University of Southern California Dissertations and Theses

Model based view-invariant human action recognition and segmentation

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Model based view-invariant human action recognition and segmentation

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Content THE EFFECT OF STUDY GROUP PARTICIPATION AND COURSE
LOAD ON THE COURSE COMPLETION RATES, SUCCESS RATES AND
GRADE POINT AVERAGES OF FIRST GENERATION COLLEGE
STUDENTS AT THE COMMUNITY COLLEGE

by

Gregory V. Schulz

A Dissertation Presented to the
FACULTY OF THE ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

March 2007

Copyright 2007 Gregory V. Schulz

ii

TABLE OF CONTENTS

List of Tables iv
Abstract vi

Chapter One:
Introduction 1
Purpose of the Study 1
Research Questions 3
Limitations 4
Delimitations 5
Definition of Terms 5
Methodology 7

Chapter Two:
Review of the Literature 9
Forms of Peer Group and Study Group Behavior 9
Functional Theories – Integration, Involvement 11
& Attrition and Study Group Participation
Course Load 20
First Generation College Students 28

Chapter Three:
Methods Section 36
Sample 36
Measures 39
Independent Variables 39
Dependant Variable 41
Procedures 43

Chapter Four:
Findings 44
Peer Group Participation 44
Course Load 56

Chapter Five:
Summary of Study 62
Implications for Practice 69
Implications for Further Research 70
Recommendations 71

iii

References 73
Appendix A – Additional Tables 81
Appendix B – TRUCCS Questions 83

iv

LIST OF TABLES

Table 1: Course Completion Rates (CCR) and 45
Success Rates (SR) of First Generation
College Students by Frequency of Helping
Another Student Understand Homework
within the past 7 days

Table 2: Grade Point Averages (GPA) of First 46
Generation College Students by Frequency
of Helping Another Student Understand
Homework within the past 7 days

Table 3: Course Completion Rates (CCR) and 48
Success Rates (SR) of First Generation
College Students by Frequency of Studying
Alone at Home within the past 7 days

Table 4: Grade Point Averages (GPA) of First 49
Generation College Students by
Frequency of Studying Alone at Home
within the past 7 days

Table 5: Grade Point Averages (GPA) of First 50
Generation College Students by Frequency
of Studying With Students from Other
Courses within the past 7 days

Table 6: Pearson Correlations between Course 53
Completion Rates and Studying Together

Table 7: Pearson Correlations between Success 54
Rates and Studying Together

Table 8: Pearson Correlations between Grade Point 55
Average(GPA) and Studying Together

Table 9: Course Completion Rates of First 57
Generation College Students by Course
Load (by units attempted)

Table 10: Success Rates of First Generation 58
College Students by Course Load
(by units attempted)
v

Table 11: Grade Point Average of First 59
Generation College Students by
Course Load (by units attempted)

Table 12: Pearson Correlations between Course 60
Completion Rates and Course Load

Table 13: Pearson Correlations between 61
Success Rates and Course Load

Table 14: Pearson Correlations between Grade 61
Point Average (GPA) and Course Load

Table 15: Course Completion Rate of First 81
Generation College Students by
Ethnicity

Table 16: Course Completion Rate of First 82
Generation College Students by Gender

Table 17: Course Completion Rate of First 82
Generation College Students – First
Time students

vi

Abstract

The purpose of this study was to understand some
of the conditions likely to enhance the student
success of first generation college students at the
community college. Specifically, I was interested in
the effect that course load and student study group
participation have on the course completion rates,
success rates and grade point averages of first
generation college students. Studies have shown that
student success rates, course completion rates and
grade point averages are positively impacted by study
group participation, and certain course-taking
patterns. I was interested in how these variables
affect the student success of first generation
community college students. I performed statistical
analysis including several descriptive statistics and
frequencies, Pearson correlations, and comparison of
means tests on a sample of first generation college
students from the Los Angeles Community College
District (LACCD) to investigate the relationship
between the dependant variables of course completion
rates, success rates and grade point averages and the
vii

independent variables of study group participation and
course load. This study found a small correlation
between course load and course completion rates, a
small correlation between course load and success
rates, a small correlation between course load and
grade point average, and weak correlations among
various study group participation variables and course
completion rates, success rates and grade point
averages for first generation college students.

1
CHAPTER 1
Introduction
In California today, an increasingly diverse
population of potential college students presents new
challenges and opportunities for community colleges
which are aiming to serve the academic needs of all
students effectively. In particular, immigration
trends have contributed to an ever increasing number
of first generation college students, defined as
students at the college whose parents educational
attainment did not exceed a high school education. In
order to create and maintain a strong local economy
and trained workforce, community colleges shoulder a
critical responsibility - preparing and educating
adults for the future.
Purpose of the Study
This study seeks to examine two topics, study
groups and course load, which have potentially
practical and theoretical implications for increasing
the course completion rates, success rates and grade
point averages of students in community colleges as

2
well as basic understandings about community college
students.
In this study, course completion rate was defined
by calculating or dividing the number of courses
completed by the number of courses attempted for each
semester, over a period of nine semesters. Completed
courses were defined as a grade of A, B, C, D or F, or
in cases of classes graded with a pass/no pass scale,
P or N (Hagedorn, 2006). A completion rate was then
calculated by was measured on a per semester basis by
examining the student’s transcripts and determining
whether they completed the course.
Success rate was defined by calculating or
dividing the number of courses completed by the number
of courses attempted for each semester, over a period
of nine semesters. Success in a course was defined as
a grade of A, B, or C, or in cases of classes graded
with a pass/no pass scale, P (Hagedorn, 2006). I
success rate was then calculated on a per semester
basis by examining the student’s transcripts and
determining whether they were successful in the
course.

3
Research Questions
Based upon a review of the literature which is
discussed in the next section, I am asking the
following research questions:
• What are the patterns and frequency of
participation in peer and work groups among first
generation community college students?
• Do first generation community college students
who engage in peer group participation have
higher course completion rates, success rates and
grade point averages than first generation
community college students who do not?
• Do first generation community college students
who have larger course loads in terms of units
have higher course completion rates, success
rates and grade point averages than first
generation community college students with lower
course loads?

The first question, about student peer networks,
has not only potential practical implications, but is
also an area of research about which basic descriptive

4
research is necessary in the community colleges.
Though this topic has been studied at length for
decades in high schools and four year colleges
(Coleman, J, 1961; Hollingshead, 1975; Portes, 2005),
very little is known about the actual nature of
participation in community college student study
groups. The second of these questions, concerning
course load, is seen as an area of course-taking
activity that would have implications for college
interventions on behalf of first generation students,
if in fact it is substantially related to course
completion rates, success rates and grade point
averages. This study will examine course load in
terms of the number of units.
Limitations
The following limitations apply to this study:
1. The study is limited to the data collected in
the 2001 TRUCCS survey and related transcript data.
2. The research is descriptive in nature.
3. The results of the research will not be
generally applicable to all first generation college
students.

