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Comparing the effectiveness of online and face-to-face classes among California community college students

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Comparing the effectiveness of online and face-to-face classes among California community college students

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COMPARING THE EFFECTIVENESS OF ONLINE AND FACE-TO-FACE CLASSES
AMONG CALIFORNIA COMMUNITY COLLEGE STUDENTS

by

Treisa Sullivan Cassens

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

August 2010

Copyright 2010 Treisa Sullivan Cassens

ii
Dedication

To my wonderful husband and amazing daughter,
thank you for your patience, love and support,
without which this journey would not have been possible;
for you this journey was taken.

iii
Acknowledgments
This dissertation is the culmination of the support of many people. There are too
many people who have mentored me towards seeking out this degree, processing through
to the end and who will celebrate its completion.
Thank you to Dr. Guilbert Hentschke, the chair of my dissertation committee
whose guidance, support and words of wisdom (“a problem deferred is a problem
solved”) helped keep the process do-able. Thank you also goes to my committee
members, Dr. Melora Sundt and Dr. David Dwyer, for their words of wisdom and the
hours of time they spent with this document. Tons of gratitude goes to my fellow TOOL
members who shared laughs, tears and the long process with me. Thank you David
Bolton, Val Callet, Laura Castaneda, Ryan Corner, and Jim Peng. Jason Rey, you have
been the saving grace both for keeping me on track, helping to talk about the study and
for the friendship you have extended. Thank you also to the fellow USC Orange County
weekend cohort members, including Mr. Patrick, who shared in the pains of academia
and being away from places that we probably would rather be.
Special thanks to colleagues and friends Sue Berman, Adele Dick and Doug
Larson for your friendship, moral support and encouragement. Thank you goes to Gregg
Carr who was both my personal cheerleader and security guard, walking me to my car on
all those late nights through the dark and scary parking lot. You‟re a model of both
professionalism and class in the field of education. To Dwayne Thompson the god of
research whose brain works in amazing ways. To the library group and all my other
amazing friends - all I can say is simply Thank You!
iv
To my family and friends who did without me over the last several years at
parties, gatherings and through all sorts of important events. Thanks for keeping my
spirits up! Jim and Jennifer thank you for the beautiful nature of your continued lifelong
friendship. To Melissa Messner, who is my best friend and sister, thank you for
everything you are and everything you mean to me. Everyone has been so patient with
me and thank you is not enough, but these are the best words I can come up with.
Special thank you to my mom, Janet Means, who always believes that I can attain
the highest standards and has unwavering faith in me. Your belief that I could and would
do this, continued love, countless hours of telephone support, babysitting and patient
understanding is beyond belief and deeply felt.
Special thank you and love goes to my husband who is amazing in every possible
way! You were there time and time again to redirect my path, support me with whatever I
needed (time, food, advice, statistics help) and are truly my knight in shining armor.
Thank you to our daughter Zoë who was patient and wise beyond her years, and was able
to push me through the last loop by wishing on a penny that mommy would complete her
doctorate. Now that this has reached its completion, I look forward to spending all sorts
of time with you both – bring on the tickle wars!

v
Table of Contents
Dedication ii
Acknowledgments iii
List of Tables vii
Abstract viii
Chapter One: Introduction
Background
The Economic Problem and Higher Education 1
Online Education as a Partial Solution to a Multiplicity 3
of Demands
Statement of the Problem 4
Higher Education Curriculum Models 5
Purpose of the Study 7
Research Question 7
Significance of Study 8
Overview of the Dissertation Sections 8

Chapter Two: Literature Review
Distance Education in Higher Education 10
Online Education as Relatively Effective in Higher Education 12
Faculty Communication, Interpersonal Relationships and Student Success 15
Methodological Questions of Evaluation 16
Comparison of Online versus Face-to-Face Classes 18
Community Colleges and Student Characteristics 25
Research Question 32
Chapter Three: Methodology
Research Question 34
Participants 35
Data Collection and Sampling 36
Data Analysis Framework 38
Qualitative Measures 40
Discussion and Significance 43
Limits and Ethics 43

Chapter Four: Findings and Discussion
Treatment of the Data 47
Comparisons for Online and Face-to-Face Data Sets 48
Findings for Overall Comparison of Online Distance Education versus 50
Face-to-Face Classes
vi
Findings for Comparison of Online Distance Education versus 52
Face-to-Face Classes for Low Income Students
Findings for Comparison of Online Distance Education versus 54
Face-to-Face Classes for Basic Skills Students
Findings for Age Categories Comparison of Online Distance Education 56
Face-to-Face Classes
Findings for Gender Comparison of Online Distance Education versus 63
Face-to-Face Classes
Findings for Ethnic Group Comparison of Online Distance Education 66
versus Face-to-Face Classes
Faculty Survey Findings for Online DE versus Face-to-Face 73
Communication Patterns
Chapter Five: Conclusion
Summary of Findings 78
Risk Factors and Limitations 81
Further Research 82
Implications for Practice 84
Conclusion 86
References 88
Appendices:
Appendix A: Quantitative Data 98
Appendix B: Board of Governors Fee Waiver (BOG) 113
Appendix C: Courses Included in the Study 115
Appendix D: Faculty Survey 117
Appendix E: Student Counts for Online versus Face-to-Face Enrollments 119

vii
List of Tables

Table 1: Quantitative Elements of the Study: Statistical Tests Performed 39
Table 2: Faculty Survey Questions 41
Table 3: Quantitative Elements of the Study Test 1: Overall – F2F versus 52
Online DE
Table 4: Quantitative Elements of the Study Test 2: BOG – F2F versus 54
Online DE
Table 5: Quantitative Elements of the Study Test 3: Basic Skills – F2F versus 56
Online DE
Table 6: Quantitative Elements of the Study Test 4: Age – F2F versus Online DE 60
Table 7: Quantitative Elements of the Study Test 5: Gender – F2F versus 65
Online DE
Table 8: Quantitative Elements of the Study Test 6: Ethnicity – F2F versus 70
Online DE
Table 9: Students‟ Ability to Utilize Communication Methods Effectively 74
Table 10: Faculty Communication Methods Used 76

viii
Abstract
Over the past decade, online classes have become extensively utilized by higher
education. Recent literature found, when focusing on upper level courses and four-year
college students, that online classes are as effective as face-to-face classes in serving the
curricular needs of students. This study sought to enrich research by examining
community college students with a further study of student characteristics (age, gender,
basic skills, ethnicity, low-income) to see if they contributed to the students‟ grades in
each delivery method. A faculty survey was used to investigate other possible influences
on success. No significant differences were found for the pass/fail comparisons or in
faculty choices for communication with students. However, objective measures did show
that students achieved increasing numbers of A and F grades in online classes.
1
Chapter One
Introduction
Background
The Economic Problem and Higher Education
During times of economic slowdown, the educational sector faces increasing
budget cuts that impact its ability to perform effectively or to serve as many students as it
otherwise would. This is a current issue in today‟s society and one that becomes
exacerbated by the increasing numbers of unemployed people who return to college for
retraining (Wheeler, 2008).
The higher education sector is the site for preparation for the skilled workforce
that ensures that California has a competitive economy (Johnson, 2009; Moore, Shulock,
Ceja and Lang, 2007). It is projected by the Public Policy Institute of California that, at
current graduation rates versus demand, there will be a shortfall of college graduates of
almost a million by the year 2025 (Johnson, 2009). Additionally, it has been shown that
workers with only a high school diploma are “more than twice as likely to be
unemployed” than someone with a college education (Johnson, 2009. p.3).
The recent increase in unemployment numbers leads to a higher demand for
education institution enrollments and a higher load of students looking to get further
education or retraining. Some schools, such as State University of New York at
Binghamton, are showing as much as 50% higher applications in 2009 versus 2008
(Mangan, 2008, p.1) and other systems such as the Boston area public higher education
institutions have seen a “4% increase in student enrollments” (“Enrollments up,” 2008,
p.83).
2
This swell in enrollments is especially stressful due to higher education cutbacks
that are the result of declines in California property tax revenue (Mangan, 2008). These
reductions from the state are affecting each level of the public educational system. The
University of California (UC) and California State University (CSU) systems are both
closing their admissions process and cutting back standing enrollment numbers for
incoming students (Fisher, 2009; Steinberg, 2009). Each year, up to 25,000 of these
students are then forced into the already burdened California community college system
(Hayward, Jones, McGuinness and Timar, 2004). At United States community colleges,
this impact has amounted to increasing student populations during the period of 2001-
2006 by 2.2 million students (Bradley, 2008, p.6).
These economic and enrollment issues within higher education become even more
of a predicament with the recognition that community colleges enroll “nearly half of the
minority students that are in the higher educational system” (American Association of
Community Colleges, 2009; Wright, 2000, p.332). The Community college system has
become the access point for higher education attainment and entry for many minority
groups, including ethnic minorities, workforce development focused students, low-
income and basic skills students (Cavanagh, 2004; Dolan, 2005; Moore, Shulock, Ceja, &
Lang, 2007; Shulock & Moore, 2005). In California alone, the Los Angeles Community
College District enrolls “almost three times as many Latinos and nearly four times as
many African American students as all of the UC campuses combined” (American
Association of Community Colleges, 2009).
High numbers of full-time working adults - many with families - low-income
students, basic skills students and many English language learners are also attracted to the
3
education opportunities that they can engage in at the community colleges (Moore,
Shulock, Ceja, & Lang, 2007). Often these students are first generation Americans and
the first generation to attend college (Dougherty, 1994; Hoachlander, Sikora, & Horn,
2003; Moore, Shulock, Ceja, & Lang, 2007). For many students who have student
characteristics that can be considered risk factors, the community college system is their
only option for higher education due to cost, family responsibilities, and other issues.
Online Education as a Partial Solution to a Multiplicity of Demands
This diversity of demands facing the community colleges and their student
populations has led to increasing strains on facilities and increased demand for classes.
The solution for many institutions is to increasingly move their classes to alternative
methods of delivery (Allen & Seaman, 2008). In a traditional classroom, the faculty meet
with the students in person and at the same time. This is often described as face-to-face
(F2F) instruction. Examples include lecture-based classrooms and labs and tutorials
where the focus is on dialogue (Keegan, 1998).
Distance education incorporates a variety of formats that have been used and
developed over the last 150 years. The advent and changes in technology have allowed
alternative classroom situations for in-person F2F based traditional classrooms and
teaching (Keegan, 1998; Phipps, 1998). DE is the “separation of the teacher from the
learner and the learner from the learning group,” through using an “artificial medium” to
facilitate communication (Caywood & Duckett, 2003, p. 98; Cox, 2005; Keegan, 1998,
p.43). This is in contrast to a traditional face-to-face classroom that focuses the
experience on the physical presence of the faculty member and the student to proceed
4
with an interaction of “dialog, lecture, seminar or tutorial” formatted setting (Keegan,
1998, p. 43; Phipps, 1998).
DE entails instruction where innovations in teaching methods are by definition,
increasingly being used (Keegan, 1998). DE has been expanding and at the same time has
rapidly proliferated the variety of format options it employs. Today‟s Distance Education
classroom represents a variety of media format options (video, Internet sites, chat)
incorporated into one course management package that is offered via the Web and
provides multitude instructional pedagogical options. Additionally, these web classrooms
can be accessed from any place and at any time. This provides an option to the non-
traditional student for access to education, as well as to the traditional students who have
difficulties in enrolling in F2F classrooms (Keegan, 1998). Therefore, these online
classrooms have become a solution for colleges and are increasingly scheduled as the
demand is supported with online enrollments showing growth at “rates that exceed the
total higher education population” (Allen & Seaman, 2008, p.5; Biemiller, 2009). The
associate degree track (community college) students “account for over one-half of the
online enrollments” within the higher education system (Allen & Seaman, 2008, p.5).
Statement of the Problem
With the recessing economy, failing stock market and higher unemployment rates,
many families are making conscious decisions to send students to a community college
first and then have them transfer to the more expensive four-year institutions for the end
of their college careers (Mangan, 2008). Community colleges have been historically more
affordable to the general public, but as four-year institutions continue to raise tuitions,
they are increasingly the only option for students and their families (Price, 2003). One
5
solution used by many in higher education institutions is to raise tuition rates. However,
the California community colleges lack the ability to raise tuition. The tuition rates are set
by the state legislature, and per California legal requirements require a majority to agree
in a legislative session to any rate increases. Therefore, the option of increasing tuition
(even as a temporary fix) to make up the immediate fiscal deficits in this current fiscal
crisis, is not available as an option for the California community college system.
All these factors add up to the California community colleges enroll nearly three-
quarters of California‟s public undergraduates (Moore, Shulock, Ceja, & Lang, 2007).
The California community college system is one of the largest; in 2008 the system
enrollment totals accounted for 24% of the community college student enrollments for
the entire nation (Community College League of California, 2009). The system is thus
already burdened with high numbers of students, but when we add the students pushed
from four-year institutions, this creates a critical load problem for community colleges
that are already on the brink of a breaking point (Hayward, Jones, McGuinness, & Timar,
2004). The community colleges are then left with the mission to educate the continuing
students, the returning students, new (high school graduates) students as well as the
increasing number of unemployed (Holland, 2009).
Higher Education Curriculum Models
The quality of the content materials presented in higher education classrooms
(online or F2F) is a question that perplexes administrators. Some see the online
environment as opening possibilities to transform education with learner centered and
directed educational applications (Phipps, Wellman, & Merisotis, 1998). However,
Phipps, Wellman and Merisotis (1998) also suggest that the online environment poses a
6
whole new level of difficulty when looking to evaluate student learning, by separating the
learning of the subject matter from the learning going on in navigating and succeeding in
an a multi-media online based classroom environment. However, the learning about the
content matter and learning about the use of multi-media are not separate in distance
education classrooms. Others argue that higher education pedagogical and curricular
practices have not changed significantly, and that students are taught basically the same
set of information in essentially the same ways no matter the mode of the classroom
(Lucas, 2006).
Despite these researchable possibilities, the educational environment now looks
different. From a student perspective, a student can attend class from any location and on
his or her own time schedule. This allows increased access to higher education for a
wider audience than possible before when higher education was limited to only F2F
classrooms (Allen & Seaman, 2008; Phipps, Wellman, & Merisotis, 1998). From an
institutional perspective, online educational offerings allow the institution to access non-
traditional student populations. Higher education institutions are expanding their class
offerings and allocating increasing portions of their funds towards supporting the online
world (Allen & Seaman, 2007; Biemiller, 2009). In the 2000-2001 academic school year,
in a National Center for Education Statistics (NCES) (2003) survey, 56% of two-year and
four-year schools offered distance education classes. However, within just a few years the
online distance education course offerings had increased to 66% at four-year schools, but
the important difference is that at the two-year schools, offerings had increased to 97% of
those responding (NCES, 2008). This shows that two-year schools are at the forefront of
7
this evolution from exclusively offering F2F courses, to a mix of classroom methods that
relies heavily on online course offerings.
Purpose of the Study
Research Question
Are students‟ GPA and distribution of grades earned in online equivalent to those
in F2F classes? The research problem for this dissertation focuses on the differences in
learning, examined through final grade attainment for online students and traditional F2F
students. The focus will be on doing this comparison within a community college setting.
This is important due to diversity in ages, basic skills backgrounds, income and
educational backgrounds of students. This diversity begs the question of how effective
each classroom format (online DE versus F2F) is for students based on these built-in
“risk” factors. The issue of student self-selection into classes adds an additional risk
factor that this study does not add controls for. However, there is no reason to suspect
that the lack of random distribution would impact or diminish the basic research focus of
this dissertation negatively. Therefore, this is not studied as an additional risk factor or
controlled for within this dissertation.
Through examination of classroom formats and students‟ earned grades, this
study seeks to look at the significance of the movement to online instruction. If a
difference is found in grades attained, then further studies should look into this shift from
the traditional face-to-face environment to compare curriculum standards and locate “best
practices” for instruction to differing types of students within the online classroom. If no
significant differences are found, then the expansion to online learning environments is a
positive policy for community colleges, and one that accommodates the preferences of
8
many students, meets educational goals, and is compatible with the strategic plans of
administrators.
Significance of the Study
If the findings show that there is in fact no difference between the demographic
groups in an online DE versus a F2F class, then the potential is that the community
colleges are serving their students‟ educational needs appropriately by moving more and
more classes into the online DE format. This will support the higher education policy of
continuing to allocate funds to the online division and redistributing increasing numbers
of course sections out of the F2F format and into the online DE format.
Alternatively, if there is a difference in achievement by class method of delivery,
then additional research will need to occur to look for other factors that affect success and
differences. These should include curriculum and pedagogical issues, as well as student
and faculty preparation for the online environment. This further research would help in
developing policy statements for the community colleges to facilitate guidance with best
practices and help with strategic planning for student success.
Overview of the Dissertation Sections
This document will follow a sort of storyline to lead the reader through the
research process I underwent in order to answer the stated research question. In chapter
two, the literature review section, a discussion occurs concerning the growth of distance
education, which includes online education as the latest and a particularly effective
version of distance education. There is also a review of the literature when it is presenting
comparisons of the online versus face-to-face classrooms. Finally this chapter examines
research for a current look at student characteristics and how these could be seen as a risk
9
factor toward their success in the higher education environment, with a particular focus
on the community college system.
In chapter three, the methodology section, the data collection and analysis process
is outlined. Here I delineate the measures used for collecting the quantitative and the
qualitative data, and also whom the participants for the study are. Limits and ethics for
this study are additional parts of this chapter. The findings and discussion section, chapter
four, details the results of the data analysis process. Comparisons between the online and
face-to-face classes overall and by student characteristics are outlined within this chapter.
Finally this is also where the results of the faculty survey are discussed.
Finally, in chapter five, the conclusion, there is another summary of the findings
of the statistical tests and the survey followed by a discussion of implications for practice
and final concluding remarks. Within this chapter is also a discussion of the risk factors
and the limits that were present for this study.

