

a. CoReq = Yes

b. Dependent Variable: CourseGrade

Table 69: Pass Rates by Tutorial Enrollment zero scores excluded [DataSet1] C:\Users\Owner\Documents\My Documents\Classes\MATH\Proseminar Stuf

f\Grades, Attempts, GPA and Test Scores MERGED.sav

CoReq = No

CourseGradeLettera

Frequency

Percent

Valid Percent

Cumulative

Percent

Valid F 7 21.9 21.9 21.9

D 7 21.9 21.9 43.8

C 7 21.9 21.9 65.6

B 9 28.1 28.1 93.8

A 2 6.3 6.3 100.0

Total 32 100.0 100.0

a. CoReq = No

CoReq = Yes

Statisticsa

288

CourseGradeLetter

N Valid 6

Missing 0

a. CoReq = Yes

CourseGradeLettera

Frequency

Percent

Valid Percent

Cumulative

Percent

Valid F 1 16.7 16.7 16.7

D 2 33.3 33.3 50.0

C 1 16.7 16.7 66.7

B 2 33.3 33.3 100.0

Total 6 100.0 100.0

a. CoReq = Yes

Table 70: Pass Rates by Tutorial Enrollment zero scores included

CoReq = No

289

CourseGradeLettera

Frequency

Percent

Valid Percent

Cumulative

Percent

Valid F 11 30.6 30.6 30.6

D 7 19.4 19.4 50.0

C 7 19.4 19.4 69.4

B 9 25.0 25.0 94.4

A 2 5.6 5.6 100.0

Total 36 100.0 100.0

a. CoReq = No

CoReq = Yes

Statisticsa

CourseGradeLetter

N Valid 10

Missing 0

a. CoReq = Yes

CourseGradeLettera

Frequency

Percent

Valid Percent

Cumulative

Percent

Valid F 5 50.0 50.0 50.0

D 2 20.0 20.0 70.0

C 1 10.0 10.0 80.0

B 2 20.0 20.0 100.0

Total 10 100.0 100.0

a. CoReq = Yes

Table 71: Overall Pass Rates zero scores included

290

CourseGradeLetter

Frequency

Percent

Valid Percent

Cumulative

Percent

Valid F 16 34.8 34.8 34.8

D 9 19.6 19.6 54.3

C 8 17.4 17.4 71.7

B 11 23.9 23.9 95.7

A 2 4.3 4.3 100.0

Total 46 100.0 100.0

Table 72: Pass Rates zero scores excluded

Statistics

CourseGradeLetter

N Valid 38

Missing 0

CourseGradeLetter

Frequency

Percent

Valid Percent

Cumulative

Percent

Valid F 8 21.1 21.1 21.1

D 9 23.7 23.7 44.7

C 8 21.1 21.1 65.8

B 11 28.9 28.9 94.7

A 2 5.3 5.3 100.0

Total 38 100.0 100.0