5
Delimitations
The study uses a survey conducted during the
spring of 2001 (TRUCCS) involving a cohort of students
who were enrolled in the Los Angeles Community College
District during that semester. The study focuses only
on study group participation, course load, course
completion rates, course success rates and grade point
averages.
Definition of Terms
Study group participation is defined as the
pattern of behavior students’ display when studying
for their classes. In this study, various forms of
study group participation patterns defined in the
survey instrument. Various forms of study group
participation utilized in the study included helping
another student understand homework, studying in small
groups outside of class (for any class), working in
small groups during class time, studying alone at the
home, studying alone at the college library, studying
outside class with students from this course and
studying outside class with students from other
courses (not this course).

6
Course load refers to the number of units a
student took during the semester, measured in four
increments: full (12+ units), three-quarters (9.5-11.5
units), half (6-9 units), or less than half (.5-5.5
units) semester load.
Student success is defined as how well students
performed in their college work. In this study, the
specific measures of student success, included course
completion rate, success rate and grade point average.
Course completion rate is measured on a per
semester basis by examining the student’s transcripts
and determining whether they completed the course. A
course completion is considered to be a grade of A, B,
C, D or F, or in cases of classes graded with a
pass/no pass scale, P or N (Hagedorn, 2006). A
completion rate was then calculated by dividing the
number of courses completed by the number of courses
attempted for each semester, over a period of nine
semesters.
Success rate is measured on a per semester basis
by examining the student’s transcripts and determining
whether they were successful in the course. A

7
successful grade is considered to be a grade of A, B,
or C, or in cases of classes graded with a pass/no
pass scale, P (Hagedorn, 2006). A success rate was
then calculated by dividing the number of course units
passed by the number of course units attempted per
semester, over a period of nine semesters.
Grade point average was measured for each student
in the sample on a per semester basis, over nine
semesters. Grade point average was calculated as the
number of units attempted per course multiplied by the
grade points per course to produce total grade points.
The total grade points were then divided by the number
of units attempted to arrive at grade point average.
Methodology
TRUCCS was a joint project with the University of
Southern California (USC), Rossier School of Education
and the Los Angeles Community College District
(LACCD). From 2001 to 2003, this longitudinal study
was performed to examine the goals, student success,
and academic patterns of approximately 5,000 community
college students in the Los Angeles area. This study
The TRUCCS survey encompassed over 40 questions

8
regarding demographics, course taking, studying
behavior, grade point average (GPA), and academic
goals.
The TRUCCS survey was administered beginning in
Spring 2001 to LACCD community college students from
varying backgrounds, ethnicities, gender, and ages.
The participants were not randomly selected. The
sample instead involved selected classrooms with the
objective of maximizing variance in some of the
study’s main independent variables. Two follow-up
surveys also were administered. Students from LACCD
signed release authorizations for their college
transcripts to be examined. The analysis of the
student’s course taking history is based on transcript
data. Data from the TRUCCS database were analyzed
using multiple statistical methods, including
frequencies, descriptive statistics, and correlations.

9
CHAPTER 2
REVIEW OF THE LITERATURE
There are two primary theoretical frameworks
related to study and work group participation that
will be discussed in this review: Tinto’s model
theorizes that social and academic integration within
the institution leads to persistence. Tinto’s model
examines how social integration, which is measured by
such factors as interaction with faculty and
participation in extracurricular activities, and
academic integration, which is usually measured by
grades or other indications of academic achievement
affect student persistence (Bailey, Alphonso, 2005).
Bean’s model theorizes that the environment has a
greater influence on student persistence.
Forms of Peer Group and Study Group Behavior
An important variable investigated in this study
is the peer and study group patterns and behaviors of
college students, which take many forms. In order to
provide an overview of the various types of peer and
study group behavior, various examples and definitions
from the literature are provided below.

10
Research conducted on peer and study groups has
provided a number of definitions and forms of peer and
student group activity.
Lundberg (2003) researched collaborative
learning, consisting of students who taught science
subject matter to other students in a peer learning
environment. Maxwell (1998) also examined the
collaborative active learning of students at a
community college, who participated in voluntary
supplemental instruction workshops.
A number of studies have researched the effect of
small student groups which provide individualized
feedback, develop learning skills, and student
integration. (Cockrell, Caplow, Donaldson, 2000;
Tinto, 1997; Cabrera, Nora, Castanada, 1993).
Stage (1989) examined hours spent in social
activities and hours engaged in intercollegiate
athletics, as well as students’ level of informal
faculty relations.
Feldman and Matjasko (2005) researched how
student involvement in extracurricular activities,
including student clubs and activities, student

11
government, school newspaper, vocational clubs
affected student success.
Pike, Kuh and Gonyea (2003) defined social
involvement in their study of college student
educational outcomes to include a student’s personal
experiences, acquaintances with other students, and
topics of conversation.
Pascarella and Terenzini (2005) defined work
group participation as students working with other
students in groups, during class.
Each of these forms of peer and study group
behaviors and the effect each had on student success
is discussed in more detail in the following section.
Functional Theories – Study Group Participation,
Integration, Involvement & Attrition
Studies have found strong evidence that student
success is related to study behavior, including time
spent at the library, and interactions with other
students (Kramer, 1997).
Kramer’s study included a sample consisting of
217 Hispanic graduates at one two-year private junior
college. Close to 90% of these graduates were first-

12
generation college students. Furthermore, they had
stopped their formal schooling for an average of nine
years before enrolling in college. Only 20% graduated
from a U.S. high school, 50% received the General
Education Diploma (GED), and 30% completed high school
outside the United States. The population in the study
consisted of commuter students, the majority of whom
were responsible for raising children and many of whom
were working off campus. They had little time for
extracurricular activities. Because of the nature of
the student body, the study institution has few
student organizations. Kramer concluded that given the
significance of students' integration into the
academic and social environment of the college for
persistence and success in college, it is important
that these concepts be operationalized correctly for
each student population. Once the validity of the
indicators is established, the institution can provide
experiences to strengthen the academic and social
integration of their students. (Kramer, 1997)
Lundberg (2003) investigated the way
collaborative learning that occurs primarily outside

13
the classroom affects college students' understanding
of science. Lundberg asserted that collaborative
learning is particularly important for the increasing
number of nontraditional students who have limited
time available for study groups and other peer
learning activities occurring outside of class time.
Using a national study of 4,644 college students of
various academic majors, multiple linear regression
was used to identify variables that enhance science
learning. Time spent in peer learning settings, such
as teaching science to peers and discussing science
with peers, were the strongest predictors of
understanding science; moreover, this finding was
consistent even for nontraditional students who
reported less frequency of engagement in such
activities. The study suggested that science educators
can enhance learning when they structure their courses
to include peer learning that engages students with
each other over science issues outside the classroom.
Lundberg (2003) found strong evidence that
students who participate in study groups are more
likely to be successful. The sample consisted of 4,644

14
undergraduate students who took the College Student
Experiences Questionnaire (CSEQ) during the 1998–1999
academic year, drawn from a larger data set of
approximately 20,000 students from 20 institutions.
The institutions were primarily comprehensive colleges
and universities (60%, n=2,767) or research
universities (25%, n=1,163), but included doctoral
universities 670 The Journal of Higher Education FIG.
1. Proposed Path (7.5%, n=347), liberal arts colleges
(7.2% n=337), and AA degree granting colleges (.6%,
n=30). The sample included slightly more women
(n=2,594, 57%) than men (n=2,050, 43%). Lundberg
(2003)
Research has provided evidence that a
collaborative approach to learning, including
activities such as supplemental instruction, provide
an effective environment for peer support by using
small student groups to provide individualized
feedback, and develop learning skills, collaborative
active learning and student integration. (Cockrell,
Caplow, Donaldson, 2000; Maxwell, 1998; Tinto, 1997;
Cabrera, Nora, Castanada, 1993).