10
Chapter Two
Literature Review
This dissertation studied face-to-face (F2F) education and online distance
education classrooms. Distance education, and especially online distance education, has
expanded tremendously in the numbers of classes offered and student enrollments within
the past decade. For some students this innovation may represent a disruption of their
success within classrooms, as the online distance classroom may be less effective for
them. The literature will be explored that discusses evaluation of achievement (through
grades) and students‟ satisfaction within the traditional F2F classroom versus the online
distance education classroom. Finally, this dissertation referenced literature that discusses
the community colleges, their unique student populations, and the risk factors associated
with this population that might interfere with success in higher education classrooms.
Distance Education in Higher Education
Distance education (DE) has evolved from first uses, such as through
correspondence style courses in the early 1900‟s, to increasingly acceptable and
mainstream uses, which utilize technological innovations within university and colleges
today (Keegan, 1998). DE can become even more complex with ever increasing
technology innovations and can incorporate a variety of formats. These include basic DE
classes utilizing a single medium or media (for example a live-streaming session, where
students and the instructor communication via a live Internet based connection). These
classes are more complex and incorporate multiple technologies and media to engage the
learner in the classroom experience. They can include: video, live-streaming, online chat,
11
whiteboards, Internet sites, CD‟s or tapes, and more (Burbales & Callister, 2000;
Caywood & Duckett, 2003; Keegan, 1998; Phipps, 1998; Tallent-Runnels et al., 2006).
Each of these technologies can be classified as synchronous or asynchronous, and
courses can incorporate either or both. Students participating in asynchronous
communication patterns are communicating at different times with each other and their
instructor (Bernard, et al, 2004; Heckman & Annabi, 2006; Keegan, 1998; Phipps, 1998).
However, with synchronous communication, students and faculty are in communication
with each other at the same point in time (Bernard et al., 2004; Keegan, 1998; Phipps,
1998). Each form or medium of DE, based on approach, utilizes these communication
patterns in more or less effective ways. For example, email has seen large adoption
patterns by many college students and faculty as a primary means of asynchronous
communication both in DE and F2F classes (Heckman & Annabi, 2006; Phipps, 1998).
The increased developments and innovations in Internet technologies and learning
course management systems software programs have greatly fostered the expansion of
virtual or online DE classrooms (Keegan, 1998). These classrooms increasingly are adept
at using both asynchronous and synchronous communication in a user-friendly manner
(Tallent-Runnels et al., 2006). According to the National Center for Educational Statistics
(NCES) (2008) in their 2006-2007 survey, approximately 77% of the DE courses were in
the online format, and only 10% were listed as “other format” (this could be televised,
video, or other non-online formats), thus showing that higher education is focusing its DE
classrooms in the online method of delivery. Over the last two decades there has been a
high level of growth in DE online course offerings and enrollment rose 20% from 2001 to
2007 (Allen & Seaman, 2008; Mayadas, Bourne, & Bacsich, 2009). Some reports
12
mention that the growth has been as much as 12% in the numbers of student enrollment
from 2006 to 2007 (Allen & Seaman, 2008). Still others mention, in a study looking at
online enrollment growth in 2002, that growth was approximately 33% per year (Tallent-
Runnels et al., 2006, p. 2). These statistics suggest students are increasingly choosing
online options in their enrollment choices when they attend higher education institutions.
With this sort of expansion, online distance education (DE) has become a major
component of higher education institutions‟ course format offerings, with some estimates
setting online students as 20-25% of all students at U.S. colleges (Allen & Seaman,
2008). According to the Department of Education and NCES (2003) in a 1998-1999
survey, out of 1,487 institutions that responded 753 offered some sort of distance
education courses. This shows that approximately 50% of higher education institutions
were offering DE courses. However, in a later NCES (2008) 2006-2007 survey 4,200
institutions responded and 66% reported offering DE courses. This shows that higher
education institutions have expanded these offerings, with a growth of 16% within the six
years between the data reports. This accounts for an estimated “12.2 million enrollments”
due to DE courses (National Center for Education Statistics, 2008).
Online Education as Relatively Effective in Higher Education
Many in the higher education community see Online DE as a “transformative
change” in the way education is processed (Phipps, 1998). Online based DE is held up to
be a way to change the pedagogy of education in such a way that the process of educating
students changes from a faculty directed form of education to a student directed model
(Keegan, 1998; Phipps, 1998; Tallent-Runnels et al., 2006).
13
Tallent-Runnels et al. (2006) point to the fact that online classrooms are not the
same as F2F, in their methods of curriculum format and delivery, but that on average they
are equally as successful. In many classrooms students have been shown to gain
knowledge of the curriculum in a different manner (Russell, 1999, 2009; Tallent-Runnels
et al., 2006; Thomas, 2008). Through the use of multiple methods of delivery, online
learning (also referred to as e-learning) adapts and is interactive in such ways that when
done correctly, it can adjust to the student‟s needs and make each lesson or skill set
tailored to the students‟ capability level and learning styles (Heckman & Annabi, 2006;
Phipps, 1998; Thomas, 2008). On the other hand, other research found that many online
classrooms simply transcribe the F2F course into an Internet version, and do not develop
or use many of the available enhancement tools (Cox, 2005).
E-learning and online DE is seen by some as a disruptive innovation and one that
has not lived up to its promises of transformation (Christensen, Horn, & Johnson, 2008;
Zemsky & Massy, 2004). Disruptive innovation can be defined for the purposes of this
discussion as a failure of online students to achieve the pedagogical revolutionary
promises. Additionally, online education when looked at as a disruptive innovation is
essentially a difference in style of classroom or distribution source much like first
generation correspondence DE courses (Zemsky & Massy, 2004). Online education is
also classified as disruptive, in that it can be seen as disrupting the current system‟s
development and innovation possibilities because of the singular concentration on the
less pedagogically complex online market (Christensen, Horn, & Johnson, 2008).
Therefore, online education when classified as a disruptive innovation, is seen as being
offered in a reduced time frame than the currently normalized semester or quarter format,
14
in what is seen as more simplistic curriculum and once programmed into the educational
software, taught by less qualified faculty. Further, online DE has interrupted the
allocation of the normalized state or local government based fiscal streams, because
many higher education institutions see online education as a non-choice and feel pressure
to offer classes in this format (despite not being fully prepared for implementation) due to
demand and limits with institutional funding (Cox, 2005). Overall, online specific DE has
many promises, as mentioned above, but is still relatively new and thus it lends itself to
large amounts of speculation, optimism and criticism.
The online technologies discussed above that are utilized in online classes allow
for a record of pedagogy. However, in the F2F classes there is no record that occurs in
that same fashion. Thus the research into online technologies focuses quite a bit on the
individual methods and how they are successfully or unsuccessfully implemented. In the
F2F classroom there is tremendous, arguably even greater, variability in teaching
behaviors, most of which have not been measured or accounted for in the research. My
study is similar.
DE has been described as the ability to “democratize higher learning” by allowing
campuses to reach more students who would not otherwise have been able to have access
to post-secondary education (Burbules & Callister, 2000). There has been a tremendous
boost in the non-traditional student enrollments and DE is seen as allowing students to
register and attend higher education within the constraints of their schedules, thus adding
access to the value of a higher education degree (Caywood & Duckett, 2003; Cox, 2005;
Hirschheim, 2005; Keegan, 1998; Phipps, 1998; Tallent-Runnels et al., 2006; Zemsky &
15
Massy, 2004). This democracy, increased value, and access could be due to limitations in
geography, economy or schedules (Burbules & Callister, 2000).
Faculty Communication, Interpersonal Relationships and Student Success
Faculty communication with students and interpersonal relationships, also have an
impact on students success. There have been many studies that look into interactions as a
critical factor in student success and motivation to succeed. A few of these researchers
(Astin, 1993; Chickering, 1969; Tinto, 1993) have published landmark studies that
informed many further studies on the intricacies of interpersonal relationships and
students‟ success in college. For instance, Komarraju, Musulkin and Bhattacharya (2010)
wrote up their research on these interactions as affecting students in a positive way
towards self-concept and productive achievement. They mention that these relationships,
though most defined in research as occurring in formal classroom settings, can encourage
academic learning and student‟s intellectual pursuits outside of the classroom (Cox &
Orehovec, 2007; Komarraju, Musulkin, & Bhattacharya, 2010).
These faculty interactions that occur outside of class, in the form of feedback and
support, can encourage a student‟s satisfaction and learning while establishing a mentor
or role-model type relationship, thus especially promoting confidence for Hispanic and
Asian/ Pacific Islander students (Fusani, 1994; Komarraju, Musulkin, & Bhattacharya,
2010). Current technologies, such as email, can also be utilized for peer-to-peer
communication that promotes college integration and development of positive
interpersonal relationships that further academic success. Research has found that email
is successfully utilized by first-year college students (peer-to-peer) to promote
communication and relationships, both with current college cohorts and high school
16
friends (Gatz & Hirt, 2000). This research shows that current uses of technologies can be
applied to online DE classrooms, via learning systems, and with proper promotion and
classroom requirements, can be utilized successfully for student success through
communication patterns that promote interpersonal relationships between faculty and
students.
Methodological Questions of Evaluation
One problem with the utilization of an online format as a preferred method of
delivery for DE is that much of the research is in the beginning stages of evaluation
(Tallent-Runnels et al., 2006). There are many methodological questions, such as design
and pedagogical methods, that face the researcher when evaluating an online DE
classroom versus the traditional F2F classroom (Tallent-Runnels et al., 2006). When you
combine the various formats (for example discussion boards, posted videos, drop-boxes,
etc), communication (synchronous and asynchronous) and the subject matter at hand in
the DE classroom, this makes defining and evaluating a DE course a far more complex
process than when evaluating a traditional single method F2F course. However, when
evaluating the single aspect of student learning, grades are one of the effectively
formalized and widely recognized as a record of performance standards that exist within
the higher education system (Black & William, 1998; Bloom, 1997; Willingham, Pollack,
& Lewis, 2002).
Evaluation of higher education classrooms is centered on the learning outcomes
that students are expected to accomplish and a teacher‟s subjective review of the
student‟s grasp of this knowledge set through a final grade (Alkin, 1973/1974; Cowan,
George, & Pinheiro-Torres, 2004; Melton, 1996; Willingham, Pollack, & Lewis, 2002).
17
By measuring a student‟s achievement through a formalized defined outcome and a
standardized measure, higher education institutions are able to assess both the student‟s
grasp of the curriculum as well as the effectiveness of the instruction in imparting the
subject knowledge models (Melton, 1996). Curriculum is the means for structuring a
class in terms of the actual content, delivery, and facilitation of a student‟s grasp of the
knowledge presented (Alkin, 1973).
Grades have been the adopted benchmark for many colleges for facilitating the
measurement standards and quality control within a classroom for student learning
(Caywood & Duckett, 2003; Nagy, 2000; Western Association of Schools and Colleges
(WASC), 2009). This does not mean that the grades are used for evaluating the instructor
or his or her instructional techniques; rather, they are used for considering individual
students and the information about the subject matter at hand that they have absorbed
(Cowan, George, &Pinheiro-Torres, 2004; Willingham, Pollack & Lewis, 2002). This is a
unifying concept for both F2F and online DE classrooms.
The Western Association of Schools and Colleges (WASC) requires all classes to
“have integrity and be organized around substantive and coherent curricula which define
expected learning outcomes” (Caywood & Duckett, 2003, p. 99). These learning
outcomes and formative competencies, assessable through a normalized performance
measure (grades) are one aspect unifying the curricular aspects of the F2F and online DE
classrooms (Black & William, 1998; Willingham, Pollack, & Lewis, 2002). Therefore,
even if the methods of instruction for the two forms of classes are very different, the
content and evaluation measures are standardized. Additionally many colleges have one
18
outline of record for the course, not separate, different outlines for the F2F and the online
DE version.
Online learning has been effective in multiple locations at improving students‟
learning by aligning best practices for instructional support with curriculum
standards(Cowan, George, & Pinheiro-Torres, 2004). Within online classrooms, this
translates into allowing for alternative instructional pedagogies to achieve learning
outcomes (Tallent-Runnels et al., 2006). Online classrooms can represent the best of both
the instructional components and the curriculum components by aligning expectations of
learners to the expectations of the student learning outcomes (Tallent-Runnels et al.,
2006). It is essential, however, that research on online DE classroom assessments be
examined in a culturally sensitive manner in which a student‟s background, institutional
data and assessments are all examined equally together (Karlberg, 2008) in order to make
a situationally appropriate and possibly generalizable evaluation. Therefore a comparison
of the students and their individual student characteristics must be a factor when
conducting an examination of a classroom (F2F or online DE).
Comparison of Online versus Face-to-Face Classes
With the complexity of online DE classrooms, evaluation comparisons with F2F
classrooms are also complex. In light of this difficulty, many researchers choose to take
and examine only specific portions of the student experience, or the classroom
instructional media utilized, when evaluating differences between these class formats.
Many studies use a mixed methods approach for evaluation, examining primarily the
quantitative student outcomes and then looking into a qualitative assessment of
satisfaction. Overall meta-analyses have found that there was no significant difference
19
between the two classrooms (F2F versus online DE) in students‟ achievement (Merisotis
& Phipps, 1999; Russell, 1999, 2009; Tallent-Runnels et al., 2006). That is to say that
there is no variation between grades and achievement of outcomes in the online class
versus the F2F classroom (Merisotis & Phipps, 1999; Russell, 1999, 2009; Tallent-
Runnels et al., 2006).
Meta-analytic research has looked at the difference in achievement for online DE
courses and traditional F2F courses. For example Tallent-Runnels et al. (2006) did a
review of the literature and found that the studies they examined revealed no difference
between the achievement of an online student and a F2F student as measured by a mix of
final course grades and achievement on individual course projects or exams. Russell
(1999, 2009) also found this to be true when he performed a very similar meta-analysis of
studies that focus on a comparison of online versus F2F classrooms. Both of these studies
evaluate the effectiveness of online classrooms as compared to F2F classrooms and the
results overall support that there is no significant difference between the two classroom
formats, based on students‟ grades and achievement. However, within both of these meta-
analytic studies, many of the articles cited do not have a high level of methodological
rigor. Due to the description of specialized populations, the results were not clearly
applicable to policy decisions and application for the generalized higher education
student population. Additionally, using both of these meta-analyses (Tallent-Runnels et
al., 2006; Russell, 1999, 2009) for policy application applying to undergraduate higher
education student populations is faulty, because of the limits in the research and due to
the extreme specificity of the individual groups studied (for example: a graduate literature
class). Both these studies (Tallent-Runnels et al., 2006; Russell, 1999, 2009) represent
20
just a portion of the research conversation on online course effectiveness and serve as a
stepping stone for future research.
Further studies looked at student achievement through grades in classes with
identical textbooks, materials and instructors for undergraduate four-year students. This
research found no statistical difference in the distribution of course grades by method of
delivery (Bata-Jones & Avery, 2004; Dellana, Collins, & West, 2000; Jennings &
Bayless, 2003; Leasure, Davis, & Thievon, 2000) or mastery of content on a pretest –
post-test design (Maki, Maki, Patterson, & Whittaker, 2000). This shows that the method
of delivery does not affect the grades and achievement of students, and they achieve
equally in both formats. When materials and course structure remained identical but the
teachers for the F2F and online sections differed, Caywood and Duckett (2003) found no
difference in learning distribution of grades on a multiple-choice final exam. This allows
for the conclusion that the instructor does not affect the students‟ grades.
Some studies, however, found significant differences between online and F2F
classrooms. For example, Keefe (2003) studied at an undergraduate business course
online and in the F2F format, and found that students attained lower grades in the online
section as compared to the F2F section. The author suggests that for this particular
specialized topic, the F2F setting had benefits that the online DE students did not receive.
Mentzer, Cryan and Teclehaimanot (2007) found that students did significantly better in
the F2F class versus an online version, but in their study this may be due to the lower rate
of assignments being submitted in the online section. These two studies represent a few
examples that suggest a counter to the meta-analytic studies and the majority of the
21
literature, by suggesting that F2F may be more effective for student learning when
looking at final course grades.
Examining specifically the literature on graduate level course comparisons
offered online and in the F2F format, there was no statistical difference found between
classroom type as measured through students‟ overall grades received on the multiple-
choice assessment tests (Yucha & Princen, 2000; Woo & Kimmick, 2000). Mikulecky
(1998) did find that in a graduate class in which the classes online and F2F were
structured identically for assessment and use of discussions, when examining the
discussions there were differences in use and outcomes of this method of curricular
delivery by format. In the online class discussions, students gave longer and slightly more
articulate responses. On the other hand the F2F class discussions exhibited a higher
degree of linking materials and outside information (Mikulecky, 1998).
One study made a comparison baseline for an online blended course (that offers a
portion of the class in the F2F format and other components of the class online) and a
traditionally F2F formatted class. Again, there was no difference found between the
achievement levels of the graduate students as measured by responses on a multi-question
learning skills inventory (Parkinson, Greene, Kim, & Marioni, 2003). However,
Parkinson, Greene, Kim and Marioni (2003) did find that students felt more satisfaction
from the F2F class. Their satisfaction could be due to the instant feedback they received
from the instructor and fellow students during discussions that expanded their grasp of
the subject and its contents.
Additional research elaborated on the complexity of a hybrid version of the DE
classroom, in which there is a mix of the online DE course site‟s components and a
22
retention of the F2F meetings (although more limited in time and frequency). The
research focused on examining undergraduate F2F classes, online blended and online
sections of the same courses. Lim, Kim, Chen and Ryder (2008) found that in this
situation, students in the online and blended courses had higher achievement in the F2F
courses between a pretest and post-test designed for content achievement evaluation.
They also found that there was no significant difference in satisfaction between the three
formats of the course (Lim, Kim, Chen, & Ryder, 2008). This could be due to the fact
that students self-selected through their registration preferences the class of their own
choosing, thus increasing satisfaction through ownership.
Faux and Black-Hughes (2000) found no significant differences between these
three formats in scores for the pretest using a pre/post design. On the post-test the F2F
students did attain at a significantly higher score (Faux & Black-Hughes, 2000). Brown
and Liedholm (2002) further found no difference on a pretest/ post-test design, but took
the evaluation further by looking into the idea of levels of complexity for class content
topics. They found that as the topics became more complex, the achievement rate in the
F2F classroom was significantly higher than in the blended or online classrooms (Brown
& Liedholm, 2002). Buckley (2003) found no differences in achievement in a nursing
class, based on a comparison of final course grades and achievement on exams, for the
same class taught in a traditional F2F format, in a web enhanced course and in a
completely online format. However, Buckley‟s study (2003) does match the findings in
Faux and Black-Hughes (2000) where the students‟ perceptions of the effectiveness of
the class for learning were low for the web-based class and at the medium range for the
traditional class, thus showing students‟ preference for the F2F class format.
23
In each of these studies, higher level (graduate) or specialized (nursing) students
achieved final course grades at equal rates, despite the difference in format for the class.
Students did have differences in perceptions, ultimately preferring the F2F, but only by a
very slight margin. An interesting finding when looking at the group of studies is that
students preferred the instruction format of the online class, but enjoyed the interaction
with their peers and instructor better in a F2F format (Buckley, 2003; Faux & Black-
Hughes, 2000; Parkinson, Greene, Kim, & Marioni, 2003). This could be because of the
instantaneous feedback occurring during class discussions as well as the group dynamics
being used to answer knowledge questions posed in the discussion formats.
The complexity of online classes leads some of the research to concentrate on the
effectiveness of singular parts or pieces of the online classroom. Heckman and Annabi
(2006) focused solely on the discussion platforms of online courses and looked into the
collaborative learning potentials of discussions. Overall they found that “asynchronous
learning networks” were as effective in the online format as in a F2F course (Heckman &
Annabi, 2006, p.2).
In some studies, only the faculty attitudes toward online education were
evaluated. Shieh (2009) summarizes the National Association of State Universities and
Land Grant Colleges survey whichfound that a majority of faculty feel that, when looking
at learning outcomes, online classrooms as a whole are “less effective.” When faculty
who are teaching in this format were asked the same question, these negative views
showed a marked decline (Shieh, 2009). Much of the negative perceptions about online
education and its failures may in fact be due to pure gossip (or urban legends) rather than
be based in the experiences of faculty who are teaching these classes.
24
Additional research, for example Ridley and Husband (1998), examined the
possibility of increased cheating potential in an online classroom. The study incorporated
the random assignment of undergraduate students into classrooms. The researchers were
focused on observing performance in an online classroom with the added concentration
of studying cheating level comparisons (ease of and numbers of incidences) in an online
classroom. Their findings show that, in fact, academic integrity is intact in these online
classrooms based on grades data comparisons (Ridley & Husband, 1998). One additional
finding, which they were not expecting, was that based on grade distribution data, many
online students achieved lower grades than the “offline students” (Ridley & Husband,
1998). Their data is important, especially since the researchers examined over 100
undergraduate classes. This finding is in direct conflict with the “no significant
difference” findings of much of the research in online DE course evaluation.
The majority of the existing literature does in fact concentrate on a student‟s
satisfaction of online DE classes. The majority of these found that satisfaction rates for
the online DE courses were higher than for the F2F classes (Buckley, 2003; Hirschheim,
2005; Lim, Kim, Chen, & Ryder, 2008; Maki, Maki, Patterson, & Whittaker, 2000;
Mentzer, Cryan, & Teclehaimonot, 2007; Merisotis & Phipps, 1999; Mikulecky, 1998;
Woo & Kimmick, 2000; Yucha & Princen, 2000; Yudko, Hirokawa, & Chi, 2008).
However, “satisfaction” does not equal “effectiveness” as seen from the comparisons
mentioned previously. Grades must be separated from other student perception measures.
In order to judge the effectiveness of the online DE classroom, research needs to be
accomplished that looks into the learning outcomes and final grade comparisons of
multiple levels of students, for both methods of delivery. This is especially relevant
25
because of the number of general education requirement classes that are being moved
from the F2F format into the online DE format. Even though students may be more
“satisfied” with the online classroom, the question that still remains, despite the level of
research and amount of literature discussing the online classes: is the “no significant
difference” judgment applicable, and are students in fact still achieving in these online
classes at acceptable rates?
Community Colleges and Student Characteristics
Despite the discussion on online classrooms versus F2F classrooms for student
achievement, there has been little examination of the actual students who are taking each
version of these classes. One very important question is, “which version is better for
whom?” (Burbules & Callister, 2000, p.276). Issues of cultural differences, social
economic status (SES) differences or age of students are often not addressed (Karlberg,
2008). There are many student characteristics that have the potential to impact motivation
for success.
Much of the research discusses gender and or age as a variable or as a means for
sorting students into categories within their studies. In a NCES longitudinal study
describing characteristics and enrollment patterns several student characteristics are
mentioned (Berkner & Choy, 2008). The study discusses student age groups, financial
status, academic preparation (especially math) and their enrollment patterns at both two-
year and four-year institutions. Overall age was a central variable in this study for
structuring the discussion of student success within higher education. One significant
finding is that independent students (who it mentions, also tend to be older) have higher
completion rates of degrees (Berkner & Choy, 2008). Other research has focused on
26
gender as a predictor for academic success. In a study by Naderi, Abdullah, Hamid and
Sharir (2008), the researchers fail to show intelligence and gender play a role in GPA
scores in a multiple regression analysis. Their sample is small (N=153) and focused
primarily on Iranian students. The study does, however, serve as an example of much of
the research that utilizes gender as one of the main characteristics for categorization, even
when it may not result in significant differences as a variable for study.”
These student characteristics are often more prevalent in the community college
student populations than anywhere else in the higher educational system. However,
further specialized research into the community college populations in online DE classes
is limited (Cox, 2005). Community college students represent a sizeable group in the
online DE cohort; in a recent report Allen and Seaman (2008) found that while the
community college population was only 37% of the higher education system, the
community college students accounted for 50% of the online enrollments (p. 6).
The community college system‟s mission is to provide access for a wide range of
students to low cost postsecondary educational options (Cox, 2005; Dougherty, 1994;
Douglass, 2000; Wright, 2000). Community colleges can provide a gateway for students
of color and of a low socioeconomic status (SES), to the higher education system and a
bachelor‟s degree (Chavez, 2008; Dougherty, 1994; Moore, Shulock, Ceja, & Lang,
2007). The community college policies of equity through open admissions and low
tuition cost have thus made them a popular choice for non-traditional students that
experience one or more of these student characteristics (versus the demographic
characteristics of traditional higher education students that tend to enroll in four-year
institutions) (Bailey & Morest, 2006; Chavez, 2008; Hoachlander, Sikora & Horn, 2003;
27
Moore, Shulock, Ceja, & Lang, 2007). Further, community colleges are sometimes the
only choice for minorities and low SES students, and because of this, community college
students represent, as a cohort, a population with a large proportion of risk factors
(Dougherty, 1994; Hoachlander, Sikora& Horn, 2003; Moore, Shulock, Ceja, & Lang,
2007).
The community colleges or two-year colleges, serve approximately 50% of the
nation‟s higher education minority populations, the rest being at four-year, for profit, and
other higher education institutions (Olsen, 2003; Wright, 2000). In 2006-2007 the
Government Accounting Office listed minority student populations from all higher
education institutions and noted that many are highly represented within the community
colleges with numbers such as: 60% Hispanic, 50% Asian/ Pacific Islander, Alaskan
Native, and Black, and 43% white or non-Hispanic (Melguizo, 2009; Melguizo,
Hagedorn & Cypers, 2008). However, in California alone the community college system
serves one of the largest minority populations for any state, with the 2007-2008
headcount numbers totaling about 7.5% African-American, 34% White Non-Hispanic,
17% American Indian/ Alaskan Native, Asian, Filipino or Pacific Islander, and 30%
Hispanic (California Community Colleges Chancellor‟s Office, 2008). This means that
approximately 55% of those that stated their ethnicity are minority students (California
Community Colleges Chancellor‟s Office, 2008). In the not too distant future these
numbers are projected to increase; within California Latino students alone are estimated
to be as much as 48% of the 18-24 age category, typically the college aged group, by the
year 2015 (Chavez, 2008).
28
For groups such as Latinos, the community colleges are the conduit for entry into
higher education and the four-year schools (Chavez, 2008; Melguizo, Hagedorn &
Cypers, 2008). In the California community colleges during the 2008-2009 academic
year, Latino students accounted for 30% of the total student enrollments statewide, which
is comparable to the White student enrollments of 34% (Community College League of
California, 2010). In the UC system Chicano and Latino enrollments accounted for only
20% of the total student enrollments for the 2008 academic year (University of
California, 2010). Therefore, Latinos are highly represented in the two-year system and
account for a significant number of the enrollments at community colleges within
California.
Latino student populations have many barriers to educational success, such as
educational preparedness (basic skills) and low incomes (Santos & Melguizo, 2008).
Studies have mentioned that as many as “62% of Latino children in California come from
low income families” (Chavez, 2008, p. 2). This presents a risk factor because of the
expense of a secondary education, from the books to course fees to time costs of
attending school and not working to provide income for the family. Additionally, Latino
students at community colleges account for a higher number of basic skills level students
than are present in the UC and CSU systems (Melguizo, Hagedorn & Cypers, 2008;
Sengupta & Jepsen, 2006). These students will have to do more catch up work to be
prepared to succeed at the four-year colleges and universities. This means additional
years of college and expense for families that are already struggling to make ends meet.
The community college high ethnic minority population enrollments are not just
limited to Latinos; many other groups have higher numbers within the community college
29
system versus the four-year colleges and universities. The diverse numbers of ethnicities
are reflected in the overall totals of the student enrollment, with people of color
composing over 50% (Woodlief, Thomas, Orozco, & Dowell, 2003). African-Americans
account for over 7% of the enrollments in the California community college system
(Community College League of California, 2010; Santos & Melguizo, 2008).
Additionalminority enrollments within the California community college system are:
Asian and Filipino students 15%, and American Indians, Alaska Natives and Pacific
Islanders 1.6% (Community College League of California, 2010). To contrast this, within
the UC system the minority enrollment percentages are much lower. African-Americans
are 4%, Chicano and Latino students are 20%, and American Indian students are .6% of
the total student enrollment during the 2008 academic year (University of California,
2010). However, Asian and Filipino student enrollments show higher enrollment numbers
within the UC system (37%) (University of California, 2010).
Much of the research discussing the student characteristics of community college
students mentions that many of these community college students are first generation
college students and children of immigrants (Carnevale & Fry, 2002; Hoachlander,
Sikora, & Horn, 2003; Moore, Shulock, Ceja, & Lang, 2007; Olsen 2003). The National
Urban Institute study found that when looking into the well being of immigrant families,
one in five children within the United States was the child of an immigrant (Reardon-
Anderson, Capps & Fix, 2002). Within the last ten years in California, the increase in
immigrant populations has been on the rise to the point that “one-quarter of current
Californians, 9.6 million adults and children, were born outside of the United States”
(Bunch, 2008, p.2). Immigrant student populations are important to include in a
30
discussion of student characteristics because of the high risk for success within the
educational system due to a multiplicity of issues, for instance acculturation and coming
from homes where a language other than English is spoken (Bunch, 2008; Dolan, 2005).
Furthermore, many of these students have a lack of observational learning and modeling
as to what to expect in the college setting stemming from a lack of familial networks and
are thus unaware of ways in which to seek assistance (Bensimon, 2007; Santos
&Melguizo, 2008; Stanton-Salazar, 1997). Finally, the greatest number of immigrant
students in California come from Mexico, are younger than other immigrant populations
and come with basic skills remediation issues (Conway, 2009).
The community college system serves the largest number of basic skills (BS),
English as a second language (ESL), and remedial college students (Bunch, 2008; Dolan,
2005; Dougherty, 1994; Hoachlander, Sikora, & Horn, 2003; Moore, Shulock, Ceja, &
Lang, 2007; Olsen 2003). The Chancellor‟s Office of the California Community Colleges
has said that 20.1% of all students have attempted a basic skills course (Perry, 2002).
Community colleges deal with the fact that many of their students are at basic skills level,
thus needing additional help with their educational experience in order to “catch up” to
four-year college and university academic curriculum demands (Moore, Shulock, Ceja, &
Lang, 2007). Additionally, Spann (2000) mentions that between 30-90% of community
college students are in need of some sort of remediation or extra academic help (p.2).
Finally community colleges tend to enroll students with lower socioeconomic
status (SES) who are attracted by the lower tuition costs. Within the California
community college system in the 2007-2008 academic year, over 420,000 students
qualified for the Board of Governors (BOG) waiver based on income qualifications and
31
over 300,000 students qualified based on financial need (California Community Colleges
Chancellor‟s Office, 2009). BOG waiver criteria is for students that are below the
“federal poverty guidelines” with the 2009-2010 (see Appendix B) criteria being that a
single person household earning less than “$15,000” a year and a 5 member household
earning less than “$37,200” a year can qualify (California Community Colleges
Chancellor‟s Office, 2009).
These income student characteristics are particularly detrimental to success to
low-income California residents, where the cost of living is high in many areas, thus
providing a distraction towards attaining a higher education degree. The well-being
studies of children in low income families has shown that these children may be more
likely to get less education, live in poverty, have little to no medical care and become
single parents, adding one more complication and risk factor to their success within the
schooling system (Mayer, 1999). Accordingly, many of these students can only attend
part-time, due to demands from their families or because they are employed either part-
time or full-time (Hoachlander, Sikora, & Horn, 2003; Johnson & Sengupta, 2009; Kane
& Rouse, 1999; Olsen, 2003; Supiano, 2008; Wright, 2000).
Employment (full-time and/or part-time) can be thought of as a risk factor for
success, and some community college students work full-time while also attending
classes (Hoachlander, Sikora, & Horn, 2003; Kane & Rouse, 1999). Studies note that as
many as two-thirds of the community college students are working at least part-time
(Hoachlander, Sikora, & Horn, 2003; Kane & Rouse, 1999). While “84% of community
college students work versus 78% at four-year institutions” is not that much of a
percentage difference, this number is much more significant when related to findings that
32
“over half of the community colleges students report that work is their primary endeavor
versus only one quarter at the four-year institutions” (Kane & Rouse, 1999, p. 66-67).
This points to relationships of low completion numbers to student employment at
community colleges, and a risk factor that suggests that for these students, work often
takes precedence over school and impacts their success rates (Hoachlander, Sikora, &
Horn, 2003). Therefore low-income status and the need to be employed while a student,
is another important portion of the influences of income variables as a risk factor that
could affect students‟ success within the higher education system.
Research Question
Community colleges have a large number of students with risk factors, such as ethnic
minority status, low socioeconomic status, and remedial or basic skills educational needs.
Cox (2005) goes on to further argue that these students are thought of as especially “hard
to educate” and are thus often ignored in research (p.1756). The student characteristics
listed above all impact the success of a student in a classroom and can add one more
dynamic to overcome when trying to succeed in the online environment. The policy
implications are that the movement of classes into the online DE format may have the
unintended consequence of reducing the success rates of students within the community
college system. If there is a difference in grade achievement then this is an important
finding for college administration, accreditation institutions, a student‟s success and
transfer discussions. This dissertation will not focus on the choices of institutions in
shifting their courses into an online format but focus on students and their earned grades.
The purpose is to compare effectiveness of two types of instructional methods via the
crude, but widely accepted measure of earned grades. Therefore, the research question
33
that this dissertation addresses is: are students in the California community colleges
achieving grades in the online DE classes in roughly equivalent proportions to those in
F2F classes? Additionally, within community colleges, students have a higher level of
student characteristics that can be considered risk factors, which could impact grades, so
this study utilizes student characteristics as an additional method of comparison of the
two methods of instruction to help explain any differences in achievement.