15
Cockrell, Caplow, Donaldson (2000) researched
problem based learning in a Higher and Continuing
Education graduate program at a Midwestern Research I
university. Their research found that students
perceived themselves as the owners of knowledge that
is acquired in a collaborative learning community, and
this sense of ownership based on the work conducted in
groups, led to increased student success.
In a study at a middle-class suburban community
college of over 22,000 commuter students from diverse
backgrounds, Maxwell (1998) compared the performance
of students in supplemental instruction workshops,
with those who did not. Students who had qualified for
financial aid were invited to participate in the
supplemental instruction groups on a voluntary basis.
Maxwell found strong evidence that supplemental
instruction programs were correlated with students
studying together, which has been found to increase
student success.
Tinto (1997) examined the effect of a learning
community named the Coordinated Studies Program (CSP)
in a study of first-year students at Seattle Central

16
Community College. A sample of fifteen classes were
selected in order to capture a representative sampling
of first-year students in similar subjects, some of
which were enrolled in CSP and some who were not.
Tinto found strong evidence that learning communities
lead to higher persistence rates among students.
Cabrera, Nora, and Castaneda, (1993) examined the
effect of academic integration, social integration and
institutional commitment on student persistence in a
study of 2,459 first time college freshmen at a large
southern urban institution, and found strong evidence
that each variable had a positive effect on
persistence.
Such studies have shown that students have a
feeling of ownership of their learning outcomes and
knowledge when they acquire it through a collaborative
learning approach. Collaborative groups are an
organizational form that arise when a group works
together jointly and continuously on a particular
project towards a specific goal, and help students
take independent and collective ownership of
knowledge. Through these shared learning groups,

17
students develop a peer network that enables them to
persist at higher rates in their studies while
simultaneously meeting their social and academic
needs. These learning groups cause students to work
closely together and develop a network of support. By
actively involving students in the learning process as
a collaborative rather than competitive approach,
programs promote both student learning and academic
and social communities in college (Tinto, 1998; Astin
1993). In addition, collaborative settings such as
learning communities promote student involvement and
achievement in nonresidential settings where students
have numerous obligations outside of college.
Pascarella and Terenzini (2005) examined students
who participated in work group participation, or
working with other students in groups, during class.
Students who spend time participating with in-class
work groups have higher retention and success,
compared with those who do not.
Kuh and Zhao (2004, et al., 2001) studied a
sample comprised of 80,479 randomly selected first-
year and senior students from 365 4-year colleges and

18
universities who completed the National Survey of
Student Engagement (NSSE) survey in the spring of
2002. This is an annual survey of first-year and
senior students. The NSSE instrument measures the
degree to which students participate in educational
practices. Specifically, NSSE assesses student
experiences in the following areas: (a) involvement in
a range of educationally purposeful in-class and out-
of-class activities; (b) amount of reading and
writing; (c) participation in selected educational
programs, such as study abroad, internships senior
capstone courses, as well as learning communities; (d)
perceptions of the campus environment including the
quality of students’ relationships with peers, faculty
members, and administrators; and (e) student
satisfaction with academic advising and their overall
collegiate experience. Kuh and Zhao found that
participating in learning communities was uniformly
and positive linked with student academic performance,
engagement in educationally fruitful activities (such
as academic integration, active and collaborative
learning, and interaction with faculty members), gains

19
associated with college attendance, and overall
satisfaction with the college experience.
Chuetaco, Dennis and Phinney (2005) examined the
success of 100 first generation college students at a
four-year university and found that peer support was
weakly correlated with grade point average. In
contrast to the theoretical emphasis on social
integration, Bean and Metzner (1985) explicitly
developed a model of attrition for nontraditional
students, both at four-year and two-year institutions.
Their contention was that social integration would
play a much smaller role among these students and that
outside “environmental” variables would be more
important. These variables included finances, hours of
employment, outside encouragement and family
responsibilities. They also suggested that “goal
commitment” and “intent to leave” were important for
nontraditional students and that these students are
more focused on the economic benefits of their
education. Since Bean and Metzner place a great
deal of importance on environmental factors
outside the college’s control, their approach would

20
appear to leave less potential for an institutional
response. In their model, the two variables that are
under the control of the colleges are academic
advising and course availability. Presumably,
academic advising should be designed to increase
goal commitment and influence the student’s intent
to leave. Course availability is certainly a logical
determinant of attrition, especially for
nontraditional students, who generally have a more
instrumental view of their college education. The
results of their study concluded that nontraditional
students are more affected by the external environment
than by social integration variables affecting
traditional students.
Course Load
Course load represents the total number of
semester hours and letter graded courses, college
credit, pass/fail, and developmental courses,
specifically, course load is defined as a student’s
enrollment in a full (12+), three-quarter (9.5-11.5),
half (6-9), or less (1-5.5) than half unit load
(Szafran, 2001).

21
For purposes of this study, I will define course
load using these four categories. Previous research
has concluded that course load is not correlated with
academic success (Adelman, 1992, 2005; Tinto, 1987;
Metzner, 1989; Pascarella & Terenzini,, 1979; Szafran,
2001).
In examining course load and course taking
patterns, Adelman (1992, 2005) compared students
enrolled in community colleges to students enrolled in
other institutions of higher education and noted that
community colleges served a variety of needs and
functioned in a variety of ways for individuals at
varying stages in their lives. As a result, he noted
that students take courses for a variety of purposes,
and many part time students perform better
academically than full time students.
Metzner (1989) studied the effect of the
perceived quality of academic advising on student
attrition, for 1,033 first-time freshmen at a public
urban university. The study concluded that high
quality advising reduced attrition. Low-quality
advising was less successful in reducing attrition,

22
but was associated with less attrition than was no
advising at all. Metzner found that contrary to common
assumptions, students who register for more credits
tend to earn higher GPA’s and have greater retention
even after controlling for academic ability, prior
academic success, on-campus employment hours, and
other background characteristics. Students who
register for more difficult courses, however, tend to
earn lower GPAs and experience lower retention. Any
effect of credit load on retention appears to work
through GPA. While much of the effect of course
difficulty on retention also works through GPA, course
difficulty does have a separate negative effect on
one-year retention. While the possibilities that
weaker students might be more successful with lighter
credit loads or that stronger students might be more
successful with more difficult courses were
investigated, no significant interactions between
prior academic success, academic load, and success
were found.
Szafran (2001) studied 487 students at a four
year university. In the study, Szafran notes that both

23
students and advisers often assume that a lighter
academic load during the first year of college will
result in greater student success. The research is
based on the fall 1996 entering cohort of new college
students at Stephen F. Austin State University. These
students were registering for the first time at the
university and were bringing with them less than 15
hours of transfer credit. Stephen F. Austin State
University is a comprehensive regional university in
East Texas. Its total enrollment in the fall of 1996
was 11,690 of which 10,116 were undergraduates.
Persons applying to the university for admission as
new college students were required to meet one of the
following three requirements: graduation from high
school in the top half of their graduating class, a
cumulative SAT score of 1010 or greater, or a combined
ACT score of 21 or greater. The university typically
draws one third of its students from the Houston area,
one third from the Dallas-Fort Worth area, and one
third from other parts of East Texas. The fall 1996
entering cohort of new college students consisted of
2,047 persons. The cohort was 60 percent female and 21

24
percent minority. Their median SAT score was 1000 and
their median ACT score was 21. Their median percentile
rank in their high school graduating class was 68.
Because of its non-urban setting, relatively few
nontraditional age students enroll. In this entering
cohort, 93 percent were age 19 or less at entry into
college, and 97 percent were no older than 24. Because
the calculation of some variables involved
considerable data coding and manipulation, the
decision was made to work with a sample of the
students. From the entering cohort of 2,047 new
students, a 25 percent systematic random sample was
selected. This resulted in 512 students. Twenty-three
of these students were dropped from the sample since
they were not full-time students on the 12th day of
the semester, which is the university’s official
census day. An additional two students were dropped
from the sample because they withdrew from all their
classes before the end of the semester thereby
receiving no grades and, therefore, having no grade
point average. This left a sample of 487 students.
Academic load in the study was measured in terms of

25
credit load and course difficulty; success was
measured in terms of GPA and retention. The
experiences of a sample of first-year students at a
comprehensive regional university were examined. While
the credit loads for which students register were
related to academic ability and prior academic
success, the difficulty level of courses for which
these students register was not. Variation in student
credit loads was reduced because weaker students are
required to take developmental courses but do not drop
a corresponding number of college credit courses.
In contrast to the studies identified above
however, additional research has provided evidence
that students who take a larger number of credits
during their first term have higher retention rates.
Hoyt (1999) conducted a study at Utah Valley
State College (UVSC), an urban community college that
enrolled 18,174 students during the fall of 1998.
Historical data maintained on the college indicated
that over half of the students drop-out of the college
and fail to earn a degree or transfer, and about half
the entering freshmen require remedial education. The

26
average age of students at the college was 22. About
54% are males, and 46% females, with 93% White. With
the exception of having fewer minority students (7%
compared with 24% at two-year colleges nationally),
this population is similar to the national population
of students at community colleges where approximately
62% of those who enroll fail to earn any degree or
certificate and drop-out of college. The fall 1993,
1994, and 1995 freshmen cohorts were tracked to
determine how many students graduated, transferred,
were still enrolled, or dropped out of the institution
by fall 1998. Entering freshmen were identified by
obtaining a student’s first-term taking a college
course on the campus. In this study, students who took
a larger number of credit hours at the college during
their first-term had higher retention rates. This was
explained as full-time students may have a greater
commitment or more financial support for pursuing
their studies. Logistic regression was used to gain an
understanding of which variables had the strongest
direct relationship with student attrition. Because of
indirect relationships and inter-correlation among the

27
variables, it is difficult to assess the importance of
individual factors.
Zhao (1999) researched the factors affecting the
four-year academic performance and outcomes of 1,249
under prepared students at a community college. The
fall 1994 freshmen required remediation in reading,
writing, or mathematics. Subjects were defined as
achievers if, by summer 1998, they had earned a degree
or certificate from the college, transferred to a
senior college, or earned at least 30 credits. All
remaining subjects were regarded as nonachievers. End
of semester records for all students were reviewed for
relevant information. The study used Astin's input-
environment-outcome model and conducted logistic
regression analysis on 30 possible predictors with
academic outcomes as the dependent variable. One input
variable and five environment variables entered the
final model as a result of forward stepwise selection.
These six significant predictors of these students’
academic outcomes were: cumulative credit hours
earned; good academic standing; cumulative grade point

28
average; course load; the number of developmental
courses taken; and race/ethnicity.
Bailey (2004) found that students who take a full
time course load perform better, persist more and
transfer at a higher rate than students who take a
part time course load. Tierney (1992) also found that
full-time students have a greater likelihood of
graduating from college than part-time students.
Based on the evidence from the literature, I am
asserting that for first generation college students,
the higher number of units a student enrolls in, the
higher his or her completion rates, success rates and
grade point averages will be.
First Generation College Students
In a study conducted at an urban commuter
university, Dennis, Phinney, and Chuateco (2005) found
that for first generation college students, peer
support (or lack of needed peer support) is a stronger
predictor of student outcomes than support from the
family, when both family and peer support variables
are included in a regression analysis. They thus
confirmed that first generation college students would

29
perceive their peers as better able than their
families to provide the support they needed, in order
to do well at college. In focus groups carried out
with some of the participants in the study, many
students reported that peer support was the most
helpful strategy for dealing with academic problems.
Although the family members of first-generation
college students can provide emotional support, most
family members cannot provide vital instrumental
support.
In a study of first generation college students
at universities across the United States, Kuh, Pike &
Gonyea (2003) found that being a first generation
college student was negatively related to social
involvement and indirectly associated with lower
levels of integration and gains. They suggested that
institutions can create opportunities for these
students to engage in college life and to connect with
their peers, through formal extracurricular and other
institutional structures, such as learning
communities. A limitation of this study in its
application to first generation college students is

30
that the findings of this study are more likely to
hold for traditional age (i.e., 18-23 years old),
full-time students at 4-year institutions.
Pike and Kuh (2005) point out that several
aspects of first-generation students' college
experiences have been demonstrated to affect success
in college, even after controlling for precollege
characteristics. First generation students are less
likely to live on campus, to develop relationships
with faculty members, and to perceive faculty as being
concerned about their development. Each of these
consequences has a negative affect on persistence.
Also, Pascarella, Pierson, Wolniak, Terenzini,
(2004) found that extracurricular involvement had
significant positive effects on critical thinking,
degree plans, internal locus of attribution for
academic success, and preference for higher-order
cognitive tasks for first-generation students.
As defined by Bailey (2005), learning communities
typically organize instruction around themes, and
students go through such programs as cohorts. Learning
communities are designed to provide more coherent and

31
engaging experiences than traditional courses, and to
give students and faculty more opportunities for
increased intellectual interaction and shared inquiry.
However, Bailey (2004) points out that the design of
learning communities may discourage non-traditional
students from participating. Because a learning
community typically requires a cohort of students to
attend several classes in group, it may be difficult
for working students or students attending part-time
to participate.
In a study of learning communities at LaGuardia
Community College, Tinto and Love (1995) found that
many student work groups were maintained outside of
class, after being formed in class. Learning
communities allowed students to form peer groups as
well as do other things that they would not have done
if they were not in cohort classes with those peers.
The students in those learning communities also
reported high levels of social and academic support,
which contributed to their involvement in class, their
satisfaction, and their belief that they would stay in
college. However, measures of persistence within the

32
college showed only slight differences for students
who participated in learning communities, compared
with those who did not.
In their review of the literature on first
generation college students, Grimes and David (1999)
state:
The first-generation status of students is
an important factor because approximately 80% of
community college students are first-generation
college students (Phillippe,1995), and this
first-generation status has often been associated
with other risk factors such as remedial
placement, lower socioeconomic status, and
minority status. Pascarella et al. (1996) suggest
that first-generation students enter college
academically at risk and then encounter a world
where they are less likely to experience many
conditions positively related to persistence,
performance, and learning. They report that
first-generation students are often under-
prepared students with weaker reading,
mathematics, and creative thinking skills. These

33
students also indicate lower degree aspirations
and anticipate a longer time to complete a
degree.