34
Chapter Three
Methodology
Research Question
Current educational practices in community colleges include the increasing
movement of traditional face-to-face (F2F) classrooms into an online DE environment.
Essentially, there are too many students to serve and not enough classrooms or money to
serve them all in the traditional F2F format. A report by Allen and Seaman (2008)
mentions that online enrolments grew by 12.0 percent from 2006 to 2007 (p.1). In the
community colleges, where there are a high number of students with risk factors (for
example ESL and BS students), does this shift to the online DE environment work as
effectively as the traditional F2F classroom. This study examined students‟ learning as
demonstrated by final class grades, comparing an online DE classroom and a traditional
F2F classroom to see if students are achieving at the same GPA and distribution of grade
rates for each method of delivery. Class grades will be used as a measurement due to this
being a commonly accepted standard for assessment and measurement, and GPA
accepted within the higher education system as the transfer standard towards an effective
means of evaluating effectiveness of each type of classroom for student learning (Bloom,
1997; Williamham, Pollack & Lewis, 2002; Yorke, 2003).
This study sought to quantify the difference in attainment in an online DE versus
traditional F2F classroom primarily by examining student characteristics. The hope was
to have a historical look at classes taught both online only (not in the hybrid form) and
F2F over the period of five years. The budget cut woes and reality of these cuts changed
this study, and the archive data set gathered had to be shrunken down from a five year
35
time period, to a study of the academic years (fall and spring semesters) 2007-2008 and
2008-2009. The starting date of fall 2007 was chosen owing to a campus decision that at
that point every class online or F2F had a Blackboard shell to use as an instructional tool
(previously only online classes had access to Blackboard).
The study includes courses across disciplines, for example: math, English,
business, social studies, learning skills and computer science. For each class examined,
the goal was to look at the summative results of student learning (grades) and compare
the attainment of these predictor variables for each type or style of classroom setting (F2F
versus online DE) according to student characteristics. The focus is on the community
college student in California. Community college students are ones who are newly
exposed to both the college F2F and the college online DE classroom. Additionally,
community colleges offer the unique ability to examine attainment for students with a
very diverse set of characteristics (age, gender, etc) and student characteristics (BS, SES,
etc.).
Participants
The participants for this study were students at Southern California Community
College (SCCC) enrolled in this 2-year transfer and vocational community college from
2007 to 2009. Females accounted for 54% and males for 44% of the enrolled student
population (Community College League of California, 2009). According to the SCCC
accreditation report, the rate of female enrollment was 52.3%, and male enrollment was
47.5%. Additionally, according to the SCCC 2007 program review and accreditation data
provided by SCCC, over 70% of the students enrolled were 25 years or younger. This
college is very diverse ethnically (according to its accreditation documents) and has a
36
population that is composed of 16.1% Hispanic, 33.2% Asian, 2% African American,
39.7% Caucasian, satisfying one of the areas identified previously as a potential risk
factor. Students‟ personal data was pulled from the SCCC research database based on
participation in specific classes. The classes targeted were offered in a single semester in
F2F and online format. If a class was only offered in one of these formats, it was
excluded.
Data Collection and Sampling
The quantitative data set consists of enrollments for online DE and traditional F2F
classes for the students at SCCC. The data were made available from the research office
and public documents, such as the schedule of classes. Data sets were sampled from
archived data accessible through the research office of SCCC. Each student was chosen
based on his or her attendance in either the F2F or online DE class within defined classes.
Courses examined were from across the discipline spectrum and include: accounting,
anthropology, art, business, computer business applications, criminal justice, college
success, computer science, counseling, digital arts, environmental studies, English,
history, health education, humanities, marketing, math, music, philosophy, psychology,
real estate, sociology, and Spanish (for list of courses included see Appendix C). The
final total was 25 subjects, 867 classes/sections and 212 instructors.
One section of this set of data encompasses the grades of each student, and the
other set of data gathered will be the demographic characteristics of the student. The
student demographic characteristic variables that were examined are: race/ethnicity, age,
gender, and basic skills (defined as currently taking a basic skills class and/or ever having
taken a basic skills class at SCCC). Financial information is more difficult to attain; in the
37
application to the college, a student does not note this like the other student demographic
information attained for this study. However, if a student was accepted for a Board of
Governors fee waiver (BOG), this is tracked within the college (see Appendix B for BOG
charts). Therefore, when available for a student, this data is also used as a measure of
comparison. The quantitative elements of the study, identified as student characteristics,
are: gender, age, ethnicity, basic skills and low-income (via the BOG Waiver).
This study focused on examining the differences in student learning and
curriculum (grades), rather than teaching styles. Success in online DE versus F2F classes
is examined based on the students‟ final grades. The official measure of “success” is
defined as any grade that is passing, this being a C (2 grade points) or better. The
college‟s regulations noted within its printed catalog note that if a student earns a GPA of
2.0 or below, they get placed on probation. Some classes are graded on a “credit/no
credit” basis, and for those classes “credit” counts as success. The grades data is
compared to student demographic characteristics (listed above) to measure meaningful
differences in attainment based on the typical characteristics of a California community
college student.
This is a quasi-experiment and no students have been randomly assigned to
groups for study. Students self-selected through enrollment choices into either the online
DE or the traditional F2F version of the classes sampled. As mentioned in the Literature
Review, students self-selected based on criteria like: personal schedules, family
responsibilities, work schedules, course availability and other influences. This is an
archival study utilizing records from the research office of SCCC with no randomness
applied for student assignment to classes. However, this sample is thought to be effective
38
and wholly representative of the population, with no reason to suspect that the data
provided would not be able to answer the basic premise of this dissertation. Students are
required to enroll in classes for transfer, and successful completion of the general
education pattern for the Associate of Arts degree or certificates of achievement.
Therefore, they will enroll in the appropriate classes, those examined in this study, if they
intend to participate in higher education and/or continue their education at a four-year
institution.
Data was gathered across the higher education subject spectrum by looking at
diverse disciplines to provide meaningful data in order to see any difference in attainment
or discipline effects. The study started with a basic online versus F2F t-test and then
expanded to additional t-tests and chi-squares looking for significant differences between
online and/or traditional F2F student by student demographic characteristics. The goal
was to assess factors that affect student performance data (grades) and which factors
(data sets listed following) are more influential.
Data Analysis Framework
The dependent variable of course grade was compared across the independent
variable of class format – online DE versus F2F. Additional demographic independent
variables (student characteristics) were also explored. A series of t-tests and chi-squared
tests of independence were employed for all data analyses. These statistical methods were
chosen due to classroom teaching delivery method as the focus of this dissertation.
Student characteristics were a secondary piece of the study and discussed due to the
generalized student characteristics of the community college students as discussed in
Chapter Two (see Cox, 2005).
39
Six foundational t-tests were conducted that compared the data. These tests
included the entire sample and formed the foundation for further comparison and
discussion (see Table 1). The letter grades earned by students were noted as GPA value
(A=4.0, B=3.0, C=2.0, D=1.0 and F=0) to form the parts of the dependent variable.

Based on the beginning findings and small effects on the six basic t-tests, further
examination was performed to look a bit closer at the students‟ characteristics for
possible influences and differences. These additional tests were to include a look into
comparing online DE to F2F broken out by the disciplines listed above. However, the
ultimate goal was to see if online DE or F2F is a more effective pedagogical mechanism
for students‟ success, so additional tests were utilized to look more in depth and from a
slightly different perspective into student demographic characteristics. The t-tests
transferred letter grade to GPA value; in contrast, the next sequence of chi-square
analysis tests were performed to look at frequencies of those who passed the class (grade
of A, B or C) or did not pass the class(grade of D or F).
Table 1: Quantitative Elements of the Study: Statistical Tests Performed
Test 1: Overall - F2F versus Online DE
Test 2: BOG - F2F versus Online DE
Test 3: Basic Skills - F2F versus Online DE
Test 4: Age - F2F versus Online DE
Age: under 21, 21-25, 26-35, 36-45, 46-55, or 56 and older
Test 5: Gender – F2F versus Online DE
Male or Female
Test 6: Ethnicity – F2F versus Online DE
Asians/ Filipinos, Black, Hispanic, Other (includes Native
American and Pacific Islanders) or White
40
With the chi-square tests some differences were found that pointed to the
possibility of interesting significance levels. However the question was whether these
differences were in fact of consequence or simply due to the large sample size. One last
set of chi-square test was performed with the same independent variables (class method
and student characteristics). This time the grade data was broken down into a more
complex manner where each grade(A‟s, B‟s, C‟s, D‟s and F‟s) was examined in a four
degrees of freedom chi-square. The objective here was to look for differences in grades
and student characteristics in a more specific manner and to try to bring additional
meaning to tests of this large data set.
Qualitative Measures
To increase the validity of this study, a qualitative measurement instrument was
used to increase the robustness and value of the findings to the field of knowledge. An
eight question survey instrument (Table 2) was sent to each of the faculty members who
were the instructors of record for the classes studied (see Appendix C for a list of the
courses and Appendix D for the survey instrument). These were the same courses from
which the quantitative data was gathered. These surveys were made general to allow for
any sort of answer that might point to differing teaching practices that might have
allowed for varied student experiences, and thus grades, in the F2F versus online DE
version of a course. For the qualitative elements of the study there were 55 courses
examined, for a total of 867 sections, taught by 212 instructors.
41