These are important issues to consider, as first
generation college students present colleges with
unique needs and challenges, compared to traditional
college students whose parents have likely attended
college in the past. Based on the theory of social
integration, I am asserting that increased involvement
in study group behavior for first generation college
students will have a positive effect on their course
completion rates. More specifically, based upon the
review of the above literature, I am asserting the
following hypothesis statements:
• First generation community college students
who have a higher semester course load will
experience higher course completion rates
than first generation community college
students who have a lower semester course
load.

34
• First generation community college students
who have a higher semester course load will
experience higher success rates than first
generation community college students who
have a lower semester course load.
• First generation community college students
who have a higher semester course load will
experience higher grade point averages than
first generation community college students
who have a lower semester course load.
• First generation community college students
who engage in peer or work group
participation will have higher course
completion rates than first generation
community college students who do not.
• First generation community college students
who engage in peer or work group
participation will have higher success rates
than first generation community college
students who do not.
• First generation community college students
who engage in peer or work group

35
participation will have higher grade point
averages than first generation community
college students who do not.

36
CHAPTER 3
METHODOLOGY
Sample
The sample in this study is from a population of
recent community college students from the Transfer
and Retention of Urban Community College Students
Project (TRUCCS), conducted in the Los Angeles area.
The TRUCCS Project contains questionnaire and
transcript data from approximately 5,000 students
across the nine Los Angeles Community College (LACCD)
campuses. Questionnaire data was collected in the
Spring of 2001 from a cross section of community
college students, representative of the district
(Hagedorn, 2002).
Students who participated in the study provided
releases of their transcripts and other district
records for the purposes of the research study. The
initial survey data collection occurred in during the
Spring 2001 semester. For purposes of this study, the
analysis of study group participation is based on the
survey and transcript data for the Spring 2001
semester.

37
The analysis of course load is on a per semester
basis, and includes transcript data from nine
semesters for students who were defined as first
generation college students based on responses
recorded in the Spring 2001 survey. Specifically, the
semesters analyzed included Spring 2000, Fall 2000,
Spring 2001, Fall 2001, Spring 2002, Fall 2002, Spring
2003, Fall 2003 and Spring 2004.
After identifying the students in the sample that
were first generation college students, I then
analyzed the transcript data from the Spring 2000
semester through the Spring 20004 semester, examining
on a per semester basis, the course load that each
first generation college student carried. Each student
by semester produced a case to be analyzed. This
method of organizing the data produced the 9,828 cases
analyzed in this study.
Specifically, the sample for this study consisted
of 4,967 students at the LACCD, of whom 1,906 were
first generation college students. First generation
college students were determined using a TRUCCS survey
question which asks students to identify the highest

38
level of education obtained by their father and
mother. Question 41
1
of the TRUCCS Survey conducted in
Spring 2001 asked “What is the highest level of formal
education obtained by your parents either in the U.S.
or in another country? (Mark one response in each
column).”
Students who responded that both their mother and
father obtained a high school/GED or less education,
were categorized as first generation college students.
If either parent had less than a college education,
but the other parent had some college experience, the
student was not considered a first generation college
student for purposes of this study.
The sample of first generation college students
included 1,165 female students and 711 male students.
(See Table 15 in the appendix). 381 students in the
sample claimed to be first time college students,
meaning they had zero units of prior college
experience. The number of students who claimed to have
at least some college experience (one or more units of
experience) was 1,479. (See Table 16 in the appendix).

39
Measures
Data files containing enrollment history were
obtained directly from the student transcript database
at the institution. Given the careful processes
involved in assembling these files (which are the
basis for course transcripts), these data provide a
much higher degree of reliability and validity than do
student survey reports of course enrollments.
(Maxwell, 2003)
Independent variables
The first independent variable course load was
measured by examining the number of units that
students in the sample are enrolled in, to distinguish
between students enrolled in course load is defined as
a student’s enrollment in a full (12+), three-quarter
(9.5-11.5), half (6-9), or less (1-5.5) than half unit
load. For purposes of this study, I will define
course load using these four categories. Students were
defined as enrolled in a course if they remained in
the course past the first census date and thus earned
either a favorable (A,B,C,D, Pass) or unfavorable (F,
Withdrawal, Incomplete, No Pass) grade. For purposes

40
of student enrollment, a measure called units
attempted was created which counts any units the
student is enrolled in as of the census date – the
date after which the student will receive a W if he or
she drops. Prior to that date, any add/drop activity
is considered course shopping.
The second independent variable, peer group
participation, was measured based on the TRUCCS survey
responses to questions that asked specifically about
study behavior, taken from the following survey
questions:

Question 13 of the TRUCCS Survey conducted
in Spring 2001 asked “In the past 7 days,
approximately how many TIMES did you help another
student understand homework” and “In the past 7 days,
approximately how many TIMES did you study in small
groups outside of class (for any class)”. Question 14
3

of the TRUCCS Survey conducted in Spring 2001 asked
“In the past 7 days, approximately how many TIMES did
you work in small groups during class time”. Question
15
4
of the TRUCCS Survey conducted in Spring 2001
asked “In the past 7 days, approximately how many
HOURS did you study alone at the home”, “In the past

41
7 days, approximately how many HOURS did you study
alone at the college library”, “In the past 7 days,
approximately how many HOURS did you study outside
class with students from this course” and “In the past
7 days, approximately how many HOURS did you study
outside class with students from other courses (not
this course)”. (For the detailed Likert scale response
choices offered in the survey, see Appendix B).
Dependent variable
The first dependent variable, course completion
rate, was measured on a per semester basis by
examining the student’s transcripts and determining
whether they completed the course. A course completion
is considered to be a grade of A, B, C, D or F, or in
cases of classes graded with a pass/no pass scale, P
or N (Hagedorn, 2006). A completion rate was then
calculated by dividing the number of courses completed
by the number of courses attempted for each semester,
over a period of nine semesters.

Number of courses with the grade of
A, B, C, D, F, P or N
CCR=
Number of courses of enrollment

42

The second dependent variable, success rate, was
measured on a per semester basis by examining the
student’s transcripts and determining whether they
were successful in the course. A successful grade is
considered to be a grade of A, B, or C, or in cases of
classes graded with a pass/no pass scale, P (Hagedorn,
2006). A success rate was then calculated by dividing
the number of course units passed by the number of
course units attempted per semester, over a period of
nine semesters.

Number of courses with the grade of
A, B, C, or P
SR=
Number of courses of enrollment

The third dependent variable, grade point
average, was measured for each student in the sample
on a per semester basis, over nine semesters. Grade
point average was calculated as the number of units
attempted per course multiplied by the grade points
per course to produce total grade points. The total
grade points were then divided by the number of units
attempted to arrive at grade point average.

43
Procedures
Throughout this study, the student was the unit
of analysis. Statistical analysis was performed
including the calculation of descriptive statistics,
comparison of means and Pearson correlations. Analysis
was performed to evaluate the relationship between
peer group participation and completion rates, success
rates and grade point average, as well as the
relationship between course load and completion rates,
success rates and grade point average.

44
CHAPTER 4

FINDINGS

Peer Group Participation
Effect of Peer Group Participation Completion Rates,
Success Rates and Grade Point Average
After analyzing each of seven peer group and study
participation variables, three variables appeared to
be statistically significant, yet they produced small
correlations. The first five tables shown below
summarize the mean completion rates, success rates and
grade point average for three peer group variables
that are statistically significant showed
correlations. The more a student helped another
student understand homework, the higher the success
rates and grade point averages they experienced (See
Tables 1 and 2).

45
Table 1

Course Completion Rates (CCR) and Success Rates(SR) of
First Generation College Students by Frequency of
Helping Another Student Understand Homework within the
past 7 days (N=1,851)
Helping another
Student
Understand
Homework Mean CCR Sample(N) Mean SR Sample (N)
None .868 (615) .701 (615)
1 time .901 (477) .724 (477)
2 times .915 (361) .766 (361)
3 times .893 (193) .778 (193)
4 times .919 (75) .765 (75)
5+ times .940 (130) .802 (130)
p = .000
Helping another student understand homework:
Mean CCR .895 Mean SR .737
Standard deviation CCR .208 Standard deviation
SR .329

46
Table 2
Grade Point Averages (GPA) of First Generation
College Students by Frequency of Helping Another
Student Understand Homework within the past 7 days
(N=1,786)
Helping another student
understand homework Mean GPA Sample (N)
None 2.441 (581)
1 time 2.491 (463)
2 times 2.691 (351)
3 times 2.675 (190)
4 times 2.653 (74)
5+ times 2.761 (127)
p = .000
Helping another student understand homework:
Mean GPA 2.560
Standard deviation GPA 1.055

When examining the mean completion rates and
grade point averages for the variable of studying
alone at home, the more students studied at home, they
experienced higher completion rates and grade point
averages. However, this finding is not inconsistent
with the hypothesis as one could study frequently with
peers and also study frequently at home. The variables
would not be mutually exclusive.

47
After computing a test of statistical
significance for the difference between means, the
result was sig (2-tailed) p = .045 for completion
rates, p= .000 for success rates and p=.000 for grade
point average (See Tables 3 and 4). The Pearson
coefficient for studying alone at home and completion
rates was r=.046, and the Pearson coefficient for
studying alone at home and success rates was r=.110,
indicating weak correlations between variables. It
should be noted that a larger sample size like this
one yields a more statistically significant
coefficient than would a smaller sample.

48
Table 3
Course Completion Rates (CCR) and Success Rates(SR) of
First Generation College Students by Frequency of
Studying Alone at Home within the past 7 days
(N=1,865)
Studying
Alone
At Home Mean CCR Sample (N) Mean SR Sample (N)
None .921 (69) .735 (69)
< 1hr .862 (115) .645 (115)
1-2 hrs .875 (413) .691 (413)
3-5 hrs .897 (572) .733 (572)
6-10 hrs .909 (400) .759 (400)
11-20 hrs .898 (191) .813 (191)
21-35 hrs .912 (67) .802 (67)
36-45 hrs .925 (16) .798 (16)
46+ hrs .928 (22) .768 (22)

Completion Rates Success Rates
p = .045 p = .000
Mean CCR .895 Mean SR .736
Standard deviation CCR .208 Standard deviation
SR .329

49
Table 4
Grade Point Averages (GPA) of First Generation
College Students by Frequency of Studying Alone at
Home within the past 7 days
(N=1,800)
Studying Alone at Home Mean GPA Sample (N)
None 2.305 (69)
< 1hr 2.199 (109)
1-2 hrs 2.357 (390)
3-5 hrs 2.542 (555)
6-10 hrs 2.676 (389)
11-20 hrs 2.947 (187)
21-35 hrs 2.830 (64)
36-45 hrs 2.972 (16)
46+ hrs 2.755 (21)
p = .000
Mean GPA 2.559
Standard deviation GPA 1.057

Table 5 indicates that the more first generation
college students study with students from other
courses, the higher their GPA will be (insofar as the
subsamples are larger than 30 students and large
enough to estimate GPA levels). It should be noted
that the percentage of students in the sample who
study less than one hour per week with students from

50
other courses is over 82% of the sample. However, the
sample size of students who study with students from
other courses is so small, that this variable will not
be discussed further.

Table 5
Grade Point Averages (GPA) of First Generation
College Students by Frequency of Studying With
Students from Other Courses within the past 7 days
(N=1,789)
Studying with students
From other courses Mean GPA Sample (N)
None 2.535 (1,288)
< 1hr 2.540 (185)
1-2 hrs 2.570 (227)
3-5 hrs 2.727 (97)
6-10 hrs 2.814 (37)
11-20 hrs 2.792 (10)
21-35 hrs 3.524 (6)
36-45 hrs 3.333 (2)
46+ hrs 2.692 (1)
p = .000
Helping another student understand homework:
Mean GPA 2.562
Standard deviation GPA 1.056

51
Peer Group Participation & Course Success
Tables 6, 7, and 8 below show the Pearson
correlations for each of the relationships between the
dependant variables and each of the independent
variables. When measuring the correlation between the
dependent variables and each of the other independent
study group variables, results showed that some of the
three items being analyzed were statistically
significant in terms of the relationship to course
completion rates, success rates and grade point
average. A number of variables proved to be
statistically insignificant, so their results will not
be discussed in detail.
The strongest associations were in fact, the weak
correlations between helping another student
understand homework and course completion (r=.090),
helping another student understand homework and
success rates (r=.098), studying alone at home and
success rates (r=.110), helping another student
understand homework and grade point average (r=.101),
studying at home alone and grade point average r=.179)

52
and studying with students from other courses and
grade point average (r=.065).