The questions were broken into sets, in which each set sought to ascertain a
separate piece of the puzzle for what was used in the online DE or F2F class (for
complete survey instrument see Appendix D). Set one (Table 2, questions one and two)
Table 2: Faculty Survey Questions
1. Asynchronous (please check all methods you utilize on a regular basis)
  Email
  Discussion Board
  File Transfer (ex: PPT Slides)
  Streaming Media
  Texting
  Blogging
  Other:
2. Synchronous (please check all methods you utilize on a regular basis)
  Web-based Chat
  Telephone
  In-Person Meetings - Outside of Scheduled Class Lecture/Lab Hours
  Video Conferencing
  Instant Messaging
  Other:
3. Can you please describe why you choose the above communication patterns:
4. What in your experience with the above methods (asynchronous and/or
synchronous)
in your class were effective: and why:
5. What in your experience was not effective: and why:
6. Can you describe how you used the above methods of additional instruction
(asynchronous and/or synchronous) for grading purposes:
7. What are your perceptions of your students‟ ability to utilize asynchronous
methods
effectively? (please circle the appropriate number)
Very Somewhat Neutral Somewhat Very
Effective Effective Ineffective Ineffective
1………………2……………………3……………..…4………….…...…5
8. What are your perceptions of your students‟ ability to utilize synchronous
methods
effectively? (please circle the appropriate number)
Very Somewhat Neutral Somewhat Very
Effective Effective Ineffective Ineffective
1………………2……………………3……………..…4………….…...…5
42
asked the faculty what methods of asynchronous and synchronous communication
methods they used within their classes. These were a series of check boxes, making it
simple for the faculty to complete. Set two (Table 2, questions three, four, and five)
sought to understand why faculty used these particular methods and what their
experiences were. These were short answer, allowing the faculty to give a richer and
more descriptive answer. Due to the variety of classes, these were made as open and
general as possible in order not to solicit a specific answer and allow for discrepancies
across the disciplines. Question 6, (see Table 2) was again short answer and sought to
solicit descriptions of methods of communication in grading. Finally, set four of the
survey questions (Table 2, questions seven and eight) used a Likert scale where a faculty
member could rate his or her perceptions of students‟ abilities to utilize asynchronous and
synchronous methods of communication within their classrooms. The goal was to see if
there is a pattern from the perspective of the faculty members for communication styles
that supports the above data sets and potential conclusions that this dissertation is
seeking.
A total of 264 surveys were sent to faculty members in mid-October with one
month in which to complete them. The classes surveyed were grouped as a set and sent to
the individual faculty members. Each set was placed in their campus mailbox, with a due
date of November 30
th
. Follow-up was conducted through email and personal
conversations. Most surveys were returned via the print method, but several of the faculty
that teach primarily online chose that method for submitting their responses. Starting in
January, the survey data were assembled and organized according to similarities and
original responses.
43
Discussion and Significance
My original expectations were that I would find that overall there is no difference
in online and F2F classroom formats, thus reflecting the patterns of much of the
previously discussed research. However, I also felt that there may in fact be a difference
between specific risk factors for performance or success rates. The community college
students present a group sub-set that about which there is a lack of published research and
their experience has not been adequately examined for success or failure rates when
determining effectiveness of the online environment (discussed in Literature Review)
(Cox, 2005). Research suggests that community college students struggle to perform
because of multiple student characteristics and potential risk factors. Thus the hypothesis
is that when examining student demographic data for example students with a basic skills
background, that these BS students do perform less effectively in an online DE versus a
F2F classroom. If true, this would imply that the transfer of classrooms from a traditional
F2F format to an online format may not be in the best interest of the California
community college students and their learning.
Limits and Ethics
There are more limitations for this particular study than present in studies with
controlled and randomly assigned groups. Therefore, there are several obvious limits that
this study cannot control for. Primarily, this is an archival based study that gathered
historical data from actual records thus prohibiting the possibility of random assignment.
Therefore, there is a lack of control for both student and faculty selected preference of
instructional methods. Students may in fact choose classes in online DE or in the F2F
format because of scheduling conflicts or belief that one may influence their success in
44
one way or another. This self-section does impact the study as another potential risk
factor, but there is no reason to assume that this would harm the basic premise in the
research question or impact the data negatively. Furthermore, this dissertation mirrors
much of the research discussed in the Literature Review.
This study did not seek to match classes by faculty of record. This is due to
difficulties of gathering enough data that would allow extrapolations of data that would
seek to answer the broad comparisons of student achievement in online versus F2F
classrooms. There is a tremendous amount of variability in the online world and in F2F
classrooms in instructional styles utilized. This study does not utilize controls, or set a
taxonomy, to track pedagogical tactics or variables within either classroom method of
instruction.
Grades are used as the standard of comparison for this study, but there are limits
to what grades can tell us with certainty, that limits their absolute effectiveness. Since a
student‟s grade is the standard of measure for the campus as related to the entire school,
there is a certain amount of differences between faculty assignment of these grades that
cannot be controlled for. These faculty and course related curves lead to a level of “wash
out” that removes some of the differences that this study is in fact seeking to identify.
Additionally, these differences are also present between instructors within the school and
grades as a standard measure can lead to a retarding of the usefulness of these findings.
This study cannot control for individual student ability within a subject area,
which might include prior training or exposure at other campuses. There is no ability to
control for individual comfort with the technology used (Internet, chat, etc.) when
participating in an online DE class. Another element this study cannot control is that
45
faculty may be better prepared to teach in an F2F class versus an online DE section.
Finally, there was not a control as part of this study for the pedagogical styles that the
faculty utilized within particular classrooms.
Due to the focus of this study on community college students, application and
generalizability to the larger college populations is constrained. As discussed earlier,
community colleges represent the placement of many of the higher education enrollments
for minorities and low-income students, compared to other institutions within the higher
education realm. Additionally, the measurement of grades should be an appropriate
measure of student success in a classroom, due to four-year schools accepting the given
grades at a community college for matriculation and student transfer. However, grades
represent a crude proxy for measuring what and how much students learn.
Data gathered kept the actual identity of the student confidential (all names,
student identification numbers, etc. were removed by the institutional data office).
Participants‟ identifiers and names, from the professors, to the students, to the members
of the research office, and finally the college research site, are all altered to ensure that
confidentiality is secured. Due to the removal of all student identifiers a final count of the
unique individual students that participated in this study was not possible. All data was
collected via college records and therefore informed consent and debriefing was not
done.

46
Chapter Four
Findings and Discussion
The purpose of this study was to compare student performance in online distance
education (DE) and face-to-face (F2F) classrooms. With increasing numbers of
traditional F2F classrooms being transferred to the online DE format, questions have
arisen as to the success of these classrooms in effectively serving the educational needs of
the student. Overall there were no differences found between the online DE and F2F
classroom formats. However, when student characteristics combined with individual
grades were examined, there were more proportionately more online DE A and F grades
and more F2F B, C and D grades earned by students.
The primary method of comparison for this study is the examination of archived
quantitative data that was attained via the campus research office. The archival data is
focused in the academic years 2007-2008 (fall 2007 and spring 2008) and 2008-2009 (fall
2008 and spring 2009). There were 867 sections used for comparison, which
encompasses 55 classes from 26 disciplines (discussed on pages 31-33, for courses see
Appendix C).
A qualitative survey was also sent to the faculty of record, obtained from the class
schedule, who taught the surveyed classes. There were 264 surveys sent out; 46 packets
were to faculty who were not reachable, and 56 were returned. This represents a 25%
return rate.
Archival data was gathered and utilized to compare the success rates of
community college students in online DE and F2F classes. This study focuses specifically
on California community college students and compares the two versions of classrooms
47
and attainment based on student characteristics that may prevent success in higher
education classes (for discussion see pages 21-27). The student characteristics for this
study are: low income, basic skills, gender, ethnicity, and age.
A survey was completed by faculty members who teach classes in each format
(online DE and/or F2F) and focused on the communication methods they utilize in each
classroom, with further questions focusing in on which communication methods they felt
were most effective (discussed on pages 35-38) . The survey data sought to reveal if there
were differences in the structuring of the communication methods in the two classroom
formats that might have affected the grades students received.
Each data element was used for the purpose that it provided a possible piece of
the puzzle of student success in online and F2F classes. The quantitative archival data
included class sections, grades, and student‟s demographic information. The faculty
survey focused on what instructional methods teachers use in online and/or F2F classes,
to look for potential pathways to success and to identify the elements that differed
between the types of classrooms.
Treatment of the Data
Findings will be presented in this chapter addressing the research questions from
several perspectives. First, a simple t-test comparison was made comparing GPA value
(A=4.0, B=3.0, C=2.0, D=1.0, F=0) for the online DE versus F2F delivery method of the
classes. This allows for an overall summary discussion that looks at the archival data to
discuss answers from a perspective of grade point average. As discussed earlier, this
standard is generally used for the transfer process from the 2-year to 4-year institutions.
Due to the large sample size, it was determined that only alpha levels less than .001
48
would be considered as significant. Each risk factor was also compared utilizing t-tests to
look for differences in GPA attainment. Next, a chi-squared test of independence was
utilized to look for differences in students with passing grades (A, B, C) and not passing
grades (D, F). The purpose was to investigate possible generalized academic success
differences between the F2F and online classes, based on a simple pass/no pass
comparison. Finally, four degrees of freedom chi-squares were utilized to examine
specific letter grades (A, B, C, D, and F) to provide a deeper examination of the archival
data for a better understanding of any achievement patterns of community college
students in the online DE versus F2F classrooms. The faculty survey provided an
additional level of analysis of the effectiveness of the method of classroom delivery, by
providing a description of communication methods from the instructor‟s perspective.
Comparisons of Online and Face-to-Face Data Sets
There were slight differences in the students enrolled in each method of class
delivery. The online DE classes generally include fewer enrollments than the F2F classes.
There were 18,776 student characteristic profiles in the data set gathered that for the
online DE classes. The online students mean age was 25 years old, which is slightly
higher than the 21-25 average age of the community college student, but matches well the
demographics of SCCC. The youngest enrollment in the online DE classes was a 14 year
old student and the oldest was a 75 year old student. There were less low income and
basic skills students represented in the online DE classes, versus the F2F classes.
However, the ethnicity percentages of the online class enrollments were very close to the
campus percentages.
49
For the F2F classes at SCCC there were 44,218 student characteristic profiles in
the data set. This accounts for approximately 70% of the total data set (62,994 student
records). Overall the F2F class enrollments match the profile of the college as a whole, as
detailed in the participants section of chapter three. The student characteristics data
showed that those enrolled in F2F classes had a mean age of 22 years old, the minimum
age was 14 and the oldest student was 80. Approximately, 55% of the F2F class students
only, were students that were under 21 years of age while 30% were in the 21-25 year old
category. Additionally, 16,053 of these students were low-income, which is 67% of the
total number of low-income students from the sample. Of the total female students in the
data set (33,372) 67% of them registered for the F2F classes and 74% of the total males
in the data set (28,877) registered for a F2F class. Many of the basic skills students also
registered for the F2F classes, with 16,538 basic skills students in the F2F class
accounting for 78% of the total population of basic skills students for this college data set
(21,292). Finally, within the F2F classes the majority of students were in two broad
ethnic categories: white (41%), and Asian/ Filipino (31%). This generally matches the
populations of the college as a whole. The other categories Native Americans and Pacific
Islanders, (25%), Hispanic (21%) and Black (2%) also have F2F enrollments generally
matching the enrollments of the college.
Based on the data above, the F2F classes had more basic skills and low-income
students than the online DE classes. This could be due to several reasons, and all are
anecdotal, thus not supported by research or data within this dissertation. One aspect is
the self-selection of students, versus seeking out a counselor or other professional to
assist in college course selection. Students with low incomes or basic skills may not be
50
aware of the ease of use for the online learning systems and may also lack access to a
computer with internet. They may thus shy away from courses offered in the online DE
format and thus the percentages for this study reflect this. Additionally, proportionally
more males took more F2F classes than online DE classes. Again, anecdotally this could
be due to male dominated programs that, due to schedules and program requirements,
may rely on the F2F classes for class scheduling. For example, sports preferences and/or
the fact that the campus‟ criminal justice program is primarily offered in a F2F format
and has only a few classes offered in the online format. The ethnicity breakdown on the
F2F versus online DE classes is fairly representative of both the percentages for the
campus and each method reflects similar patterns of enrollment. See Appendix E for
more information.
Findings for Overall Comparison of Online Distance Education versus Face-to-Face
Classes
The overall comparison of online DE classes and F2F classes resulted in no
differences between the two methods of instruction, for GPA and pass/fail comparisons.
However, when an examination of individual grades occurred there were more online DE
grades of A and F earned by students. Again, there was no control for students‟ self-
selection into classes due to the assumption that when examined as one large group the
differences of distribution are mitigated.
When the entire body of quantitative data was subjected to a single independent-
samples t-test analysis, which accounted for the possibility of chance, the divergence
between the means found that there are no significant differences in students‟ grades (see
Table 3 and Appendix A). The finding of no differences between the classroom types,
51
suggests that both classroom types are equally effective and there is no advantage to one
format over the other in terms of the students‟ grades. This was confirmed with a non-
significant chi-squared analysis between passing grades (C or better) and non-passing
grades (D or F), (see Table 3). The standardized residuals suggest that all differences
could be expected due to chance alone. This shows that students, in multiple disciplines,
both pass and fail to pass classes at equal rates in each method of delivery. This test was
looking solely at pass/fail rates and not searching for determinations of effectiveness.
Additionally, this confirms the findings of the majority of the literature cited in Chapter
Two which suggests that there tends to be no significant differences between online DE
and F2F classrooms when it comes to students‟ grade attainment.
Despite these analyses concluding a lack of differences between the student grade
assignment in online DE and F2F formats, when looking at the specific grades students‟
earned in each class format, a different picture emerges. A four-degrees of freedom chi-
square test of independence, which compared the frequency of individual letter grades (A
through F) across the two methods of instruction, was performed on the data. Findings
suggest that there were significantly more A‟s and F‟s in the online method of instruction
and significantly more B‟s, C‟s and D‟s in the F2F method of instruction (see Table 3 and
Appendix A). This wider distribution of grades in online DE classes was an unexpected
finding that was not discussed in the literature and suggests a topic for future research
into contributing factors that influence students‟ grades attainment.
52
Findings for Comparison of Online Distance Education versus Face-to-Face Classes for
Low Income Students
The findings for the comparison of online DE and F2F classrooms for low income
students resulted in a similar pattern and supported the general overall findings listed
above in the overall comparison, which also supports the research cited in Chapter Two.
The basic comparisons found that there was little to no significant difference between the
online DE and F2F classes. However again, the grades breakdown showed more A‟s and
F‟s earned in the online DE classes. For this study low income status for California
community college students is measured at SCCC through acceptance of a Board of
Governor‟s Waiver (BOG – Appendix B). Therefore, this is not the entire group of
potentially low income students attending SCCC but just those who applied for and were
Table 3: Quantitative Elements of the Study - Test 1: Overall - F2F versus
Online DE

T-test: t (47,201) = 3.090, p = .002, r (47,201) = -0.14
Online DE Face-to-Face
Means 2.3977 2.3535

Chi-square:
2
(1)
= 5.504, p = .019, r
s
(57,657) = .010
Online DE Face-to-Face
Grade C or Better 11052 23664
Grade D or F 7518 15425

Four-degrees of Freedom Chi-square:
2
(4)
= 338.883, p < .001, r
s
(47,201) = -.027
Online DE Face-to-Face
Grade A 5033 (8.7*) 8758 (-6.0*)
Grade B 3469 (-3.7*) 7972 (2.6*)
Grade C 2496 (-9.6*) 6878 (6.7*)
Grade D 1008 (-3.9*) 2522 (2.7*)
Grade F 3233 (5.7*) 5834 (-3.9*)
* Number in parenthesis represents the Standard Residual

53
accepted for the BOG waiver which indicates there may be students who are not
accounted for in this research that could be considered as low income.
For BOG only students, there is no significant difference and chance is accounted
for in a t-test looking at the online DE method of instruction and F2F method of
instruction in GPA value of the letter grade earned (see Table 4 and Appendix A). The
chi-square test for independence between passing and non-passing grades for BOG only
students resulted also in a lack of a relationship between online DE and F2F methods of
instruction (see Table 4 and Appendix A). All differences were as expected by chance,
and students are passing and not passing at equal rates. However again, as discussed
above in the overall comparison, when the student‟s actual letter grade was investigated,
the four-degrees of freedom chi-square test produced a significant relationship. For the
low income students, significantly more letter grades of A and F were earned than would
be expected by chance in the online DE method of instruction, and significantly more B‟s
and C‟s than expected by chance were earned in the F2F classes (see Table 4
andAppendix A). Therefore, again the pattern of students‟ earning more of A‟s and F‟s in
online DE and the middle B, C and D grades in the F2F format (a bi-polarizing pattern) is
also true for low-income students.
54

Findings for Comparison of Online Distance Education versus Face-to-Face
Classes for Basic Skills Students
For the comparison of basic skills students, looking for a difference in
achievement between the online DE classroom and the F2F classroom, the results were
mixed. Basic skills students are ones who need to complete mastery of lower level math
and English before they are able to take college or four-year transfer accepted math and
English classes.
For basic skills students only, the results of the t-test (see Table 5 and see
Appendix A) comparison showed that GPAs value in online DE classes was significantly
higher than the GPAs in F2F classes. This would show that more students, than
accounted for by chance alone, earned higher grades in online DE classes. It is possible
Table 4: Quantitative Elements of the Study - Test 2: BOG - F2F versus Online
DE

T-test: t (17,768) = 1.396, p = .163, r (17,768) = -.010
Online DE Face-to-Face
Means 2.4775 2.4461

Chi-square:
2
(1)
= 8.209, p = .004, r
s
(21,715) = .019
Online DE Face-to-Face
Grade C or Better 4648 8816
Grade D or F 3007 5246

Four-degrees of Freedom Chi-square:
2
(4)
= 166.943, p < .001, r
s
(17,768) = -.028
Online DE Face-to-Face
Grade A 2231 (5.8*) 3375 (-4.3*)
Grade B 1372 (-3.6*) 2929 (2.7*)
Grade C 1017 (-6.2*) 2495 (4.6*)
Grade D 423 (-1.8*) 890 (1.3*)
Grade F 1212 (4.4*) 1826 (-3.2*)
* Number in parenthesis represents the Standard Residual

55
that this difference could be explained by the characteristics of the basic skills student, in
that the online DE course allows for more “classroom” time spent on the concepts, rather
than just listening to a lecture presented by the instructor. Many of the instructors that
responded in the survey (for survey instrument see Appendix D), mentioned that for
online DE classes they simply put detailed PowerPoint Slides on the classroom page,
instead of taping a live lecture. The students would thus not have to take notes while a
teacher is talking to learn the materials and could focus on the theories and concepts.
Finally, the online DE format may offer the students greater amounts of time to complete
the assignments and tests without the F2F demands that in-person classes may impose.
However, despite the difference being significant and the potential research areas left to
explore, for this study the effect size mitigates this finding as one to focus on because it
suggests a very weak relationship.
The chi-square test for independence between passing and non-passing grades for
basic skills only students again failed to find a relationship between online DE and F2F
methods of instruction (see Table 5 and see Appendix A). Again, all differences could
have been produced simply by chance. Further, a measure of Spearman‟s rho is .000,
showing that there is absolutely no relationship between the groups (see Appendix A).
This again confirms the past literature, which suggests that there is no difference between
the online DE method of delivery and the F2F method of delivery for student
achievement.
Once again, the four degrees of freedom chi-square test, which broke the grades
into letter grade-based categories, showed the same patterns as the overall comparison
and the test for the BOG students. Basic skills students received significantly more A‟s
56
and F‟s in the online DE sections than expected by chance, and significantly more of the
middle grades were assigned in the F2F class than expected by chance (see Table 5 and
see Appendix A). This once again points to a split in the grade attainment between the
online DE and F2F delivery with more bi-polarized A and F grades being earned in the
online DE classes. Further research would need to explore the differences, if any exist,
between the basics skills and standard online DE courses that may have also affected this
outcome.
Findings for Age Categories Comparison of Online Distance Education versus Face-to-
Face Classes
The results of the comparisons by age groupings varied. In general they followed
the same patterns of no significant differences for pass/fail comparisons and higher A‟s
and F‟s in online classes. However, the younger students showed a greater aptitude
Table 5: Quantitative Elements of the Study - Test 3: Basic Skills - F2F versus
Online DE

T-test: t (15,814) = 5.774, p < .001, r (15,814) = -.046
Online DE Face-to-Face
Means 2.6221 2.4726

Chi-square:
2
(1)
= .002, p = .963, r
s
(19,002) = .000
Online DE Face-to-Face
Grade C or Better 3026 9233
Grade D or F 1667 5078

Four-degrees of Freedom Chi-square:
2
(4)
= 194.087, p < .001, r
s
(15,814) = -.065
Online DE Face-to-Face
Grade A 1557 (8.5*) 3539 (-4.9*)
Grade B 857 (-3.5*) 3069 (2.0*)
Grade C 589 (-7.0*) 2601 (4.0*)
Grade D 236 (-2.8*) 911 (1.6*)
Grade F 656 (2.1*) 1801 (-1.2*)
* Number in parenthesis represents the Standard Residual

57
toward success in the online classes, and the older students a greater success rate in the
F2F classes. Age groupings were separated by California community college standards
and were chosen based on the preferences of the college research office (who provided
the data). The age groupings were broken into the following six categories: less than 21,
21 to 25, 26 to 35, 36 to 45, 46 to 55, 56 and older. The minimum age for this grouping
was 14 and the maximum age, where data were available, was 80, with the average
student age being 23.13 years old.
When looking at the GPA differences between online DE and F2F courses, each
age group produced slightly differing results. Tests for the students under 21 years old,
and those 36 to 55 year olds all produced no significant differences between the student‟s
online DE and F2F GPA, and all grades earned differences between the two methods of
delivery were as expected by chance alone (see Table 6 and Appendix A). These age-
based groupings of students were consistent with the patterns previously discussed and
thus match the research literature findings of Tallent-Runnels, et al. (2006) and Russell
(1999, 2009). However, for those 21 to 35 years old the GPA attained in F2F was
significantly higher than GPA in online DE classes (see Table 6 and Appendix A). Like
before with the significance difference found in basic skills students, the effect size is
weak suggesting that these are weak relationships. These findings suggest that as students
begin to get older than 21 years old, success rates in the online DE classes begin to
decrease, possibly because of a lack of familiarity with technology or a lack of
experience with the online format, but again the correlations are very weak, and this
difference does not continue for the 46 to 55 year olds. Therefore, this finding is taken
lightly and would suggest that further research is needed.
58
There was a significant difference in GPA values for students aged 56 and older.
Those in the F2F classes achieved a higher GPA value than in the online DE classes (see
Table 6 and Appendix A) with the effect size suggesting that this is a weak to moderate
relationship. For this group, it can be said with more confidence that these students do
indeed do better in the F2F classroom format. This finding could be due to the older
students‟ lack of comfort with both computers and/or the online DE education software
applications utilized for the online DE classrooms. Another possible reason for the
difference might reside in the older students‟ motivation for attending school. If the class
was being taken for leisure, the older student might be placing less importance on their
letter grades, possibly making them also less tolerant towards dealing with difficulties
that might arise for them in the online DE format. Since these difficulties might be
different from any challenges they may have experienced in their earlier college career
when there were only F2F offerings, this might lead them to simply stop working on the
online DE class, resulting in a failing grade. Additional research could explore these
possibilities and should look at the students‟ grades but also measure their computer
applications skills and comfort. The chi-squared tests discussed below also help to clarify
possible differences that were found by looking at specific grade categories instead of an
overall comparison.
Chi-squared tests of independence were performed for each age group, and no
differences in pass rates between online DE and F2F classrooms. The students younger
than 21 and those 36 or older showed no significant pass versus no-pass differences
between the online DE and F2F teaching formats (see Table 6 and Appendix A). This is
the majority of the age groupings (the remaining groups are discussed below) and also
59
includes the under 21 year old students who would be attending community college right
out of high school. These findings support the literature and suggest no differences
between the outcomes in the two classroom methods of delivery.
However, in the analysis of the age groupings of the 21 to 35 year old students,
there were significant differences found (see Table 6 and Appendix A). Each of these has
a similar pattern, in which significantly more students than expected by chance passed the
F2F classes and failed the online DE classes. This finding is critical due to the large
number of community college students being within these age brackets, the mean age of
the entire sample falls within this range. Additionally, many of the returning students
(due to unemployment and other factors) previously discussed are also within these age
brackets. Therefore the F2F classroom would be a better choice for scheduling for the
students that are seeking transfer, retraining and additional course credits.
Four degrees of freedom chi-squared tests of independence were again performed
with the results replicating the previous findings discussed for the other risk factor groups
(basic skills and BOG students). Across three age groupings, for students under 21 to 35
years old, significantly more letter grades of A and F were earned in the online DE
method classrooms (see Table 6 and see Appendix A). These grouping include most of
the student body attending SCCC. The 36 to 55 yr old students‟ letter grade distributions
were all as expected by chance. This would suggest that the classrooms are equally as
successful for these students in the online DE and F2F method of delivery. Finally, for
the age 56 and older students, all letter grades varied as expected by chance except for the
D‟s in the online classes, which occurred significantly more than expected by chance.
These findings also are consistent with the discussions above for the basic skills and
60
Table 6: Quantitative Elements of the Study - Test 4: Age - F2F versus
Online DE
Under 21 years old
T-test: t (21,893) = 2.728, p = .006, r (21,893) = -.018
Online DE Face-to-Face
Means 2.2334 2.1676

21 to 25 years old
T-test: t (15,818) = -3.987, p < .001, r (15,818) = .032
Online DE Face-to-Face
Means 2.3388 2.4332

26 to 35 years old
T-test: t (5,823) = -7.377, p < .001, r (5,823) = .096
Online DE Face-to-Face
Means 2.5282 2.8032

36 to 45 years old
T-test: t (2,022) = -1.753, p = .080, r (2,022) = .039
Online DE Face-to-Face
Means 2.8457 2.9501

46 to 55 years old
T-test: t (968) = -2.345, p = .019, r (968) = .075
Online DE Face-to-Face
Means 2.8064 3.0085

56 years old and older
T-test: t (291) = -3.302, p = .001, r (291) = .190
Online DE Face-to-Face
Means 2.5683 3.1234

BOG students, due to the increase in A‟s and F‟s in the online DE classes. This supports
the value of additional studies that might point to potential correlations in research that
could focus on what influences contribute to successful grades.
61

Table 6: Continued

Under 21 years old
Chi-square:
2
(1)
= 1.102, p = .294, r
s
(26,768) = .006
Online DE Face-to-Face
Grade C or Better 3129 12032
Grade D or F 2457 9152

21 to 25 years old
Chi-square:
2
(1)
= 29.643, p < .001, r
s
(19,352) = .039
Online DE Face-to-Face
Grade C or Better 4213 7595
Grade D or F 2985 4561

26 to 35 years old
Chi-square:
2
(1)
= 31.651, p < .001, r
s
(7,085) = .067
Online DE Face-to-Face
Grade C or Better 2222 2419
Grade D or F 1343 1103

36 to 45 years old
Chi-square:
2
(1)
= 8.765, p = .003, r
s
(2,489) = .059
Online DE Face-to-Face
Grade C or Better 893 819
Grade D or F 456 323

46 to 55 years old
Chi-square:
2
(1)
=.373, p = .541, r
s
(1,160) = .018
Online DE Face-to-Face
Grade C or Better 424 409
Grade D or F 174 155

56 years old and older
Chi-square:
2
(1)
= 9.724, p = .002, r
s
(364) = .163

Online DE Face-to-Face
Grade C or Better 106 142
Grade D or F 71 47

62
Table 6: Continued

Under 21 years old
Four-degrees of Freedom Chi-square:
2
(4)
= 179.998, p < .001, r
s
(21,893) = -.028
Online DE Face-to-Face
Grade A 1344 (7.8*) 3820 (-4.0*)
Grade B 1014 (-2.6*) 4207 (1.3*)
Grade C 761 (-7.6*) 3997 (3.9*)
Grade D 365 (-2.1*) 1564 (1.1*)
Grade F 1130 (3.6*) 3693 (-1.8*)

21 to 25 years old
Four-degrees of Freedom Chi-square:
2
(4)
= 118.541, p < .001, r
s
(15,818) = .018
Online DE Face-to-Face
Grade A 1818 (2.0*) 2844 (1.5*)
Grade B 1346 (-3.0*) 2580 (2.3*)
Grade C 1026 (-4.6*) 2158 (3.6*)
Grade D 411 (-0.7*) 732 (0.5*)
Grade F 1288 (6.3*) 1617 (-4.8*)

26 to 35 years old
Four-degrees of Freedom Chi-square:
2
(4)
= 71.047, p < .001, r
s
(5,823) = .084
Online DE Face-to-Face
Grade A 1076 (-2.2*) 1213 (2.2*)
Grade B 680 (-1.0*) 723 (1.0*)
Grade C 456 (-0.4*) 470 (0.4*)
Grade D 149 (-0.4*) 157 (0.4*)
Grade F 568 (5.4*) 333 (-5.4*)

36 to 45 years old
Four-degrees of Freedom Chi-square:
2
(4)
= 7.052, p = .133, r
s
(2022) = .025
Online DE Face-to-Face
Grade A 493 (-0.1*) 432 (0.1*)
Grade B 260 (-1.0*) 257 (1.1*)
Grade C 136 (-0.2*) 122 (0.2*)
Grade D 55 (0.9*) 36 (-1.0*)
Grade F 138 (1.2*) 95 (-1.3*)

63
Findings for Gender Comparison of Online Distance Education versus Face-to-Face
Classes
For gender as a potential influence and risk factor for differentiation of course
grades between the online DE and F2F classroom format type, there were some
differences between the sexes. Females on the t-test showed no difference between the
two class formats and earned grades in each class at rates due to chance alone (see Table
7 and Appendix A). However, with male students there was a difference between success
with men performing significantly better in the online DE class than the F2F classes (see
Table 7 and Appendix A). Similar to the basic skills students above, this difference is
mitigated by the effect size which suggests a very weak relationship. This shows that
overall, the basic t-test supports the findings and direction of the literature review which
Table 6: Continued

46 to 55 years old
Four-degrees of Freedom Chi-square:
2
(4)
= 10.981, p = .027, r
s
(968) = .093
Online DE Face-to-Face
Grade A 213 (-1.6*) 247 (1.6*)
Grade B 123 (0.9*) 96 (-1.0*)
Grade C 83 (1.2*) 58 (-1.2*)
Grade D 19 (-0.1*) 19 (0.1*)
Grade F 63 (0.7*) 49 (-0.7*)

56 years old and older
Four-degrees of Freedom Chi-square:
2
(4)
= 16.841, p = .002, r
s
(291) = .197
Online DE Face-to-Face
Grade A 57 (-1.6*) 92 (1.5*)
Grade B 28 (0.6*) 25 (-0.5*)
Grade C 19 (0.2*) 19 (-0.2*)
Grade D 7 (2.0*) 0 (-1.9*)
Grade F 28 (1.3*) 18 (-1.3*)
* Number in parenthesis represents the Standard Residual
64
suggests very little difference between achievement levels of grades in the two classroom
methods of delivery.
Chi-squared tests of independence were again performed producing some
differences for comparison and discussion between male and female students and their
achievement in an online DE class versus a F2F class. More female students than
expected by chance did not pass in the online DE method of instruction than in F2F
methods (see Table 7 and Appendix A). All the frequencies for the male students were as
could be expected by chance, showing no differences between online DE and F2F in
whether a student passed or did not pass (see Table 7 and Appendix A). When the four
degrees of freedom chi-squared test investigating the frequencies of individual grades
were performed (see Table 7 and Appendix A) the analyses by gender for grades received
in online DE and F2F classes followed a similar pattern as previous groupings (BOG and
BS students), but were somewhat less consistent. For females, the online DE method
again produced a more polarizing pattern of grades, with significantly more letter grades
of A and F than expected by chance, while the F2F platform only produced significantly
more C‟s than expected. For males, however, the pattern was very clear. Online DE
classes produced significantly more A‟s and F‟s than expected by chance, while F2F
courses resulted in significantly more B‟s, C‟s, and D‟s than one would expect to occur
by chance alone (see Table 7 and Appendix A). These findings reinforce the conclusion
that was found in most of the groupings that the online DE classes create more extreme
grades, while the F2F classes produce more grades in the middle of the grading
distribution. Again, this suggests that the future research could look at the online DE
format and the possibility the format requires a slightly different skill sets, behavioral
65
pattern sets, or motivational starting points.
Table 7: Quantitative Elements of the Study - Test 5: Gender – F2F versus
Online DE
Females
T-test: t (25,190) = -1.450, p = .147, r (25,190) = .009
Online DE Face-to-Face
Means 2.4334 2.461

Chi-square:
2
(1)
= 23.878, p < .001, r
s
(30,447) = .028
Online DE Face-to-Face
Grade C or Better 6635 12305
Grade D or F 4351 7158

Four-degrees of Freedom Chi-square:
2
(4)
= 137.112, p < .001, r
s
(25,190) = -.004
Online DE Face-to-Face
Grade A 3034 (3.2*) 4937 (-2.4*)
Grade B 2136 (-2.1*) 4091 (1.6*)
Grade C 143 (-6.1*) 3250 (4.5*)
Grade D 600 (-2.0*) 1216 (1.5*)
Grade F 1842 (5.7*) 2653 (-4.2*)

Males
T-test: t (21,414) = 5.000, p < .001, r (21,414) = -.034
Online DE Face-to-Face
Means 2.3432 2.2323

Chi-square:
2
(1)
= .734, p = .391, r
s
(26,528) = -.005
Online DE Face-to-Face
Grade C or Better 4386 11025
Grade D or F 3087 8132
66
Findings for Ethnic Group Comparison of Online Distance Education versus Face-to-
Face Classes
When the same battery of tests was conducted for each major ethnicity grouping,
a very similar set of findings to student characteristics categories discussed above (basic
skills and low income) was produced and resulted in no significant difference between
the groups on the overall comparison and increases in A‟s and F‟s earned in the online
DE classes. In most cases, differences were few, and the differences that were uncovered
were followed up with very weak effect sizes greatly lessening the impact of the
difference. Also, when looking at individual letter grades, the online DE courses seemed
to once again promote more extreme grades.
For the t-test comparison of GPA units only the Asians and Filipinos student
group showed significantly better GPA values earned in online DE classes than in F2F
classes, but as with most other differences, the effect size was extremely weak. This
suggests that while there were more of these grades earned than chance would predict,
the result may be due more to the large number of students rather than more students
achieving higher grades in the online DE classes (see Table 8 and see Appendix A). For
Table 7: Continued

Four-degrees of Freedom Chi-square:
2
(4)
= 219.497, p < .001, r
s
(21,414) = -.046
Online DE Face-to-Face
Grade A 1931 (9.0*) 3673 (-5.6*)
Grade B 1294 (-3.4*) 3774 (2.1*)
Grade C 1039 (-6.9*) 3549 (4.3*)
Grade D 401 (-3.2*) 1277 (2.0*)
Grade F 1346 (2.5*) 3132 (-1.6*)
* Number in parenthesis represents the Standard Residual
67
black, Hispanic, other ethnicities (including Native Americans and Pacific Islanders) and
white students there was no significant difference between online classes and F2F class
GPA values. Students GPA values reflect achievement within each method of delivery as
expected by chance, and this would support the finding of the research referenced in the
Literature Review (see Table 8 and see Appendix A).
The comparisons of the frequency of students passing or failing to pass the class
across each method within the chi-square comparison, by ethic group, were as expected
by chance. For the Asian and Filipino students, the Black students, and the students of
other ethnicities including Pacific Islander and Native Americans, all frequencies of
students passing or not passing in either online DE or F2F classes were as expected by
chance alone (see Table 8 and see Appendix A). Meaning that there were no differences
between the classrooms formats and the grades earned for these ethnic groups.
Hispanic students failed to pass the online DE class more than expected by chance
while passing the online DE class less than expected by chance. However, in the F2F
class, Hispanic students‟ frequencies of passing and not passing were as expected by
chance (see Table 8 and see Appendix A). The effect size suggests that this is a very
weak relationship and the differences are thus not significant. White students earning a
grade of D or F in online classes occurred significantly more than expected by chance,
and all other frequencies were as expected by chance (see Table 8 and see Appendix A).
This would suggest that Hispanic and white students receive more D‟s and F‟s and thus
did not pass classes as often when they take it in the online DE format. For passing
success and transfer credit this would seem to suggest that these students should
concentrate their course selections in the F2F format. Possibilities of student motivation
68
and preparedness for the online classroom challenges, comparing just Hispanic and white
students, could increase and affect these findings; however, again, it must be stressed that
the effect sizes for these differences were very weak, suggesting that the significant
differences might simply be a relic of the large sample size.
Most of the ethnic groups followed a similar pattern to the other student
characteristics when the frequencies of the specific letter grades were examined with
more A‟s and F‟s in the online DE classes, but there is a wrinkle to add. The majority of
the frequencies of the letter grades for all of the ethnic groups occurred as they would be
expected by chance. However, many ethnic groups specifically earned more F letter
grades than would be expected by chance in the online DE classes.
Black students earned significantly more online F‟s than expected by chance
while all the other letter grades in online DE classes as well as all the letter grades in F2F
classes were as expected by chance (see Table 8 and see Appendix A). Hispanic students
also earned significantly more F‟s in online DE classes, but also significantly more C‟s
than would be expected by chance. In F2F classes, Hispanic students earned significantly
fewer F‟s and significantly more C‟s than chance would predict (see Table 8 and
Appendix A). Other ethnicities (including Native Americans and Pacific Islanders) also
earned significantly more online F‟s than expected by chance but few online B‟s than
expected by chance, with all other grades as expected by chance (see Table 8 and see
Appendix A). Like the others, white students also finished with significantly more online
F‟s than expected by chance but also significantly fewer online C‟s. In F2F courses,
white students had significantly more C‟s and fewer F‟s than chance would predict (see
Table 8 and see Appendix A). This would again suggest that students that struggle in a
69
particular subject get pushed toward the extreme grade of F, when in a F2F class they
might be able to attain a grade closer to the middle of the grading scale. However, all of
these groups were not receiving significantly more online A‟s as most previous analyses
might suggest. The reason for this can be found when we examine the final ethnic group.
While all other ethnic groups were busy earning significantly more F grades in
online DE classes, the Asian and Filipino students were earning only as many online F‟s
as chance would predict. They were however earning significantly more A grades in
online DE classes than expected by chance, a finding that only this group was able to
achieve (see Table 8 and see Appendix A). Additionally, the Asian and Filipino students
earned significantly fewer letter grades of B, C, and D in the online DE platform. In the
F2F classes, the opposite occurred, with significantly more B‟s, C‟s, and D‟s being
earned and significantly fewer A‟s being earned than expected by chance. This analysis
across the ethnic groups suggest that when the online DE classes promote more letter
grades of A, this is mostly descriptive of the Asian and Filipino students, while all of the
other ethnic groups accounted for the large number of failing grades in the online DE
classes. But again it must be stressed that all of the effect size indices should be
interpreted as very weak, so it would not be prudent to base policy changes or even make
recommendations to students based on these findings. This is due to the effect size
indices being as low as they are across all of the analyses in the present study. The
suggestion is that even the differences that were statistically significant should not be
taken as a basis for any kind of decision and based on the data, the most appropriate
interpretation of this study is to say that there are no meaningful differences in academic
success between the online DE and the F2F methods of instruction. However, these
70
findings could suggest the value of additional research comparing specific ethnicity
groups over a longer period of time, and in more depth to try to uncover specific reasons
for these different findings.
Table 8: Quantitative Elements of the Study - Test 6: Ethnicity – F2F versus
Online DE
Asians/ Filipinos
T-test: t (14,246) = 7.463, p < .001, r (14,246) = -.062
Online DE Face-to-Face
Means 2.7829 2.6041

Black
T-test: t (788) = -2.547, p = .011, r (788) = .090
Online DE Face-to-Face
Means 1.4662 1.7466

Hispanic
T-test: t (8,857) = -2.647, p = .008, r (8,857) = .028
Online DE Face-to-Face
Means 1.9419 2.0392

Other (includes Native American and Pacific Islanders)
T-test: t (1,517) = -.989, p = .323, r (1,517) = .025
Online DE Face-to-Face
Means 2.0355 2.1183

White
T-test: t (18,054) = -2.430, p = .015, r (18,054) = .018
Online DE Face-to-Face
Means 2.3005 2.3566

Asians/ Filipinos
Chi-square:
2
(1)
= 4.322, p = .037, r
s
(17,007) = -.016
Online DE Face-to-Face
Grade C or Better 4275 7222
Grade D or F 1959 3553
71
Table 8: Continued

Black
Chi-square:
2
(1)
= 8.095, p = .004, r
s
(1,056) = .087
Online DE Face-to-Face
Grade C or Better 135 295
Grade D or F 251 377

Hispanic
Chi-square:
2
(1)
= 18.033, p < .001, r
s
(11,023) = .040
Online DE Face-to-Face
Grade C or Better 1260 4486
Grade D or F 1339 3940

Other (includes Native American and Pacific Islanders)
Chi-square:
2
(1)
= 1.295, p = .255, r
s
(1930) = .026
Online DE Face-to-Face
Grade C or Better 299 711
Grade D or F 295 627

White
Chi-square:
2
(1)
= 10.812, p = .001, r
s
(22,132) = .022
Online DE Face-to-Face
Grade C or Better 4119 9084
Grade D or F 2974 5957

Asians/ Filipinos
Four-degrees of Freedom Chi-square:
2
(4)
= 199.765, p < .001, r
s
(14,246) = -.084
Online DE Face-to-Face
Grade A 2331 (7.8) 3056 (-6.0)
Grade B 1173 (-3.2) 2324 (2.4)
Grade C 730 (-6.7) 1814 (5.1)
Grade D 282 (-2.9) 631 (2.2)
Grade F 725 (0.9) 1182 (-0.7)
72

Table 8: Continued

Black
Four-degrees of Freedom Chi-square:
2
(4)
= 12.380, p = .015, r
s
(788) = .094
Online DE Face-to-Face
Grade A 39 (-0.2) 75 (0.2)
Grade B 44 (-1.1) 103 (0.9)
Grade C 52 (-1.0) 115 (0.7)
Grade D 20 (-1.0) 50 (0.7)
Grade F 126 (2.2) 166 (-1.6)
Hispanic
Four-degrees of Freedom Chi-square:
2
(4)
= 46.624, p < .001, r
s
(8,857) = .024
Online DE Face-to-Face
Grade A 429 (0.8) 1340 (-0.5)
Grade B 452 (-0.5) 1533 (0.3)
Grade C 377 (-4.0) 1612 (2.2)
Grade D 184 (-1.1) 671 (0.6)
Grade F 623 (4.2) 1638 (-2.3)
Other (includes Native American and Pacific Islanders)
Four-degrees of Freedom Chi-square:
2
(4)
= 22.725, p < .001, r
s
(1,517) = .015
Online DE Face-to-Face
Grade A 132 (1.9) 224 (-1.3)
Grade B 83 (-2.5) 263 (1.7)
Grade C 84 (-1.2) 220(0.8)
Grade D 30 (-0.7) 78 (0.5)
Grade F 150 (2.0) 255 (-1.3)
White
Four-degrees of Freedom Chi-square:
2
(4)
= 57.900, p < .001, r
s
(18,054) = .010
Online DE Face-to-Face
Grade A 1656 (1.4) 3321 (-1.0)
Grade B 1414 (-1.5) 3162 (1.0)
Grade C 1042 (-3.6) 2585 (2.5)
Grade D 402 (-1.3) 933 (0.9)
Grade F 1290 (4.5) 2251 (-3.1)
* Number in parenthesis represents the Standard Residual

73
Faculty Survey Findings for Online DE versus Face-to-Face Communication Patterns
The faculty surveys supported the overall findings that the online DE and F2F
classrooms are comparable. Of the 56 faculty surveys returned, 34 were from F2F classes
and 22 were from online DE classes, and they contained very few differences in
instructional methods. Of the faculty respondents, seven are faculty who taught one class
in both methods of delivery, online and F2F. Overall the surveys from faculty teaching
the online DE version of classes followed curricular choices of the F2F classes.
Questions seven and eight ask faculty via a Likert scale what they felt about
students‟ abilities to use asynchronous and synchronous communication effectively
(Table 2). The purposes of these questions were to see if faculty felt students were more
comfortable communicating in the online versus the F2F environment. The majority of
the results of the survey were that faculty felt students were very effective in both the
online and F2F classes in both (synchronous and asynchronous) methods of
communication (see Table 9 for survey results). It was only in the F2F classes that a
small portion of the faculty felt that students were only somewhat effective in the
synchronous and asynchronous communication methods. This may point to faculty
responding to students‟ non-verbal communication in classes concerning methods of
communication. These non-verbal behaviors would be unavailable to the online faculty,
and thus not noted.
74
Questions four and five (see Table 2) asked faculty about their experiences with
the effectiveness of asynchronous and synchronous methods of communication patterns
within their classes. Both groups of faculty used email, and they felt it was effective due
to “ease of mastery” and “student familiarity with the technology.” Faculty felt that they
were able to effectively match, “the best method to the students‟ needs.” However, both
groups also mentioned the need to utilize telephone or in person meetings in order to
control for misunderstandings and the “lack of students showing up for office hours.”
Eight faculty respondents felt that online strategies were helpful but that F2F was a
necessary part of instruction. Those faculty that taught classes online mentioned that
often “students did not read directions” and they often needed to schedule an in-person
meeting or “phone conversation so there were no misunderstandings.” Overall all
communication methods had “degrees of effectiveness” but were not seen as a
“replacement for either paying attention in class” or “following lectures online.”
Table 9: Students' Ability to Utilize Communication Methods Effectively

Face-to-Face

Very
Effective
Somewhat
Effective Neutral
Somewhat
Effective
Very
Ineffective
Asynchronous 10 9 5 3 1
Synchronous 7 11 4 3 2

Online

Very
Effective
Somewhat
Effective Neutral
Somewhat
Effective
Very
Ineffective
Asynchronous 10 2 1 0 1
Synchronous 5 4 3 1 1

* Number entries reflect the number of faculty survey responses
75
The faculty who responded to the surveys used the same methods of
communication for both formats of classes, online DE and F2F classes (see Table 2). In
questions one and two of the survey, faculty indicated their utilization of communication
methods within their classrooms (see Table 10 for summary of results). Of the people
who responded to question one (48), 100% reported using email regularly as a
communication tool for their classes. This finding shows that instructors utilize email as a
regular tool for both online DE and F2F classes. Quite a few also used the discussion
board and file transfer (PPT) in both the online DE and F2F classes. Very few utilized
streaming media (only four respondents out of 48). Texting (one respondent) or blogging
(three respondents) were also used infrequently and all of the yes responders were F2F
teachers. For synchronous communication, many of the faculty utilized the telephone (36
respondents) and in person meetings (37 respondents out of 48). The uses of other
synchronous patterns of communication, for instance web based chat (eight respondents),
video conferencing (two respondents), instant messaging (five respondents) or Skype
(written in by two online DE faculty) were seen as helpful, but not adopted across the
faculty in high numbers within the online DE or F2F classroom formats. Overall, the
answers for questions one and two of the survey point to the faculty uses of modes of
communication being consistent, and not dependent on the class mode of delivery. This
would support that students communicate with the faculty and other students within the
class in the same manner and therefore communication methods are not a potential
influence for different experiences and thus achievement.

76
Faculty documented that, in question six (see Table 2),“regular” or continuous
communication was needed in any type of class format in order to ensure that “the
students were on track” for grading purposes. Some felt that online patterns of grading
were effective and others felt that online patterns of grading and communication were
never as effective as F2F classroom experiences. The faculty responses were reflective of
the wariness of the online DE classroom to be as effective as F2F classrooms for student
success. This was mentioned previously in a few of the research articles cited in chapter
two. However, the data about methods utilized in the survey for this dissertation reflect
that faculty employ similar methods of communication patterns for instructional content
regardless of the method of classroom delivery. Therefore their hesitancy may be due in
large part to the mystique of online DE classrooms rather than actual research based
conclusions.
Faculty did mention that they chose communication methods that worked best for
“teachers,”“students,”“the schedules of both” and a “sense of convenience” and
Table 10: Faculty Communication Methods Used
Face-to-Face
Asynchronous Synchronous
Email (30) Web-based Chat (5)
Discussion Board (6) Telephone (20)
File Transfer (ex: PPT slides) (11) In-person Meetings (26)
Streaming Media (3) Video Conferencing (1)
Texting (1) Instant Messaging (2)
Online
Asynchronous Synchronous
Email (18) Web-Based Chat (3)
Discussion Board (16) Telephone (16)
File Transfer (ex: PPT slides) (15) In-person Meetings (11)
Streaming Media (1) Video Conferencing (1)
Texting (0) Instant Messaging (3)

77
“flexibility” (question three of the survey, see Table 2). Their communications choices
overall reflect that they are aware of the “students‟ tough schedules” and structure their
classrooms to both allow “classroom flexibility” and choose communication methods to
match the “comfort of the student”. For the students‟, the data for the grades comparison
show that the communication styles chosen may be more or less comfortable for the
online students, thus forcing a gap between the A‟s and F‟s, and the middle grades (B‟s,
C‟s and D‟s). This leads to suggestions for future research that examine communication
patterns with random assignment to form more comprehensive determinations of the
effectiveness of synchronous and asynchronous methods of communication in online DE
classrooms.
The results overall show that there are little differences between the online DE
classroom and the F2F classroom. Faculty structure their communication in the same
way, no matter the method of delivery. Students‟ grades overall show that in general
pass/ fail comparisons, they achieve at the same rate. Therefore the two methods of
classroom format and delivery can be considered equivalent and the movement of F2F
classes to online DE does not negatively affect the success of students. The differences in
number of grades earned for the online DE classes at the A and F levels do suggest the
need for future research, that should examine what these differences can be contributed to
and seek to make comparisons that correlate why these differences are occurring.

78
Chapter Five
Conclusion
The majority of the literature cited in chapter two suggests that there is no
significant difference between the online distance education (DE) and face-to-face (F2F)
classrooms in terms of student achievement (Russell, 1999, 2009; Tallent–Runnels et al.,
2006). Much of this literature is based on a comparison of a specific course in the online
DE and F2F formats, or alternatively a set of classes by discipline. For this dissertation
the focus was across a variety of disciplines and not on a specific course or within a
specific discipline. Additionally, very few of these studies examine success or failure
from a perspective of student characteristics. Therefore this dissertation sought to
examine students populating the two-year educational system, due to their increased
potential risk factors (identified as student characteristics for this dissertation) that might
impact a student‟s achievement (Cox, 2005; Dougherty, 1994; Hoachlander, Sikora &
Horn, 2003; Moore, Shulock, Ceja & Lang, 2007). This study combined the comparison
of online DE classrooms and F2F classroom grades with student characteristics.
Summary of Findings
The primary finding reached from the data collected is that online DE and F2F
classrooms are equally as effective for students‟ achievement. The comparison of data
showed that of the conclusions reached, there was no statistical difference between the
online DE and the F2F method of instruction overall or by risk factor. This confirms the
previous research as discussed in chapter two, which is further summarized by both
Tallent-Runnels et al. (2006) and Russell (1999, 2009). The survey supports this and
found that, faculty do not approach the two classroom types differently. The structure of
79
their classrooms and communication methods used are, for the most part, identical in
each method of delivery. This would support the college administrative decision, and
dilemma discussed in the Introduction, that moves classes from a strictly F2F method of
delivery to an online format in order to supply the additional class seats that are
increasingly demanded for by the rising number of student enrollments.
Secondary findings conclude that students have proportionally more scores of A‟s
and F‟s in online DE classrooms and B‟s, C‟s and D‟s in F2F classrooms. Looking
overall at a general comparison of these grades in the online DE classes could suggest
that self-motivation and conscientiousness are crucial variables in success. The number of
F grades in the online DE classes could be an indication of lack of self-regulation towards
continued success. Additionally, students‟ might need a higher level of intrinsic
motivation to succeed due to less physical in-class structure, and thus a student must be
able and willing to be disciplined. Alternatively, this could suggest a division for
preparedness of individuals for the online environment. This could point to a trend that
underprepared students are getting further isolated out of the higher educational system
due to their lack of opportunities and preparation for college within the K through 12
educational setting. In other words not all school systems have equivalent funding models
that provide enrichment or even full educational curriculum models that ensure students
persist to transfer to higher education institutions. Another explanation could be the lack
of student access to computers or preparation for utilization of the online educational
classroom format. All of these possibilities are potential contributing factors that should
be explored in future research.
80
Finally, the discussion of findings for ethnic groups, despite the low effect size, is
worth noting. The discussion of these findings in chapter four demonstrates that Asian
students increasingly achieve the A‟s while students of other ethnicities and with other
student characteristics are receiving the F grades in the online DE classes. This would
suggest a depiction of the bi-polarization of the online DE grades, but offers little
explanation for why these differences occur. This dissertation can therefore only be a
piece of the complex discussion of student achievement within the higher education
system.
The research in this dissertation is a portion of the ongoing conversation related to
community college students and their achievement within the higher education system.
Added to the existing research on community college students, this study can help to
inform the ongoing discussion in determining what are the best ways to offer curriculum.
Each step adds to the process of determining what works best pedagogically for students
as a whole, so that the majority of students within the system will achieve successful
transfer and degree attainment. Ultimately research like this can be applied to discussions
and improvement of policy that can seek to improve the higher education system in
California (Bensimon, Harris & Rueda, 2007).
Many researchers have suggested approaches to the study of students with risk
factors. The literature generally involved looking at students and their progress from a
more cooperative lens and, utilizing multiple pieces of information from alternative
perspectives. Bensimon (2004, 2005, 2007) concentrates her “diversity scorecard” and
community college research on looking at educational outcomes from a culture of equity
stance, while at the same time maintaining the importance of ensuring that the researcher
81
does not fall into a deficit perspective. This research focuses on cooperative discussions
that seek to look at the pieces related to student success with the focus on various student
characteristics that may affect students‟ success or progress. Therefore, this study sought
to emulate these perspectives and examined success by student demographics in order to
look for differences or explanations in method of delivery. The attempt was made to
examine inequities that might be present within the two classroom methods. The online
DE grades findings suggest that additional research should focus on multivariate
comparisons that seek to compare ethnic groups and to uncover contributing factors for
success and failure within the online DE classrooms.
Risk Factors and Limitations
This study sought to examine classroom method of delivery to determine if there
were explainable differences in achievement. In many ways it would have been more
credible were it possible to apply random assignment of both students and faculty to
classrooms. However, this would have missed what the research question sought to
answer. The purpose of this study was to compare the online DE and F2F classroom to
see if students were achieving at the same rate. This leads to a potential measurement
problem for this study, due to a similarity of institutional practices. As shown through the
faculty survey, the two formats of classes structure their communication practices in
similar ways.
This study also sought to offer a comparison of the two classroom modalities and
compare effectiveness. In utilizing the methods listed in Chapter Three, this study was
able to compare the community college population in the same manner as much of the
cited literature. Thus begin the research process for studying the community college
82
populations in order to make effective determinations concerning online classrooms and
progress potential other research arenas. This study thus attempted to gather data that
incorporated several years, multiple subject areas and thousands of students in order to
make a determination as to what, if any, were the differences between online DE and F2F
classroom achievement. In this way this study would offer a look into the actual
classroom and student experience. Additionally, the discussion and examination of
student characteristics attempted to enlighten the research as to the experiences of the
“typical” community college student.
Further Research
Community college researchers are increasingly pushing for policies that have at
their core the awareness of inequities by race in educational outcomes. The assumption is
that every student is capable of success but that there are huge failures that require a
closer look to developing some sort of student success interventions (Johnson, 2009).
Combining this study with projects such as Bensimon‟s “Diversity Scorecard” could
allow for institutional agents to look in depth at institutional outcomes and perform a
measurement of these outcomes by categories such as race (2004, 2007). In this way
there can be a shift in what questions are asked and how students are served within the
higher education system and specifically at community colleges. Combining both the
grades of students and an in-depth look at processes or institutional factors could help to
reveal correlations between factors of success and final grades within each classroom
type. Ultimately the goal would be to seek to rectify the increased number of F grades
that students achieved in the online DE classes, by assisting students to achieve more of
the passing grades (A‟s, B‟s and C‟s). Additionally, researching what Asian students are
83
doing differently or what makes them different in their approaches to online DE
classrooms may be helpful. The assumption should be made that all students want to
succeed, but with the realization that currently within this education system, many are not
persisting and/or graduating, and thus research offers the opportunity for discussion as to
why this is occurring (Dowd, 2007; Driscoll, 2007).
Future research could also incorporate a focus on students‟ computer competency.
Utilizing the EDUCAUSE Study of Undergraduate Student and Information Technology
(2008) along with a focus on specific classes might help enlighten the research on what
factors contribute to success or failure for community college students. This type of
research could also look further into how faculty structure their classrooms, in terms of
successful curricular aspects in the F2F classroom translated into effective methods in the
online DE classroom.
Another possible focus or study could include services and tutoring that
incorporate individual students‟ aptitude in online classes with an educational psychology
perspective. In this way, there could be a component of the learning that instructs the
students in the development of self-regulation skills (Karabenick, 2004; Ormrod, 2008).
Future research into communication and interpersonal relationships (as introduced in
chapter two, literature review) should be applied to online versus F2F self motivation of
students. Future studies could look into to peer-to-peer connection in online classrooms,
and also communication patterns in online DE that help promote academic achievement
through interpersonal relationships. This would further the work of Astin (1993), Tinto
(1993), and others when looking at interaction and student satisfaction, and success in
college as related to these online DE technologies. These future studies, as part of the
84
discussion about communication, could also include methods that take into account
frequency and/ or depth of communication as a factor in success of one method of
classroom versus the other (online versus F2F).
The difference by age is another component relevant to future policies and studies
on the community colleges. The study findings show that younger students generally do
better in online classes compared to the older generations. Implications for higher
education could suggest a possibility for future research in five to ten years looking at
some of the same factors, to again examine grade categories. The results might support
the online classrooms as overwhelmingly the more successful method of instruction.
Again the difficulty for community colleges is that they tend to have a more diverse set of
ages for their students (Sengupta & Jepsen, 2006) so this should be a deciding element of
future studies.
Implications for Practice
Differences in the literature describing minority outcomes can be combined with
the findings of this study to inform future research. Within the California community
college system, programs such as Extended Opportunity Programs and Services (EOPS)
seek to assist students with low-income levels and basic skills statuses to achieve transfer
to four-year institutions (California Community Colleges Chancellor‟s Office, 2010). The
findings of this study could specify that a possibility for additional assistance to the
EOPS student would be that instead of the additional counseling for class placement and
tutoring by course (as currently mandated for EOPS) that other options should be
examined that could be more effective for today‟s students. The services that EOPS
students receive should re-focus on education success skills. For example, extra time
85
spent on self-regulation, training on utilization of the online classroom and other methods
of college success may contribute to increased student learning outcomes and successful
completion of the general education requirements for transfer.
Future policies might focus on success measures and could examine EOPS-
specific student populations to see if study skills (methods/ interventions/ etc.) like the
SQ3R method of study (Robinson, 1970) are effective. This style of learning incorporates
a way for students to study that sets up a specific pattern of surveying the work, setting
up questions, reading, reciting and reviewing their answers. This teaching style could
assist in increasing a student‟s grades from the F levels to the A, B, and C (passing)
levels by offering them a structured method for studying the complex texts that are
utilized in college classrooms. With current cuts to these categorical programs of 60% in
the 2009-2010 academic year (California Community Colleges Chancellor‟s Office,
2010) and additional cuts proposed in the 2010-2011 academic year, research that
supports success measures and shows real outcomes for students and the completion of
transfer to a four year school, might help to refocus cost cutting measures away from
these minority-serving programs.
Another practice that could increase student outcomes, are orientations for college
freshmen that concentrate solely on expectations and experiences of the online college
classroom environment (Moore, Shulock, Ceja & Lang, 2007). These orientations could
include instruction focused on use of the online education software and reminders of
campus resources that offer assistance (both online and in-person). These orientations
could be offered as online tutorials, a for-credit one-unit course, or as part of the
scheduled college orientation sessions.
86
Conclusion
This study looked for a difference in grades earned by students to see if students‟
achievement in online classes was impacted by their student characteristics. Overall, this
study showed that there were no significant differences found based solely on the
comparisons of pass versus fail. This would suggest that the promises of online learning,
to revolutionize students‟ learning experience, has not yet been fully realized. In fact
some students are struggling in the online learning world and doing quite poorly versus
what they might achieve in a F2F classroom. Online learning promises increased
personalization of the learning environment for the student in order to assist them in
developing the best possible scenario for their education. However, despite these
possibilities, the faculty (as demonstrated via the survey) still communicate with students
in the same manner no matter what format the classroom instruction is offered. Online
learning may still be a promising endeavor, but at this juncture in time it is still in its
infancy, as a true curricular revolution, and there is a lot of work that needs to be done at
institutions and by faculty to make online learning effective for the students. Further
research should focus on curricular changes that can fully realize the online learning
promise. These studies should also incorporate how best to implement these in an easy
manner so that faculty can impart subject material and students can learn the material
without either group having to grapple with the technology competency curve.
The community colleges systems will need to further define their directions and
look into selecting methods of delivery by subject and class type. The differences in age
grade attainment in the online DE versus F2F classes added to the contributing factor that
many of the students in the older generations are looking for enrichment opportunities,
87
versus a concentration on transfer, could lead community colleges to redefine their
scheduling priorities. One idea would be classes aimed at transfer being online while
enrichment-focused classes would continue to be offered in the F2F format. But again
further research should focus on these issues in order to inform community college policy
statements.
Finally future research within the lexicon of community colleges should continue
to utilize the focus that has been presented through Bensimon‟s “diversity scorecard”
(2004, 2007) and Wassmer, Moore and Shulock‟s (2003, 2004) discussions of race and
transfer. This is a must in order to better serve the students in the higher education system
and adjust the environment to help mitigate their particular risk factors. The approaches
for bringing about positive policy suggestions and changes dictates that policy should
acknowledge these inequities and do its best to accommodate the learning environment
appropriately.
88
References
Alkin, M.C. (1973). Theoretical framework for the analysis of curriculum and
instructional reform. International Review of Education 19(2), 195-207.
Alkin, M.C. (1973/74). Evaluating “curriculum” and “instruction.” Curriculum Theory
Network 4(1), 43-51.
Allen, I. E., & Seaman, J. (2007). Online nation: Five years of growth in online learning.
Needham, MA: Sloan-C.
Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United
States, 2008. Needham, MA: Sloan-C.
American Association of Community Colleges. (2009). Community College Fast Facts.
Retrieved April 16, 2009, from http://www2.aacc.nche.edu/research/home.htm
Astin, A. W. (1993). What matters in college? Four critical years revisited. San
Francisco: Jossey-Bass.
Bailey, T., & Morest, V.S., Eds. (2006). Defending the Community College Equity
Agenda. Baltimore, MD: The Johns Hopkins Press.
Bata-Jones, B., & Avery, M. (2004). Teaching pharmacology to graduate nursing
students: Evaluation and comparison of web-based and face-to-face methods.
Journal of Nursing Education 43(4), 185-189.
Bensimon, E. M. (2004, January/ February). The diversity scorecard: A learning
approach to institutional change. Change, 45-52.
Bensimon, E. M. (2005). Closing the achievement gap in higher education: An
organizational learning perspective. New Directions for Higher Education, 131,
99-111.
Bensimon, E.M. (2007). Presidential address: The underestimated significance of
practitioner knowledge in the scholarship on student success. The Review of
Higher Education 30(4), 441-469.
Bensimon, E.M., Harris, III, F., & Rueda, R. (2007). The Mediational Means of Enacting
Equity-Mindedness among Community College Practitioners: Presentation at the
Association for the Study of Higher Education. Louisville, KY: University of
Southern California.

89
Berkner, L., & Choy, S. (2008). Descriptive summary of 2003-04 beginning secondary
students: Three years later. Jessup, MD: National Center for Educational
Statistics.
Bernard, R.M., Abrami, P.C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., Wallet,
P.A., Fiset, M., & Huang, B. (2004). How does distance education compare with
classroom instruction? A meta-analysis of the empirical literature. Review of
Educational Research 74(3), 379-439.
Biemiller, L. (2009, March 27). Bursting at the seams. Chronicle of Higher Education.
Retrieved April 16, 2009, from
http://chronicle.com/weekly/v55/i29/29b01101.htm
Black, P., & William, D. (1998). Assessment and classroom learning. Assessment in
Education 5(1), 7-74.
Bloom, L.Z. (1997). Why I (used to) hate to give grades. College Composition and
Communication 48(3), 360-371.
Bradley, P. (2008, December 1). Squeezed. Community College Week. Retrieved April
16, 2009, from http://www.ccweek.com
Brown, B.W., & Liedholm, C.E. (2002). Can web courses replace the classroom in
principles of microeconomics? The American Economic Review 92(2), 444-448.
Buckley, K.M. (2003). Evaluation of classroom-based, web-enhanced, and web-based
distance learning nutrition courses for undergraduate nursing. Journal of Nursing
Education 42(8), 367-370.
Bunch, G.C. (2008). Language minority students and California community colleges:
Current issues and future directions. California Community College Collaborative
(C4)1, 2-18.
Burbules, N.C., & Callister, Jr., T.A. (2000, February). Universities in transition: The
promise and the challenge of new technologies. Teachers College Record
102(2), 271-293.
California Community Colleges Chancellor‟s Office. (2009). Student Financial
Assistance Programs and Forms. Retrieved June 28, 2009, from
http://www.cccco.edu
California Community Colleges Chancellor‟s Office. (2008). DataMart: Student
Headcount Data 2007-2008. Retrieved July, 3, 2009, from
http://www.misweb.cccco.edu
90
Carnevale, A.P., & Fry, R.A. (2002). The demographic window of opportunity: College
access and diversity in the new Century. In Condition of access: Higher education
for lower income students. Heller, D.E., (Ed.) (pp. 138-151).Wesport, CT:
Praeger Publishers (ACE/Oryx Series in Higher Education).
Cavanagh, S. (2004, June 9). Kirst comments on UC system‟s temporary deferrals as a
result of race-neutral admissions policy. Education Week. Retrieved April 16,
2009, from http://www.edweek.org
Caywood, K., & Duckett, J. (2003). Online vs. on-campus learning in teacher education.
Teacher Education and Special Education 26(2), 98-105.
Chavez, L. (2008). Untapped potential: Latinos and California community colleges: A
statistical portrait of transfer preparation. CLPR: Center for Latino Policy
Research at the University of California Berkeley. Retrieved November 21, 2009,
from http://www.escholarship.org/uc/item/6dz92825
Christensen, C.M., Horn, M.B., & Johnson, C.W. (2008). Disrupting Class: How
Disruptive Innovation Will Change the Way the World Learns. New York:
McGraw-Hill.
California Community Colleges Chancellor‟s Office. (2010). Extended Opportunity
Programs and Services (EOPS). Retrieved April 22, 2010, from
http://cccco.edu/ChancellorsOffice/Divisions/StudentServices/EOPS/tabid/703/
Default.aspx
Chickering, A. (1969). Education and identity. San Francisco: Jossey-Bass.
Community College League of California. (2009). Fast Facts 2009. Retrieved April 16,
2009, from http://www.ccleague.org/files/public/FastFacts09.pdf
Community College League of California. (2010). Fast Facts 2010. Retrieved May 1,
2010, from http://faccc.org/images/FF2010-College-Stats.pdf
Conway, K.M. (2009). Exploring persistence of immigrant and native students in an
urban community college. The Review of Higher Education 32(3), 321-352.
Cowan, J., George, J.W., & Pinheiro-Torres, A. (2004). Alignment of developments in
higher education. Higher Education 48, 439-459.
Cox, B. E. & Orehovec, E. (2007). Faculty-student interaction outside the classroom: A
typology from a residential college. The Review of Higher Education 30(4), 343-
362.

91
Cox, R.D. (2005). Online education as institutional myth: Rituals and realities at
community colleges. Teachers College Record 107(8), 1754-1787.
Dellana, S.A., Collins, W.H., & West, D. (2000). On-line education in a management
science course – Effectiveness and performance factors. Journal of Education for
Business 76(1), 43-47.
Dolan, T. (2005). The community college crisis: How serious? The Education Digest, 50-
54. Retrieved April 16, 2009, from http://www.eddigest.com
Dougherty, K.J. (1994). Contradictory College: The Conflicting Origins, Impacts, and
Futures of the Community College. Albany, NY: State University of New York
Press.
Douglass, J.A. (2000). The California Idea and American Higher Education. Stanford,
CA: The Stanford University Press.
Dowd, A.C. (2007). Community colleges as gateways and gatekeepers: Moving beyond
the access “saga” toward outcome equity. Harvard Educational Review, 77(4),
407 – 419.
Driscoll, A.K. (2007). Beyond Access: How the First Semester Matters for Community
College Students’ Aspirations and Persistence. Berkeley, CA: Policy Analysis for
California Education.
EDUCAUSE. (2008). The ECAR Study of Undergraduate Students and
Information Technology. Retrieved March 15, 2010 from
http://net.educause.edu/ir/library/pdf/ers0808/rs/ers0808w.pdf
Enrollments up at public universities, colleges. (2008, October 27). Business Week
25(12), 83.
Faux, T.L., & Black-Hughes, C. (2000). A comparison of using the Internet versus
lectures to teach social work history. Research on Social Work Practice 10(4),
454-466.
Fisher, M. (2009, January 12). Calif. Budget cuts battering 2-year colleges. Community
College Week. Retrieved April 16, 2009, from http://www.ccweek.com
Fusani, D. S. (1994). “Extra-class” communication: Frequency, immediacy, self-
disclosure, and satisfaction in student-faculty interaction outside the classroom.
Journal of Applied Communication Research 22(3), 232-255.

92
Gatz, L. B., & Hirt, J. B. (2000). Academic and social integration in cyberspace: Students
and e-mail. The Review of Higher Education 23(3), 299-318.
Hayward, G.C., Jones, D.P., McGuinness, Jr., A.C. & Timar, A. (2004). Ensuring Access
With Quality to California’s Community Colleges. Retrieved November 21, 2009,
from the William and Flora Hewlett Foundation Web site
http://www.highereducation.org/reports/hewlett
Heckman, R., & Annabi, H. (2006). A content analytic comparison of learning processes
in online and face-to-face case study discussions. Journal of Computer-Mediated
Communication 10(2). Retrieved February 5, 2009, from Interscience Wiley.
Hirschheim, R. (2005). The Internet based education bandwagon: Look before you leap.
Communications of the ACM 48(7), 97-101.
Hoachlander, G., Sikora, A.C., & Horn, L. (2003). Community College Students: Goals,
Academic Preparation, and Outcomes. Washington, D.C.: National Center for
Educational Statistics. Retrieved April 22, 2009, from
http://nces.ed.gov/pubs2003/2003164.pdf
Holland, G. (2009, February 3). University cuts worsen strain on 2-year colleges;
Demand is likely to rise for a system „near the breaking point.‟ Los Angeles
Times, p. A1. Retrieved April 16, 2009, from ProQuest database.
Jennings, S.E., & Bayless, M.L. (2003). Online vs. traditional instruction: A comparison
of student success. The Delta Pi Epsilon Journal XVL(3), 183-190.
Johnson, H. (2009). Educating California Choices for the Future. San Francisco, CA:
Public Policy Institute of California.
Johnson, H., & Segupta, R. (2009). Closing the Gap: Meeting California’s Need for
College Graduates. San Francisco, CA: Public Policy Institute of California.
Retrieved January 22, 2010, from http://www.ppic.org
Kane, T.J., & Rouse, C.E. (1999). The community college: Educating students at the
margin between college and work. Journal of Economic Perspectives 13(1), 63-
84.
Karabenick, Stuart A. (2004) Perceived achievement goal structure and college student
help seeking. Journal of Educational Psychology 96(3), 569-581.
Karlberg, A.M. (2008). Harnessing assessment: Pulling together, administrators, faculty,
and staff can improve student learning. Tribal College 19(4), 24-27.

93
Keefe, T.J. (2003). Using technology to enhance a course: The importance of interaction.
Educause Quarterly 26(1), 24-34.
Keegan, D. (1998). The two modes of distance education. Open Learning: The Journal of
Open and Distance Education 13(3), 43-47.
Komarraju, M., Musulkin, S., & Bhattacharya, G. (2010). Role of student-faculty
interactions in developing college students‟ academic self-concept, motivation
and achievement. Journal of College Student Development 51(3), 332-342.
Leasure, A.R., Davis, L., & Thievon, S.L. (2000). Comparison of student outcomes and
preferences in a traditional vs. World Wide Web-based baccalaureate nursing
research course. Journal of Nursing Education 39(4), 149-154.
Lim, J., Kim, M, Chen, S.S., & Ryder, C.E. (2008). An empirical investigation of student
achievement and satisfaction in different learning environments. Journal of
Instructional Psychology 35.2, 113-119.
Lucas, C.J. (2006). American Higher Education: A History (2
nd
ed.). New York: Palgrave
MacMillan.
Maki, R.H., Maki, W.S., Patterson, M., & Whittaker, P.D. (2000). Evaluation of a web-
based introductory psychology course: I. learning and satisfaction in on-line
versus lecture courses. Behavior Research Methods, Instruments, & Computers
32(2), 230-239.
Mangan, K. (2008, November 28). Rising enrollments buoy some colleges, burden
others. Chronicle of Higher Education. Retrieved April 16, 2009, from
http://chronicle.com/weekly/v55/i14/14a00103.htm
Mayadas, A. F., Bourne, J. & Bacsich, P. (2009). Online Education Today. Science 323,
85-89.
Mayer, S.E. (1999). Trends in the economic well-being and life chances of America‟s
children. Sage Family Studies Abstracts 21(1), 49-69.
Melguizo, T. (2009). Are community colleges an alternative path for Hispanic students to
attain a Bachelor‟s degree? Teachers College Record 111(1), 90-123.
Melguizo, T, Hagedorn, L.S. & Cypers, S. (2008). Remedial/developmental education
and the cost of community college transfer: A Los Angeles county sample. The
Review of Higher Education 31, (4), 401-431.

94
Melton, R. (1996). Learning outcomes for higher education: Some key issues. British
Journal of Educational Studies 44(4), 409-425.
Mentzer, G.A., Cryan, J.R., & Teclehaimanot, B. (2007). Two peas in a pod? A
comparison of face-to-face and web-based classrooms. Journal of Technology and
Teacher Education 15(2), 233-246.
Merisotis, J.P., & Phipps, R.A. (1999). What‟s the difference (college-level distance and
classroom-based education). Change 31(3), 12-18.
Mikulecky, L. (1998). Diversity, discussion, and participation: Comparing web-based and
campus-based adolescent literature classes. Journal of Adolescent and Adult
Literacy 42(2), 84-97.
Moore, C., Shulock, N., Ceja, M., & Lang, D. M. (2007). Beyond the Open Door:
Increasing Student Success in the California Community Colleges. Sacramento,
CA: Institute for Higher Education Leadership & Policy, California State
University, Sacramento.
Naderi, H., Abdullah, R., Hamid, T. A., & Sharir, J. (2008). Intelligence and gender as
predictors of academic achievement among undergraduate students. European
Journal of Social Sciences 7(2), 199-207.
Nagy, P. (2000). The three roles of assessment: Gatekeeping, accountability, and
instructional diagnosis. Canadian Journal of Education 25(4), 262-279.
National Center for Educational Statistics. (2003). Distance Education at Degree-
Granting Postsecondary Institutions: 2000-2001. U.S. Department of Education.
Retrieved January 24, 2010, from http://nces.ed.gov
National Center for Educational Statistics. (2008). Fast Facts. U.S. Department of
Education. Retrieved April 1, 2009, from http://nces.ed.gov
Olsen, L. (2003). Preface. In B. Woodlief, C. Thomas, & G. Orozco (Eds.), California’s
gold: Claiming the promise of diversity in our community colleges (pp. 7).
Oakland, CA: California Tomorrow.
Ormrod, J.E. (2008). Educational Psychology: Developing Learners. 6
th
Edition. Upper
Saddle River, N.J.: Merrill.
Parkinson, D., Greene, W., Kim, Y., & Mariono, J. (2003). Emerging themes of student
satisfaction in a traditional course and a blended distance course. TechTrends
47(4), 22-28.

95
Perry, P. (2002). Study Session on Basic Skills: Background Data. Sacramento, CA: The
Chancellor‟s Office California Community Colleges. Retrieved November 21,
2009, from http://www.cccco.edu
Phipps, R.A. (1998). Assuring Quality in Distance Learning: A Preliminary Review.
Washington, D.C.: Institute for Higher Education.
Phipps, R.A., Wellman, J.V., & Merisotis, J.P. (1998). Assuring Quality in Distance
Learning: A Preliminary Review. Washington, D.C.: Council for Higher
Education Accreditation.
Price, T. (2003, December 5). Rising college costs. CQ Researcher 13, 1013-1044.
Retrieved April 16, 2009, from CQ Researcher Online database.
Reardon-Anderson, J., Capps, R., & Fix, M. (2002, November). The health and well-
being of children in immigrant families. New Federalism: National Survey of
America’s Families (Series B, No. B-52). Washington, D.C.: The Urban Institute.
Ridley, D.R., & Husband, J.E. (1998). Online education: A study of academic rigor and
integrity. Journal of Instructional Psychology 25(3), 184-188.
Robinson, F. P. (1970). Effective Study. New York: Harper & Row.
Russell, T.L. (1999). The No Significant Difference Phenomenon as Reported in 355
Research Reports, Summaries and Papers. Raleigh, NC: North Carolina State
University.
Russell, T.L. (2009). No Significant Difference Phenomenon. Retrieved April 16, 2009,
from http://www.nosignificantdifference.org

Santos, J.L., & Melguizo, T. (2008). Increasing baccalaureate attainment rates using
California community college transfers: An exploration of cost-effectiveness.
California Community College Collaborative (C4) 1, 34-44.

Sengupta, R. & Jepsen, C. (2006). California‟s community college students. California
Counts: Population Trends and Profiles 8, (2), 1-22.

Shieh, D. (2009). Professors regard online instruction as less effective than classroom
learning. Chronicle of Higher Education. Retrieved February 10, 2009, from
http://chronicle.com/daily/2009/02/11232n.htm

Shulock, N., & Moore, C. (2005). Diminished access to the Baccalaureate for low-
income and minority students in California: The impact of budget and capacity
constraints on the transfer function. Educational Policy 19(2), 418-442.

96
Spann, Jr., M.G. (2000). Remediation: A Must for the 21
st
-Century Learning Society.
Denver, CO: Education Commission of the States.

Stanton-Salazar, R.D. (1997). A social capital framework for understanding the
socialization of racial minority children and youths. Harvard Educational Review
67(1), 1-41.

Steinberg, J. (2009). University of California system reports rise in applications, not
admissions. The New York Times, 7 April 2009. Retrieved June 19, 2009, from
http://www.nytimes.com

Supiano, B. (2008). 2-year-college students miss out on aid – because many don‟t apply.
Chronicle of Higher Education, 17 October 2008. Retrieved June 19, 2009, from
http://chronicle.com/weekly/v55/i08/08a02602.htm

Tallent-Runnels, M.K., Thomas, J.A., Lan, W.Y., Cooper, S., Ahem, T. C., Shaw, S. M.,
et al. (2006). Teaching courses online: A review of the research. Review of
Educational Research 76(1), 93-126.

Thomas, P.Y. (2008). Managing the change towards a blended learning model at the
University of Botswana. NAWA: Journal of Language and Communication,106-
125.

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition
(2
nd
ed.). Chicago: University of Chicago Press.

University of California. (2010). Student and Workforce Data. Retrieved January 22,
2010, from http://www.ucop.edu/news/studstaff.html

Western Association of Schools and Colleges. (2009). Accreditation Standards.
Retrieved June 26, 2009, from http://www.wascweb.org

Wheeler, D. (2008, February 8). Colleges prepare for fiscal downturn. Chronicle of
Higher Education. Retrieved April 16, 2009, from
http://chronicle.com/weekly/v54/i22/22a00103.htm

Willingham, W., Pollack, J., & Lewis, C. (2002). Grades and test scores: Accounting for
observed differences. Journal of Educational Measurement 39(1), 1-37.

Woodlief, B., Thomas, C., Orozco, G., & Dowell, C. (2003). California’s Gold: Claiming
the Promise of Diversity in our Community Colleges. Oakland, CA: California
Tomorrow.

97
Woo, M.A., & Kimmick, J.V. (2000). Comparison of Internet versus lecture instructional
methods for teaching nursing research. Journal of Professional Nursing 16(3),
132-139.

Wright, S.W. (2000, April 21). Community colleges. CQ Researcher 10, 329-352.
Retrieved April 16, 2009, from CQ Researcher Online database.

Yorke, M. (2003). Formative assessment in higher education: Moves towards theory and
the enhancement of pedagogic practice. Higher Education 45(4), 477-501.

Yucha, C., & Princen, T. (2000). Insights learned from teaching pathophysiology on the
World Wide Web. Journal of Nursing Education 39(2), 68-72.

Yudko, E., Hirokawa, R., & Chi, R. (2008). Attitudes, beliefs, and attendance in a hybrid
course. Computers & Education 50, 1217-1227.

Zemsky, R., & Massy, W.F. (2004). Thwarted innovation: What happened to e-learning
and why. Philadelphia, PA: University of Pennsylvania.

98
Appendix A: Quantitative Data

Overall Comparison of Online DE to F2F Classes

T-Test: t (47,201) = 3.090, p = .002, r (47,201) = -0.14
N Mean Standard Deviation
GPA units: online 15239 2.3977 1.51837
GPA units: face to
face 31964 2.3535 1.42460

Chi-Squared:
2
(1)
= 5.504, p = .019, r
s
(57,657) = .010
Method of Instruction
Online Face to Face
Grade of C or better 11052 23664
Std. Residual -1.2 .8
Grade of D or F 7518 15425
Std. Residual 1.5 -1.0

Four Degrees of Freedom Chi-Squared:
2
(4)
= 338.883, p < .001, r
s
(47,201) = -.027
Method of Instruction
Online Face to Face
Letter Grade: A 5033 8758
Std. Residual 8.7 -6.0
Letter Grade: B 3469 7972
Std. Residual -3.7 2.6
Letter Grade: C 2496 6878
Std. Residual -9.6 6.7
Letter Grade: D 1008 2522
Std. Residual -3.9 2.7
Letter Grade: F 3233 5834
Std. Residual 5.7 -3.9

99
Comparison of Online DE to F2F Classes for Low Income Students

T-Test: t (17,768) = 1.396, p = .163, r (17,768) = -.010
N Mean Standard Deviation
GPA units: online 6255 2.4775 1.50368
GPA units: face to
face 11515 2.4461 1.39266

Chi-Squared:
2
(1)
= 8.209, p = .004, r
s
(21,715) = .019
Method of Instruction
Online Face to Face
Grade of C or better 4648 8816
Std. Residual -1.4 1.0
Grade of D or F 3007 5246
Std. Residual 1.8 -1.3

Four Degrees of Freedom Chi-Squared:
2
(4)
= 166.943, p < .001, r
s
(17,768) = -.028
Method of Instruction
Online Face to Face
Letter Grade: A 2231 3375
Std. Residual 5.8 -4.3
Letter Grade: B 1372 2929
Std. Residual -3.6 2.7
Letter Grade: C 1017 2495
Std. Residual -6.2 4.6
Letter Grade: D 423 890
Std. Residual -1.8 1.3
Letter Grade: F 1212 1826
Std. Residual 4.4 -3.2

100
Comparison of Online DE to F2F Classes for Basic Skills Students

T-Test: t (15,814) = 5.774, p < .001, r (15,814) = -.046
N Mean Standard Deviation
GPA units: online 3895 2.6221 1.47202
GPA units: face to
face 11921 2.4726 1.3793

Chi-Squared:
2
(1)
= .002, p = .963, r
s
(19,002) = .000
Method of Instruction
Online Face to Face
Grade of C or better 3026 9233
Std. Residual .0 .0
Grade of D or F 1667 5078
Std. Residual .0 .0

Four Degrees of Freedom Chi-Squared:
2
(4)
= 194.087, p < .001, r
s
(15,814) = -.065
Method of Instruction
Online Face to Face
Letter Grade: A 1557 3539
Std. Residual 8.5 -4.9
Letter Grade: B 857 3069
Std. Residual -3.5 2.0
Letter Grade: C 589 2601
Std. Residual -7.0 4.0
Letter Grade: D 236 911
Std. Residual -2.8 1.6
Letter Grade: F 656 1801
Std. Residual 2.1 -1.2

101
Comparison of Online DE to F2F Classes by Student Age Categories

T-Test: Under 21 years old: t (21,893) = 2.728, p = .006, r (21,893) = -.018
N Mean Standard Deviation
GPA units: online 4614 2.2334 1.54586
GPA units: face to
face 17281 2.1676 1.43003

T-Test: 21 to 25 years old: t (15,818) = -3.987, p < .001, r (15,818) = .032
N Mean Standard Deviation
GPA units: online 5889 2.3388 1.51449
GPA units: face to
face 9931 2.4332 1.39386
T-Test: 26 to 35 years old: t (5,823) = -7.377, p < .001, r (5,823) = .096
N Mean Standard Deviation
GPA units: online 2929 2.5282 1.49999
GPA units: face to
face 2896 2.8032 1.33968
T-Test: 36 to 45 years old: t (2,022) = -1.753, p = .080, r (2,022) = .039
N Mean Standard Deviation
GPA units: online 1082 2.8457 1.3822
GPA units: face to
face 942 2.9501 1.2837
T-Test: 46 to 55 years old: t (968) = -2.345, p = .019, r (968) = .075
N Mean Standard Deviation
GPA units: online 501 2.8064 1.35663
GPA units: face to
face 469 3.0085 1.32527
T-Test: 56 years old and older: t (291) = -3.302, p = .001, r (291) = .190
N Mean Standard Deviation
GPA units: online 139 2.5683 1.54662
GPA units: face to
face 154 3.1234 1.33003
102

Chi-Squared: Under 21 years old:
2
(1)
= 1.102, p = .294, r
s
(26,768) = .006
Method of Instruction
Online Face to Face
Grade of C or better 3129 12032
Std. Residual -.6 .3
Grade of D or F 2457 9152
Std. Residual .7 -.4
Chi-Squared: 21 to 25 years old:
2
(1)
= 29.643, p < .001, r
s
(19,352) = .039
Method of Instruction
Online Face to Face
Grade of C or better 4213 7595
Std. Residual -2.7 2.1
Grade of D or F 2985 4561
Std. Residual 3.4 -2.6
Chi-Squared: 26 to 35 years old:
2
(1)
= 31.651, p < .001, r
s
(7,085) = .067
Method of Instruction
Online Face to Face
Grade of C or better 2222 2419
Std. Residual -2.3 2.3
Grade of D or F 1343 1103
Std. Residual 3.2 -3.2
Chi-Squared: 36 to 45 years old:
2
(1)
= 8.765, p = .003, r
s
(2,489) = .059
Method of Instruction
Online Face to Face
Grade of C or better 893 819
Std. Residual -1.1 1.2
Grade of D or F 456 323
Std. Residual 1.7 -1.8

103

Chi-Squared: 46 to 55 years old:
2
(1)
=.373, p = .541, r
s
(1,160) = .018
Method of Instruction
Online Face to Face
Grade of C or better 424 409
Std. Residual -.2 .2
Grade of D or F 174 155
Std. Residual .4 -.4
Chi-Squared: 56 years old and older:
2
(1)
= 9.724, p = .002, r
s
(364) = .163
Method of Instruction
Online Face to Face
Grade of C or better 106 142
Std. Residual -1.3 1.2
Grade of D or F 71 47
Std. Residual 1.8 -1.8

Four Degrees of Freedom Chi-Squared: Under 21 years old:
2
(4)
= 179.998, p < .001, r
s
(21,893) = -.028
Method of Instruction
Online Face to Face
Letter Grade: A 1344 3820
Std. Residual 7.8 -4.0
Letter Grade: B 1014 4207
Std. Residual -2.6 1.3
Letter Grade: C 761 3997
Std. Residual -7.6 3.9
Letter Grade: D 365 1564
Std. Residual -2.1 1.1
Letter Grade: F 1130 3693
Std. Residual 3.6 -1.8

104

Four Degrees of Freedom Chi-Squared: 21 to 25 years old:
2
(4)
= 118.541, p < .001, r
s
(15,818) = .018
Method of Instruction
Online Face to Face
Letter Grade: A 1818 2844
Std. Residual 2.0 1.5
Letter Grade: B 1346 2580
Std. Residual -3.0 2.3
Letter Grade: C 1026 2158
Std. Residual -4.6 3.6
Letter Grade: D 411 732
Std. Residual -.7 .5
Letter Grade: F 1288 1617
Std. Residual 6.3 -4.8

Four Degrees of Freedom Chi-Squared: 26 to 35 years old:
2
(4)
= 71.047, p < .001, r
s
(5,823) = .084

Method of Instruction
Online Face to Face
Letter Grade: A 1076 1213
Std. Residual -2.2 2.2
Letter Grade: B 680 723
Std. Residual -1.0 1.0
Letter Grade: C 456 470
Std. Residual -.4 .4
Letter Grade: D 149 157
Std. Residual -.4 .4
Letter Grade: F 568 333
Std. Residual 5.4 -5.4

105

Four Degrees of Freedom Chi-Squared: 36 to 45 years old:
2
(4)
= 7.052, p = .133, r
s
(2022) = .025
Method of Instruction
Online Face to Face
Letter Grade: A 493 432
Std. Residual -.1 .1
Letter Grade: B 260 257
Std. Residual -1.0 1.1
Letter Grade: C 136 122
Std. Residual -.2 .2
Letter Grade: D 55 36
Std. Residual .9 -1.0
Letter Grade: F 138 95
Std. Residual 1.2 -1.3

Four Degrees of Freedom Chi-Squared: 46 to 55 years old:
2
(4)
= 10.981, p = .027, r
s
(968) = .093
Method of Instruction
Online Face to Face
Letter Grade: A 213 247
Std. Residual -1.6 1.6
Letter Grade: B 123 96
Std. Residual .9 -1.0
Letter Grade: C 83 58
Std. Residual 1.2 -1.2
Letter Grade: D 19 19
Std. Residual -.1 .1
Letter Grade: F 63 49
Std. Residual .7 -.7

106

Four Degrees of Freedom Chi-Squared: 56 years old and older:
2
(4)
= 16.841, p = .002, r
s
(291) = .197
Method of Instruction
Online Face to Face
Letter Grade: A 57 92
Std. Residual -1.6 1.5
Letter Grade: B 28 25
Std. Residual .6 -.5
Letter Grade: C 19 19
Std. Residual .2 -.2
Letter Grade: D 7 0
Std. Residual 2.0 -1.9
Letter Grade: F 28 18
Std. Residual 1.3 -1.3

Comparison of Online DE to F2F Classes by Gender

T-Test – Females: t (25,190) = -1.450, p = .147, r (25,190) = .009
N Mean
Standard
Deviation
GPA units: online 9045 2.4334 1.50706
GPA units: face to
face 16147 2.461 1.41299
T-Test – Males: t (21,414) = 5.000, p < .001, r (21,414) = -.034
N Mean
Standard
Deviation
GPA units: online 6011 2.3432 1.53142
GPA units: face to
face 15405 2.2323 1.42863

Chi-Squared – Females:
2
(1)
= 23.878, p < .001, r
s
(30,447) = .028
Method of Instruction
Online Face to Face
Grade of C or better 6635 12305
Std. Residual -2.4 1.8
Grade of D or F 4351 7158
Std. Residual 3.1 -2.3

107
Chi-Squared – Males:
2
(1)
= .734, p = .391, r
s
(26,528) = -.005
Method of Instruction
Online Face to Face
Grade of C or better 4286 11025
Std. Residual .5 -.3
Grade of D or F 3087 8132
Std. Residual -.6 .3

Four Degrees of Freedom Chi-Squared – Females:
2
(4)
= 137.112, p < .001, r
s
(25,190) = -.004
Method of Instruction
Online Face to Face
Letter Grade: A 3034 4937
Std. Residual 3.2 -2.4
Letter Grade: B 2136 4091
Std. Residual -2.1 1.6
Letter Grade: C 1433 3250
Std. Residual -6.1 4.5
Letter Grade: D 600 1216
Std. Residual -2.0 1.5
Letter Grade: F 1842 2653
Std. Residual 5.7 -4.2
Four Degrees of Freedom Chi-Squared – Males:
2
(4)
= 219.497, p < .001, r
s
(21,414) = -.046
Method of Instruction
Online Face to Face
Letter Grade: A 1931 3673
Std. Residual 9.0 -5.6
Letter Grade: B 1294 3774
Std. Residual -3.4 2.1
Letter Grade: C 1039 3549
Std. Residual -6.9 4.3
Letter Grade: D 401 1277
Std. Residual -3.2 2.0
Letter Grade: F 1346 3132
Std. Residual 2.5 -1.6

108
Comparison of Online DE to F2F Classes by Student Ethnicity

T-Test: Asians and Filipinos Only:
t (14,246) = 7.463, p < .001, r (14,246) = -.062
N Mean Standard Deviation
GPA units: online 5241 2.7829 1.41333
GPA units: face to
face 9007 2.6041 1.35848
T-Test: Blacks Only: t (788) = -2.547, p = .011, r (788) = .090
N Mean Standard Deviation
GPA units: online 281 1.4662 1.51649
GPA units: face to
face 509 1.7466 1.46098
T-Test: Hispanics Only: t (8,857) = -2.647, p = .008, r (8,857) = .028
N Mean Standard Deviation
GPA units: online 2065 1.9419 1.53086
GPA units: face to
face 6794 2.0392 1.441
T-Test: Other Ethnicities (Including Native American and Pacific Islander):
t (1,517) = -.989, p = .323, r (1,517) = .025
N Mean Standard Deviation
GPA units: online 479 2.0355 1.61089
GPA units: face to
face 1040 2.1183 1.46911
T-Test: Whites Only: t (18,054) = -2.430, p = .015, r (18,054) = .018
N Mean Standard Deviation
GPA units: online 5804 2.3005 1.5011
GPA units: face to
face 12252 2.3566 1.42351

109

Chi-Squared: Asians and Filipinos Only:
2
(1)
= 4.322, p = .037, r
s
(17,007) = -.016
Method of Instruction
Online Face to Face
Grade of C or better 4275 7222
Std. Residual .9 -.7
Grade of D or F 1959 3553
Std. Residual -1.4 1.0
Chi-Squared: Blacks Only:
2
(1)
= 8.095, p = .004, r
s
(1,056) = .087
Method of Instruction
Online Face to Face
Grade of C or better 135 295
Std. Residual -1.7 1.3
Grade of D or F 251 377
Std. Residual 1.4 -1.1
Chi-Squared: Hispanics Only:
2
(1)
= 18.033, p < .001, r
s
(11,023) = .040
Method of Instruction
Online Face to Face
Grade of C or better 1260 4486
Std. Residual -2.6 1.4
Grade of D or F 1339 3940
Std. Residual 2.7 -1.5
Chi-Squared: Other Ethnicities (Including Native American and Pacific Islander):
2
(1)
= 1.295, p = .255, r
s
(1930) = .026
Method of Instruction
Online Face to Face
Grade of C or better 299 711
Std. Residual -.7 .4
Grade of D or F 295 627
Std. Residual .7 -.5

110
Chi-Squared: Whites Only:
2
(1)
= 10.812, p = .001, r
s
(22,132) = .022
Method of Instruction
Online Face to Face
Grade of C or better 4119 9084
Std. Residual -1.7 1.2
Grade of D or F 2974 5957
Std. Residual 2.1 -1.4

Four Degrees of Freedom Chi-Squared: Asians and Filipinos Only:
2
(4)
= 199.765, p < .001, r
s
(14,246) = -.084
Method of Instruction
Online Face to Face
Letter Grade: A 2331 3056
Std. Residual 7.8 -6.0
Letter Grade: B 1173 2324
Std. Residual -3.2 2.4
Letter Grade: C 730 1814
Std. Residual -6.7 5.1
Letter Grade: D 282 631
Std. Residual -2.9 2.2
Letter Grade: F 725 1182
Std. Residual .9 -.7
Four Degrees of Freedom Chi-Squared: Blacks Only:
2
(4)
= 12.380, p = .015, r
s
(788) = .094
Method of Instruction
Online Face to Face
Letter Grade: A 39 75
Std. Residual -.2 .2
Letter Grade: B 44 103
Std. Residual -1.1 .9
Letter Grade: C 52 115
Std. Residual -1.0 .7
Letter Grade: D 20 50
Std. Residual -1.0 .7
Letter Grade: F 126 166
Std. Residual 2.2 -1.6

111

Four Degrees of Freedom Chi-Squared: Hispanics Only:
2
(4)
= 46.624, p < .001, r
s
(8,857) = .024
Method of Instruction
Online Face to Face
Letter Grade: A 429 1340
Std. Residual .8 -.5
Letter Grade: B 452 1533
Std. Residual -.5 .3
Letter Grade: C 377 1612
Std. Residual -4.0 2.2
Letter Grade: D 184 671
Std. Residual -1.1 .6
Letter Grade: F 623 1638
Std. Residual 4.2 -2.3
Four Degrees of Freedom Chi-Squared: Other Ethnicities (Including Native
American and Pacific Islander):
2
(4)
= 22.725, p < .001, r
s
(1,517) = .015
Method of Instruction
Online Face to Face
Letter Grade: A 132 224
Std. Residual 1.9 -1.3
Letter Grade: B 83 263
Std. Residual -2.5 1.7
Letter Grade: C 84 220
Std. Residual -1.2 .8
Letter Grade: D 30 78
Std. Residual -.7 .5
Letter Grade: F 150 255
Std. Residual 2.0 -1.3

112

Four Degrees of Freedom Chi-Squared: Whites Only:
2
(4)
= 57.900, p < .001, r
s
(18,054) = .010
Method of Instruction
Online Face to Face
Letter Grade: A 1656 3321
Std. Residual 1.4 -1.0
Letter Grade: B 1414 3162
Std. Residual -1.5 1.0
Letter Grade: C 1042 2585
Std. Residual -3.6 2.5
Letter Grade: D 402 933
Std. Residual -1.3 .9
Letter Grade: F 1290 2251
Std. Residual 4.5 -3.1
113
Appendix B: Board of Governors Fee Waiver (BOG): 2008-2009 and 2007-2008

Board of Governors Fee Waiver Program
BOGFW-B
2008-2009 Income Standards

Family Size

2007 Income
1 $15,315
2 $20,535
3 $25,755
4 $30,975
5 $36,195
6 $41,415
7 $46,635
8 $51,855
Each Additional Family Member $ 5,220
These standards are based upon the federal poverty guidelines as published each year by
the US Department of Health and Human Services. Under Title 5 of the California Code
of Regulations, the income standards for the BOGFW program equal 150% of the federal
poverty guidelines for the base year.
These standards are for the 2008-09 academic year and are to be used to determine
BOGFW-B eligibility EFFECTIVE July 1, 2008.

Source: California Community Colleges Chancellor‟s Office. (2008). 2008-2009 BOG
Fee Waiver. Retrieved June 28, 2009 from
http://www.cccco.edu/Default.aspx?TabId=678
114

Board of Governors Fee Waiver Program
BOGFW-B
2007-2008 Income Standards

Family Size

2006 Income
1 $14,700
2 $19,800
3 $24,900
4 $30,000
5 $35,100
6 $40,200
7 $45,300
8 $50,400
Each Additional Family Member $ 5,100

These standards are based upon the federal poverty guidelines as published each year by
the US Department of Health and Human Services. Under Title 5 of the California Code
of Regulations, the income standards for the BOGFW program equal 150% of the federal
poverty guidelines for the base year.
These standards are for the 2007-08 academic year and are to be used to determine
BOGFW-B eligibility EFFECTIVE July 1, 2007.

Source: California Community Colleges Chancellor‟s Office. (2007). 2007- 2008 BOG
Fee Waiver. Retrieved June 28, 2009 from
http://www.cccco.edu/Default.aspx?TabId=678
115
Appendix C:Courses Included in the Study

Accounting:
ACCT 100
ACCT 101
ACCT 102
ACCT 130
Anthropology:
ANTH 100
ANTH 120
Art:
ART 100
Business:
BUS 100
BUS 108
BUS 110
BUS 112
BUS 121
BUS 125
BUS 130
BUS 139
Computer Business Applications:
CBA 101
CBA 120
CBA 150
CBA 155
CBA 156
CBA 160
CBA 161
Criminal Justice:
CJ 123
CJ 140
CJ 141
CJ 142
College Success:
COLL 100
Computer Science:
CS 101
CS 130
CS 175
Counseling:
COUN 104
Digital Arts:
DA 135
DA 150
Environmental Studies:
ES 100
English:
ENGL 010
ENGL 100
116
History:
HIST 140
HIST 170
HIST 175
HIST 190
HIST 195
Health Education:
HLED 100
Management:
MGMT 110
Marketing:
MARKET 121
Math:
MATH 009
MATH 010
MATH 030
MATH 100
Music:
MUS 101
MUS 105
Philosophy:
PHIL 115
Political Science:
POL SC 180
Psychology:
PSYC 100
PSYC 160
PSYC165
PSYC 250
Real Estate:
RE 110
RE 120
RE 130
Sociology:
SOC 100
Spanish:
SPAN 180
SPAN 185

117
Appendix D: Faculty Survey

Interview Protocol:

Dear FACULTY MEMBER NAME -

You are being asked to participate in this research study because you taught COURSE in a
METHOD OF DELIVER (ONLINE VERSUS FACE-TO-FACE) class between the FALL 2007
and SPRING 2009 semesters at Golden West College. Please try to answer the questions as fully
as possible. Your identification and answers will be kept confidential and will only be used for
the purposes of this study.

This research study seeks to examine the effectiveness of the in-person face-to-face classroom
versus the online only classroom. Specifically the community college population will be studied,
due to the risk factors that they may face in succeeding in the higher education environment.

Thank you for your time and consideration in completing this survey.

Please return this to Treisa Cassens.

There are various forms of communication patterns (between the instructor and student) that can
be used within the classroom experience.

Asynchronous communication patterns are ones where the instructor and students are
communicating at different times. Synchronous communication patterns are ones where the
instructor and students are in communication at the same time.

Please answer the questions below concerning the type of communication patterns you utilize
within your classrooms.

1. Asynchronous (please check all methods you utilize on a regular basis)
Email
Discussion Board
File Transfer (ex: PPT Slides)
Streaming Media
Texting
Blogging

2. Synchronous (please check all methods you utilize on a regular basis)
Web-based Chat
Telephone
In-Person Meetings - Outside of Scheduled Class Lecture/Lab Hours
Video Conferencing
Instant Messaging

118
3. Can you please describe why you choose the above communication patterns:

4. What in your experience with the above methods (asynchronous and/or synchronous) in
your class were effective, and why:

5. What in your experience was not effective, and why:

6. Can you describe how you used the above methods of additional instruction (asynchronous
and/or synchronous) for grading purposes:

7. What are your perceptions of your students‟ ability to utilize asynchronous methods
effectively? (please circle the appropriate number)

Very Somewhat Neutral Somewhat Very
Effective Effective Ineffective Ineffective
1…………………2……………………3……………..…4…………....….……5

8. What are your perceptions of your students‟ ability to utilize synchronous methods
effectively? (please circle the appropriate number)

Very Somewhat Neutral Somewhat Very
Effective Effective Ineffective Ineffective
1…………..……2……………………3……………..…4……………….……5

119
Appendix E: Student Counts for Online versus Face-to-Face Enrollments
Total Number of Students Total 62,994

Online 18,776 Face-to-Face 44,218

Low-Income Students Total 23,801

Online 16,053 Face-to-Face 7,748

Basic Skills Students Total 21,292

Online 4,754 Face-to-Face 16,538

Age of Students

Less than 21

Total 29,994

Online 5,674 Face-to-Face 24,320

21-25

Total 20,640

Online 7,248 Face-to-Face 13,392

26-35

Total 7,542

Online 3,602 Face-to-Face 3,940

36-45

Total 2,666

Online 1,366 Face-to-Face 1,300

46-55

Total 1,271

Online 610 Face-to-Face 661

56 and Older

Total 402

Online 178 Face-to-Face 224

Gender of Students

Females

Total 33,372

Online 11,114 Face-to-Face 22,258

Males

Total 28,877

Online 7,449 Face-to-Face 21,428

Ethnicity of Students

Asian/ Filipino

Total 18,238

Online 6,270 Face-to-Face 12,013

Black

Total 1150

Online 394 Face-to-Face 756

Hispanic

Total 12354

Online 2,637 Face-to-Face 9,717

Other (Including Native American/
Pacific Islander) Total 2093

Online 599 Face-to-Face 1,494

White

Total 24238

Online 7,194 Face-to-Face 17,044

Abstract (if available)

Abstract Over the past decade, online classes have become extensively utilized by higher education. Recent literature found, when focusing on upper level courses and four-year college students, that online classes are as effective as face-to-face classes in serving the curricular needs of students. This study sought to enrich research by examining community college students with a further study of student characteristics (age, gender, basic skills, ethnicity, low-income) to see if they contributed to the students’ grades in each delivery method. A faculty survey was used to investigate other possible influences on success. No significant differences were found for the pass/fail comparisons or in faculty choices for communication with students. However, objective measures did show that students achieved increasing numbers of A and F grades in online classes.

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Asset Metadata

Creator Cassens, Treisa Sullivan (author)

Core Title Comparing the effectiveness of online and face-to-face classes among California community college students

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 08/04/2010

Defense Date 06/01/2010

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

Tag California,Community Colleges,OAI-PMH Harvest,online education

Place Name California (states)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hentschke, Guilbert C. (committee chair), Dwyer, David C. (committee member), Sundt, Melora A. (committee member)

Creator Email cassens@usc.edu,tcassens@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3264

Unique identifier UC1176148

Identifier etd-Cassens-3941 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-368243 (legacy record id),usctheses-m3264 (legacy record id)

Legacy Identifier etd-Cassens-3941.pdf

Dmrecord 368243

Document Type Dissertation

Rights Cassens, Treisa Sullivan

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