53
Table 6
Pearson Correlations between Course Completion Rates
and Studying Together
Variables Correlation of
Studying Together
& DV - Course
Completion Rate Mean Standard Deviation N
DV - Course - 0.895 .208 1,906
Completion
Rates

IV1 – Help another .090** 2.47 1.482 1,851
Student
Understand
Homework

IV2 – Study in .039 1.69 1.239 1,847
Small groups
Outside of class

IV3 – Work in .029 2.00 1.377 1,862
Small groups
During class time

IV4 – Study alone .046* 4.14 1.477 1,865
At home

IV5 – Study alone .021 2.17 1.466 1,843
In the
College library

IV6 – Study with .017 1.54 1.008 1,849
Students from
This course

IV7 – Study with .032 1.65 1.169 1,853
Students from
Other courses

** Correlation is significant at the .01 level
(2-tailed) p < .01
* Correlation is significant at the .05 level
(2-tailed) p < .01

54
Table 7
Pearson Correlations between Success Rates and
Studying Together
Variables Correlation
Of Studying
Together and
Success Rates Mean Standard Deviation N
DV - Success Rates - .737 .328 1,906

IV1 – Help another .098** 2.47 1.482 1,851
Student
Understand
Homework

IV2 – Study in .030 1.69 1.239 1,847
Small groups
Outside of class

IV3 – Work in .030 2.00 1.377 1,862
Small groups
During class time

IV4 – Study alone .110** 4.14 1.477 1,865
At home

IV5 – Study alone .026 2.17 1.466 1,843
In the College
Library

IV6 – Study with -.012 1.54 1.008 1,849
Students from
This course

IV7 – Study with .048* 1.65 1.169 1,853
Students from
Other courses

** Correlation is significant at the .01 level (2-
tailed) p < .01
* Correlation is significant at the .05 level (2-
tailed) p < .01

55
Table 8
Pearson Correlations between Grade Point Average (GPA)
and Studying Together
Variables Correlation
of Studying
Together
and GPA Mean Standard Deviation N
DV - GPA - 2.558 1.055 1,838

IV1 – Help .101** 2.47 1.482 1,786
Another student
Understand homework

IV2 – Study -.003 1.69 1.239 1,784
in small groups
Outside of class

IV3 – Work .013 2.00 1.377 1,797
In small groups
During class time

IV4 – Study .179** 4.14 1.477 1,800
Alone at home

IV5 – Study .061* 2.17 1.466 1,782
Alone in the
College library

IV6 – Study .007 1.54 1.008 1,785
With students
From this course

IV7 – Study .065** 1.65 1.169 1,789
with students
From other courses
** Correlation is significant at the .01 level
(2-tailed) p < .01
* Correlation is significant at the .05 level
(2-tailed) p < .01

56
Course Load
Rates of Course Load
The sample of 1,906 first generation college
students produced 9,828 cases to analyze. These cases
were generated by the first generation college
students over nine semesters. The findings indicate
that for first generation college students the higher
the course load in terms of units, the higher the
completion rates (Table 9), success rates (Table 10)
and grade point averages (Table 11). The course loads
in terms of units were highly variable and each of the
four categories had a large sample from which to test.

57
Table 9
Course Completion Rates of First Generation College
Students by Course Load (by units attempted)
N=9,828
Course load Mean Course Completion Rate Sample (N)
12 or more units .899 (4,044)
9.5 to 11.5 units .850 (952)
6 to 9 units .855 (2,985)
Less than 6 units .822 (1,847)
p = .000
Course load information:
Mean .8663 Standard Deviation .263

58
Table 10
Success Rates of First Generation College Students by
Course Load (by units attempted)
N=9,828
Course load Mean Course Completion Rate Sample (N)
12 or more units .770 (4,044)
9.5 to 11.5 units .684 (952)
6 to 9 units .692 (2,985)
Less than 6 units .655 (1,847)
p = .000
Course load information:
Mean .719 Standard Deviation .353

59
Table 11
Grade Point Average of First Generation College
Students by Course Load (by units attempted)
N=9,147
Course load Mean Course Completion Rate Sample (N)
12 or more units 2.788 (2,248)
9.5 to 11.5 units 2.631 (995)
6 to 9 units 2.515 (3,249)
Less than 6 units 2.421 (2,248)
p = .000
Course load information:
Mean 2.584
Standard Deviation 1.066

Effects of Course Load on Course Completion Rates,
Success Rates and Grade Point Average
After computing a test of statistical
significance for the difference between means, the
result was statistically significant (2-tailed) p =
.000. The Pearson coefficient for course load and
completion rates was r=.108 (Table 12), success rates
was r=.135 (See Table 13) and grade point average
r=.134 (See Table 14), indicating weak correlations

60
between course load and each of the three measures of
student success. In connection with the probability
level of .000, it should be noted that a larger sample
size like this one yields a more statistically
significant coefficient than would a smaller sample.

Table 12
Pearson Correlations between Course Completion Rates
and Course Load N=9,828

Variables Correlation of
Course Load
And Course Standard
Completion Rates Mean Deviation
DV - Course Completion - .866 .263
Rates
636
IV - Course Load .108** 2.72 1.185
p = .000

** Correlation is significant at the .01 level
(2-tailed) p < .01

61
Table 13
Pearson Correlations between Success Rates and Course
Load N=9,828

Variables Correlation of
Course Load
And Success Standard
Rates Mean Deviation
DV – Success Rates - .719 .353

IV – Course Load .135** 2.72 1.185
p = .000

** Correlation is significant at the .01 level
(2-tailed) p < .01

Table 14
Pearson Correlations between Grade Point Average (GPA)
and Course Load N=9,147

Variables Correlation of
Course Load Standard
And GPA Mean Deviation
DV - GPA - 2.584 1.066

IV – Course Load .134** 2.44 1.148
p = .000

** Correlation is significant at the .01 level (2-
tailed) p < .01

62
CHAPTER 5
SUMMARY OF STUDY
This purpose of this study was to examine the
effects of peer group study and course load on the
course completion rates, success rates and grade point
average of first generation college students at the
community college. The evidence for first generation
community college students indicates genuine
correlations of both peer group behavior or course
load with course completion rates, success rates and
grade point averages, though these correlations are
all small. The hypothesis stated in this paper
concerning study group participation was supported by
only some of the measures and involved only small
associations.
When measuring the correlation between the
dependent variables and each of the other independent
study group variables, results showed that about six
of the variables being analyzed were statistically
significant in terms of the relationship to course
completion rates, success rates and grade point
average. A number of variables proved to be

63
statistically unrelated to student success rates, so
they were not discussed in detail as part of this
study and the related findings.
The strongest associations were in fact, the weak
correlations between helping another student
understand homework and course completion (r=.090),
helping another student understand homework and
success rates (r=.098), studying alone at home and
success rates (r=.110), helping another student
understand homework and grade point average (r=.101),
studying at home alone and grade point average
(r=.179) and studying with students from other courses
and grade point average (r=.065). Due to the large
sample size, these correlations indicated statistical
significance, in spite of their weak associations.
The variable of studying alone at home seems to
provide an interesting challenge to the hypothesis
that first generation community college students who
engage in peer group participation will have higher
course completion rates than first generation
community college students who do not. If you set
aside the 3.6% of the sample who indicated that they

64
did not study alone at home whatsoever, the more
frequently the student studies alone at home, the
higher the course completion rates they experience. It
is very possible however that students who study in
groups also supplement their group study with
considerable time studying alone at home or in the
library. Therefore, it should be noted that the
correlation for studying alone at home does not
necessarily contradict the hypothesis that first
generation community college students who engage in
study or work group participation will have higher
persistence rates than first generation community
college students who do not, as it is possible for
students in the sample who study frequently alone at
home may also study frequently in groups. These two
variables are not mutually exclusive.
Though these correlations are weak, they are
consistent with finding in the literature which show
that participation in peer learning groups or
collaborative learning environments increases student
success rates (Kuh and Zhao 2004, et al., 2001;
Pascarella and Terenzini, 2005; Cockrell, Caplow,

65
Donaldson, 2000; Maxwell, 1998; Tinto, 1998; Astin,
1993) Through shared learning, students develop a peer
network and support system that enables them to
persist at higher rates in their studies while
simultaneously meeting their social and academic
needs.
A limitation of this study is that due to the use
of data from the Spring 2001 semester only in
analyzing peer group study behavior, the possibility
exists that the observed findings could be anomalous
rather than truly representative. Another limitation
is that while the transcript data are controlled and
were obtained directly from the institution, the
measures for study group participation were based on
student responses to survey questions.
There is still much to be learned about the
fastest growing segment of California’s Community
College population – first generation college
students. While course load appears to consistently
predict course completion rates – full time students
have been shown to experience higher course completion
rates than part time students - the correlation is

66
weak at best. The results of this study are consistent
with the findings of Zhao (1999), Bailey (2004), Hoyt
(1999) and Tierney (1992). Bailey found that students
who take a full time course load perform better,
persist more and transfer at a higher rate than
students who take a part time course load. Hoyt (1999)
concluded that full-time students may have a greater
commitment or more financial support for pursuing
their studies. Tierney (1992) also found that full-
time students have a greater likelihood of graduating
from college than part-time students.
However, the results of this study contradict the
findings of Adelman (1992, 2005) who found that
students take courses for a variety of purposes, and
many part time students perform better academically
than full time students, and Szafran (2001) who found
that increased credit course loads did not lead to
higher student success.
Since first generation college students at the
community college have rarely been researched at urban
community colleges, it is not surprising that the
results of this study are not consistent with some of

67
the existing research. Additional research is needed
on first generation college students at the community
college, in various settings, including urban, rural
and suburban.
In reflecting on the descriptive statistics for
study group behavior, the latter was shown to both
increase and decrease course completion rates for
first generation college students, depending on
whether the study group occurred during class or
outside of class. However, in almost every measure of
study participation in groups or alone, the
correlations with course completion rates were almost
always statistically insignificant. Since this study
only examines data from a particular college district
in Los Angeles, more research is needed in order to
examine the relationships in this study in other
community college settings.
Much like Dougherty (1992) who pointed out that
the independent variable of social integration varies
little in community college students, and therefore is
unable to explain differences in the dependant
variable of course completion rates, this study

68
suggests that the effect of study group behavior on
course completion rates are generally statistically
insignificant. The fact that there is conflicting
evidence in the literature regarding the effect of
course load on course completion rates and other
measures of student success further emphasizes the
need for additional research in this area,
specifically for non-traditional students, including
first generation college students.
First-generation college students may be less
likely to persist due to a lack of support from home.
Parents who have not earned a four-year degree may not
fully appreciate the value of higher education nor
expect their children to finish their studies. These
parents may lack the economic means to assist their
children financially. Parents without college
experience may be less informed about the process and
less able to guide and support their children through
the college experience. This supports the need for
programs to assist these students (Hoyt, 1999).

69
Implications for Practice
In reflecting on the conclusions generated for
the variable of course load, there are some
implications for community college practice. Community
college administrators need to continue to educate
students on the availability of student financial aid.
In many cases, students avoid a full time course load
due to the need to work to support themselves and
their families financially, as they attend college.
Given an opportunity to utilize financial aid,
students would be more likely to take on a course load
consisting of 12 or more units, and, statistically,
stand a better chance of achieving high course
completion rates.
With California continuing to be a top
destination for immigrants from around the world, the
population of first generation college students is
certain to continue growing at a rapid pace. It will
be worthwhile for today’s and tomorrow’s Community
College leaders to understand as much as possible
about these students, to ensure the highest rates of

70
student success. Doing so will help ensure a well
educated workforce for a competitive economy.
Implications for Further Research
A specific area of study that would benefit from
additional research is the behavior and experiences of
first time students among first generation college
students. Clearly, as students experience college,
they learn how to be successful, how to persist. But
how much of the first generation college student
success in course completion is due directly to the
variables examined in this study, compared to other
unrelated variables, or perhaps trial and error
(learning the successful traits of being a student as
they move through the system)? Still, based on other
research in the literature, programs such as learning
communities or cohort groups, or less structured group
activities like group assignments in and outside of
class are worth researching further, to better
understand their effectiveness.
Additional research is needed to better
understand the effect of study group participation on
course completion for first generation college

71
students as well as the additional variables that
contribute to high course completion rates among these
students. Based on the literature, programs that group
or cluster students together in and out of class are
likely to improve college course completion rates
among first generation college students, yet this
study did not find much statistically significant
evidence to support the literature.
Recommendations
A key finding identified in this study is how
little social interaction exists around academic
activities and studying together. In virtually every
analysis of study group behavior and course
completion, there is very little study group activity
in the sample. Many of the variables analyzed reported
a majority of the students sampled not participating
with study groups or peers at all. For those students
in the sample who did report some level of study group
activity, the majority of those students did so once
per week, or only a couple hours or less per week. I
suggest that this finding offers a possible
explanation regarding the small associations between

72
variables. Future research should be performed to
further investigate this phenomenon.

73
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Abstract (if available)

Abstract The purpose of this study was to understand some of the conditions likely to enhance the student success of first generation college students at the community college. Specifically, I was interested in the effect that course load and student study group participation have on the course completion rates, success rates and grade point averages of first generation college students. Studies have shown that student success rates, course completion rates and grade point averages are positively impacted by study group participation, and certain course-taking patterns. I was interested in how these variables affect the student success of first generation community college students. I performed statistical analysis including several descriptive statistics and frequencies, Pearson correlations, and comparison of means tests on a sample of first generation college students from the Los Angeles Community College District (LACCD) to investigate the relationship between the dependant variables of course completion rates, success rates and grade point averages and the independent variables of study group participation and course load. This study found a small correlation between course load and course completion rates, a small correlation between course load and success rates, a small correlation between course load and grade point average, and weak correlations among various study group participation variables and course completion rates, success rates and grade point averages for first generation college students.

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Asset Metadata

Creator Schulz, Gregory Vincent (author)

Core Title Model based view-invariant human action recognition and segmentation

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 04/19/2007

Defense Date 09/07/2006

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag community college,completion rate,course load,first generation college student,OAI-PMH Harvest,peer group,study group,success rate

Language English

Advisor Maxwell, William E. (committee chair), McGuire, Gary (committee member), Rideout, William (committee member)

Creator Email gschulz@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m422

Unique identifier UC1151180

Identifier etd-Schulz-20070419 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-479594 (legacy record id),usctheses-m422 (legacy record id)

Legacy Identifier etd-Schulz-20070419.pdf

Dmrecord 479594

Document Type Dissertation

Rights Schulz, Gregory Vincent

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu