transcript analysis of des moines area community college

TRANSCRIPT ANALYSIS OF DES MOINES AREA COMMUNITY

COLLEGE STUDENTS’ TRADITIONAL AND ONLINE COURSES

_______________

A Thesis

Presented to the

Faculty of

San Diego State University

_______________

In Partial Fulfillment

of the Requirements for the Degree

Master of Arts in Educational Leadership

with a Specialization in

Student Affairs Postsecondary Education

_______________

by

Crystal L. Dujowich

Summer 2010

iii

Copyright © 2010

by

Crystal L. Dujowich

All Rights Reserved

iv

DEDICATION

To my talented and dedicated committee whose advice and guidance was immeasurable.

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This study promotes digging into the records and letting the transcripts tell the stories

-Hagedorn, L.S

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ABSTRACT OF THE THESIS

Transcript Analysis of Des Moines Area Community College Students’ Traditional and Online Courses

by Crystal L. Dujowich

Master of Arts in Educational Leadership with a Specialization in Student Affairs Postsecondary Education

San Diego State University, 2010

Consistent with their missions, community colleges have generally been open to adopting technologies that show promise of extending educational possibilities for students. In recent years, community colleges have added an array of online courses with the expectation that they would expand access to students who are limited by time, transportation, work, and/or family constraints. The purpose of this study is to analyze the success of online courses in comparison with traditional courses by analyzing the transcripts of students who have enrolled in both traditional and online courses. Specifically, in this study, student transcripts were analyzed from the Des Moines Area Community College (DMACC). Data was collected from the academic years of 2005 to 2009 and disaggregated by demographic and academic information. Findings indicate that significant differences in grade point average (GPA) and course completion ratio (CCR) exist between students’ performance in traditional and online courses. Specifically, students on average attain higher GPAs and CCRs in traditional courses when compared to their traditional courses. Furthermore, students’ demographic categories and course taking behaviors were positively correlated with higher rates of success in online courses.

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TABLE OF CONTENTS

PAGE

ABSTRACT ............................................................................................................................. vi

LIST OF TABLES ................................................................................................................... ix

ACKNOWLEDGEMENTS .......................................................................................................x

CHAPTER

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

Background ..............................................................................................................1

Statement of the Problem .........................................................................................2

Purpose of the Study ................................................................................................4

Theoretical Bases and Organization ........................................................................5

Limitations of the Study...........................................................................................7

Definition of Terms..................................................................................................7

2 REVIEW OF THE LITERATURE ...............................................................................9

Student Demographics ...........................................................................................10

Student Performance ..............................................................................................11

Student Perspectives ..............................................................................................12

Student Retention ...................................................................................................13

Transcript Analysis ................................................................................................14

Summary ................................................................................................................15

3 METHODOLOGY ......................................................................................................16

Design of the Investigation ....................................................................................16

Research Questions ................................................................................................16

Population ..............................................................................................................17

Treatment ...............................................................................................................17

Dependent Variables ..............................................................................................18

Independent Variables ...........................................................................................18

Course Mode ..........................................................................................................19

Data Analysis Procedures ......................................................................................19

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Limitations of the Study.........................................................................................19

4 RESULTS ....................................................................................................................21

Descriptive Analysis ..............................................................................................21

GPA Analysis.........................................................................................................23

Course Completion Ratios (CCR) Analysis ..........................................................26

Summary of Findings .............................................................................................31

5 SUMMARY, CONCLUSION, AND RECOMMENDATIONS .................................34

Statement of the Problem .......................................................................................34

Purpose of the Study ..............................................................................................34

Research Questions ................................................................................................36

Theoretical Bases and Organization ......................................................................36

Limitations of the Study.........................................................................................37

Methodology ..........................................................................................................38

Treatment ...............................................................................................................38

Dependent Variables ..............................................................................................38

Independent Variables ...........................................................................................39

Data Analysis Procedures ......................................................................................40

Discussion ..............................................................................................................40

Summary of Findings .............................................................................................46

Recommendations ..................................................................................................47

REFERENCES ........................................................................................................................50

APPENDIX

A CIP CODE FAMILIES ................................................................................................54

B HAGEDORN'S AGE MODEL ....................................................................................56

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LIST OF TABLES

PAGE

Table 1. Percentage of DMACC Students Who Enrolled in both Online and Traditional Courses ......................................................................................................21

Table 2. Frequency and Percentage of Age Group Distribution ..............................................22

Table 3. Frequency and Percentages of Age Groups Represented in Course Mode Model ...........................................................................................................................23

Table 4. Frequency and Percentage of DMACC Students Receiving Pell Grants by Course Mode ................................................................................................................23

Table 5. DMACC Students’ Mean GPA Scores in Traditional and Web Courses by Demographics ..............................................................................................................25

Table 6. DMACC Students’ Correlation and Paired Difference Statistics in GPA Scores of Traditional and Web Courses by Demographics .........................................26

Table 7. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web GPA’s by CIP Code ............................................................................................27

Table 8. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web GPA’s by Ethnicity .............................................................................................28

Table 9. Comparison of DMACC Students’ Mean Differences in GPA by Ethnicity ............28

Table 10. DMACC Students’ Mean Course Completion Ratios (CCR) in Traditional and Web Courses by Demographics ............................................................................29

Table 11. DMACC Students’ Correlation and Paired Difference Statistics of Course Completion Ratios (CCR) in Traditional and Web Courses by Demographics ..........30

Table 12. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web Course Completion Ratios (CCR) by CIP Code .................................................31

Table 13. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web Course Completion Ratios (CCR) by Ethnicity ..................................................32

Table 14. Comparison of DMACC Students’ Mean Differences in Course Completion Ratios (CCR) by Ethnicity ...........................................................................................32

Table 15. CIP Code Family Chart ............................................................................................55

Table 16. Hagedorn's Age Model Table ..................................................................................57

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ACKNOWLEDGEMENTS

I would like to individually recognize the faculty who served on my committee:

Thank you, Dr. Marilee Bresciani, for serving as my chair and providing me with

your time, critical feedback, and guidance. Your insight and direction has polished my thesis

and shaped my experience.

Thank you, Dr. McFarlane, for your friendship and guidance throughout this

program. Your counsel has been sought in times of need and your support has fueled my

academic success.

Thank you, Dr. Ian Pumpian, for assisting me with this process and your time.

Thank you, Dr. Linda Serra Hagedorn, for without your instruction and personal

guidance this thesis would not have existed. Your esteemed expertise in this particular

subject matter was beyond expectations. On a personal note, I had a wonderful year under

your supervision and care in Iowa. You undoubtedly deserve every accolade that may come

your way.

I would also like to thank Des Moines Area Community College (DMACC) for their

cooperation. In particular, I would like to thank Dr. Joseph Dehart for his time, effort, and

assistance.

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CHAPTER 1

INTRODUCTION

BACKGROUND

The history of online course offerings has been brief in comparison to traditional

course offerings. Since technology has recently permitted the development of online courses,

research on this area only spans thirty-five years (Scigliano, 2000). However, as a form of

distance education, online learning has evolved through the progression of three distinct

generations (Nipper,1989). Nipper (1989) was one of the first researchers to divide these

generations upon the basis of their “development of production, distribution and

communication technologies” (p. 63).

The first generation of distance education was predominantly comprised of

correspondence study (Nipper, 1989). While historians and scholars alike may make

arguments for earlier usages of distance education, this pedagogy was not widely used until

the onset of the Industrial Revolution in the nineteenth century (Sumner, 2000). The first

recorded correspondence course in shorthand was offered by Isaac Pitman in England in

1840 (Verduin & Clark, 1991). Correspondence courses were marked largely by the use of

the postal system to deliver printed materials to the student. This enabled many rural

populations, societies colonized by the British, and women to attain some form of education.

By the beginning of the twentieth century, audiences expanded for correspondence

and distance education courses. Elementary, secondary, postsecondary, and vocational

students had found practical use of the pedagogy (Willis, 1994). In fact, 48 institutions of

higher education were granting doctorates through the implementation of distance learning in

the early twentieth century (Portman, 1978). While growth was not rapid, the use of distance

education continued to steadily increase unchanged until the 1960’s.

The second generation of distance education instituted multi-media education

(Nipper, 1989). Educators utilized radio, recordings, television, and limited computer

technology in their pedagogy (Nipper, 1989). Additionally, it is important to note that these

technologies were used in conjunction with printed materials and did not replace or do away

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with the use of printed media. Perhaps, one of the most significant advances during this time

of distance education was the application of two-way communication (Sumner, 2000).

Previously, distance education through correspondence remained a predominantly one-way

form of communication due to the lag in response time of the postal service (Sumner, 2000).

However, due to the increased support in technology and distance education, students had the

ability to more readily communicate with instructors (Sumner, 2000).

During the 1990’s, the third generation of computer-mediated distance education

experienced rapid growth in technology (Sumner, 2000). Now in the twenty-first century, the

computer remains the predominant and driving technological tool behind distance education

in what Nobel (1995) claims is the second Industrial Revolution. Computer technology has

opened access to a variety of pedagogical techniques including: internet research, chat rooms,

blogs, modular course-work, Webinars, and video conferencing to name a few. In particular,

online courses have seen unprecedented growth in higher education. In 2006, more than 96%

of the largest colleges and universities in the United States offered online courses with 3.1

million U.S. students enrolled in at least one online course during the fall 2005 term (Allen &

Seaman, 2006). Despite its rapid growth, little is known about student success in online

courses (Aragon & Johnson, 2008; Doherty, 2006).

STATEMENT OF THE PROBLEM

While Iowa’s public high school diploma attainment percentage is above the national

average, its college degree attainment falls below both the Midwest and National averages

(Iowa Department of Education, 2008). In the 2007-2008 academic year, 82.8% of all Iowa

postsecondary courses taken by public high school graduates were offered by community

colleges (Iowa Department of Education, 2008). Furthermore, the largest percentage of

public high school graduates reported intending to pursue education at a community college

(Iowa Department of Education, 2008). This percentage has increased since 1998 when

30.9% of public high school graduates were pursuing or intending to pursue higher education

at a community college to 39.2% in 2008 (Iowa Department of Education, 2008). Thus, the

majority of Iowa’s public school system graduates require access to community college

systems. Like many Midwestern states, Iowa has a large rural population. This population in

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particular, has a higher demand for community colleges as compared to Iowa’s urban

population (Iowa Department of Education, 2008).

Consistent with their missions, community colleges have generally been open to

adopting technologies that show promise of extending educational opportunities for students.

In recent years, the growing number of online courses has provided students additional

access particularly, those students who are limited by time, transportation, work, disability

and/or family constraints. The purpose of community colleges is “to provide access to a

postsecondary credential for students who may not otherwise be able to attend college”

(O’Gara, Karp, & Hughes, 2009, p. 3). Thus, community colleges typically have higher

enrollments of disadvantaged students, students of color, and low-income students than their

four-year counter parts (O’Gara et al., 2009). Despite increased efforts in student success,

community college students continue to demonstrate below average achievement. Bailey,

Jenkins, and Leinbach (2005) reported that six years after their first enrollment, 47% of

community college students in their study had dropped out without earning a degree or

credential. More troubling, however, are the numbers indicating drop-out rates are 20%

higher in online courses than in traditional face-to-face courses (Aragon & Johnson, 2008;

Doherty, 2006).

This study analyzed the transcripts and college records of approximately 12,000

students who have enrolled in both online and traditional courses within the academic years

of 2005 to 2009 at Des Moines Area Community College (DMACC). This is a unique group,

as the sample only includes an individual if they have completed, in full, at least one online

course and one traditional course at DMACC. In this manner, students were used as their

own controls. The study used demographic information as recorded in student files as well as

transcript level data containing grades and course types. The specific research questions

were:

1. What are the demographic characteristics of students who enroll in both traditional and online courses? Do they differ in terms of gender, age, ethnicity, or financial aid?

2. Among students who take both online and traditional courses, how do students differ when considering the distribution of web to traditional courses? What factors are responsible for discriminating between students who are traditional dominant, web-balanced, and web-dominant?

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3. Among students who take both online and traditional courses, how do course grade point averages (GPA’s) differ between traditional and online courses? Are there significant differences in GPA based upon student demographic or on course mode?

4. Among students who take both online and traditional courses, how do course completion rates differ between the two course types? Are there significant differences in course completion rates based upon student demographic or on course mode?

The null hypotheses tested for this study was that there are no significant differences

in a student’s ability to perform in an online course verses a traditional course.

PURPOSE OF THE STUDY

In 2006-2007 there were over 6 million students enrolled in community colleges in

the U.S. and 1,045 public community colleges (Provasnik & Planty, 2008). Additionally, in

2005-2006 over 1.5 million students enrolled in community colleges had taken at least one

online course (Allen & Seaman, 2006). In fact, with 3.1 million students enrolled in at least

one online course in the U.S., the majority of online students are enrolled in a community

college (Allen & Seaman, 2006). Thus, due to the overwhelming large percentage of drop-

out rates for online courses (Maxwell, 2003) and the rapidly increasing number of online

courses (Allen & Seaman, 2006), the literature strongly suggests a need to understand

enrollment patterns and characteristics of these students (Aragon & Johnson, 2008; Doherty,

2006; Maxwell, 2003). As Maxwell (2003) summarized, community colleges know little

about the populations who are enrolling in online courses. Failure to find effective ways of

delivering online courses at the community college level will ultimately result in low

retention rates. In states that have large rural populations like Iowa, successful retention in

online courses will be hugely beneficial to students who are at a disadvantage due to physical

access and transportation. Furthermore, Iowa has a particular need to focus on online course

success due to the large percentage of students who intend on attaining postsecondary

training at the community college level. This percentage has increased since 1998 when

30.9% of public high school graduates were pursuing or intending to pursue higher education

at a community college to 39.2% in 2008 (Iowa Department of Education, 2008). These

students now constitute Iowa’s largest population of postsecondary bound students.

Furthermore, the community colleges themselves, believe that the successful implementation

and offering of online courses will keep them accessible and competitive (Cox, 2005). Thus,

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community colleges may be well advised to explore and better understand the students who

are taking online courses.

Some of the earlier studies of online courses success have examined demographic

background (Carr, 2000; Diaz 2002; Nesler, 1999; Parker, 1999). Yet in these studies, online

students were compared to traditional students. Similarly, several studies have already

compared completion and non-completion rates in online courses (Aragon & Johnson, 2008;

Bangurah, 2004; Doherty, 2006). However, in Bangurah’s (2004) and Doherty’s (2006)

studies, comparisons were made not by the individual student but by courses offered by the

same instructor in both traditional and online settings. Additionally, Aragon and Johnson

(2008) designed their study to compare completers and non-completers in only an online

setting. In their review of educational research, Tallent-Runnels et al. (2006) reported on 76

online course research articles. However, despite the numerous surveys, comparisons

between online and traditional courses, and GPA-analyses, none of the research articles

analyzed the transcript data of students who enrolled in both traditional and online courses

using students as their own controls. Thus, the research failed to ascertain if differences were

due to the student attributes that lead to online enrollment or if the differences were due to

course delivery options. Moreover, previous studies have overlooked the individual’s success

and differences in these two settings. This study analyzed the transcripts of students who

have enrolled in both online and traditional courses within the academic year of 2005 until

the fall of 2008. In doing so, the study provided demographic information in the form of

student files which were analyzed with students’ records of grade, course type, major, and

retention.

THEORETICAL BASES AND ORGANIZATION

The framework of this study is based on the work of three scholars. First, the work of

Carol Kozeracki provides a historical framework to the purpose of this study. Kozeracki

(1999) published the work of the Center for the Study of Community Colleges which,

examined distance education and provided insight to the method of transmission and

geographical concentration of these courses. At the time, online courses were not the most

common form of distance education. However, the overwhelming majority of distance

education courses offered at community colleges was concentrated in the central region of

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the United States. In particular, Iowa was among the top three states offering opportunities

for distance education. In her work, Kozeracki reviewed literature which did not find any

differences in success in traditional verses distance education courses like Hammond (1997),

McHenry and Bozik (1997), and Russel (1999) but cited conflicting expert opinions on what

the impact of new digital technologies would have on distance education like Gladieux and

Swail (1999), Easterday (1997), and Monaghan (1995). Furthermore, Kozeracki’s work

concluded that online courses and distance education remained a small percentage of the

overall community college curriculum at less than two percent of the offerings but had

significantly different levels of activity and offerings than traditional four-year institutions.

Thus, due to the tremendous growth in online course offerings and the significant reputation

of Iowa’s distance education programs, comparing Kozeracki’s analysis with this study’s

findings will provide insight into how online courses have shifted or maintained the

paradigm of traditional and distance education.

Second, this study draws upon the foundation of transcript analysis as described by

Clifford Adelman. To date, Adelman remains the only scholar to analyze transcripts at a

national level (Adelman, 1999, 2004, 2006). Through his work in the U.S. Department of

Education, Adelman has laid the foundation for and supported the validity of predicting

grades, course completion, and student retention through transcript analysis. More

specifically, Adelman (2006) detailed that student academic momentum can be traced

through transcripts. In essence, academic momentum describes the student’s course of

movement through curriculum as forward, backward, static, or a combination of movements

during an academic term (Adelman, 2006).

Third, this study is guided by the expertise of Linda Serra Hagedorn, who has focused

much of her research specifically on the analysis of community college student transcripts.

The potency of transcript analysis is confirmed at the community college level by Hagedorn

and Kress (2008) when they stated “the only trace of the presence of some community

college students is found in their transcripts.” As Hagedorn and Kress (2008) further

explained, transcripts are the primary markers of student engagement for many community

college students. Unlike traditional students at 4-year institutions, community colleges

struggle to engage their students in campus life. Due to the many work, family, and financial

commitments of community college students, they tend to spend less time on campus. Thus,

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transcript analysis provides an ideal opportunity to measure the success of such students. In

this study comparisons will be drawn from the transcript analysis of Hagedorn’s (2005b)

previous framework; utilizing student enrollment records and student demographic

information.

LIMITATIONS OF THE STUDY

The findings of this study do not seek to generalize to all community college students.

Instead, this study’s findings are limited to the community college students of DMACC who

have enrolled in at least one traditional and online course. Additionally due to the purpose

and location of DMACC, the sample was limited by its unique characteristics. In particular,

this study was limited by the breath of academic offerings at the community college level.

Whereas, one might see a larger amount of variation in academic majors, the overwhelming

majority of students in this sample were Liberal Arts students. Additionally, due to the

location of DMACC in the Midwest, there was a higher concentration of Caucasian students

in comparison to other ethnicities. Lastly, the sample size of this population of students was

relevant only for the academic years of 2005 to 2009.

DEFINITION OF TERMS

Classification of Instructional Program (CIP) Code – a taxonomy classification used

to identify postsecondary fields of study for the purpose of assessment, tracking, and

reporting.

Distance Education – is described as instruction which utilizes one or more

technologies to deliver course content to a student who is separated from the (Instructional

Technology Council, 2009).

Online Course – is a class which offerings are delivered 100% on the Internet

(Tallent-Runnels et.al, 2006).

Traditional Course – is term used to classify a class which is offered in a face-to-face

format (Tallent-Runnels et.al, 2006). In this situation student and teacher are most often in a

classroom setting.

Traditional-dominant – a term used to describe a student’s course-taking habits with

regard to online courses. A traditional-dominant student enrolls in predominantly traditional

classes and has an online course enrollment of 30% or less. Percentages are calculated from

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the total number of online courses in his/her enrollment history and divided by the total

number of all courses in his/her enrollment history.

Transcript Analysis – is “the coding and use of enrollment files, college application

data, financial aid records, and other data that community colleges must routinely collect to

comply with state and federal reporting mandates” (Hagedorn & Kress, 2008, p.7). This

information is gathered and statistically evaluated to provide a more comprehensive

understanding of a student’s progress and academic story (Hagedorn, 2005b).

Web-balanced - a term used to describe a student’s course-taking habits with regard

to online courses. A web-balanced student enrolls in similar number of traditional classes and

online classes and has an online course enrollment of 40% to 60%. Percentages are

calculated from the total number of online courses in his/her enrollment history and divided

by the total number of all courses in his/her enrollment history.

Web-dominant - a term used to describe a student’s course-taking habits with regard

to online courses. A web-dominant student enrolls in predominantly online classes and has an

online course enrollment of 70% or more. Percentages are calculated from the total number

of online courses in his/her enrollment history and divided by the total number of all courses

in his/her enrollment history.

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CHAPTER 2

REVIEW OF THE LITERATURE

Perhaps the most enduring and controversial question in distance education is, is there

a significant difference in success between traditional and distance education? (Russell,

2001). According to Russell (2001), this question has been posed as early as 1900 and has

been reworked throughout the evolution of distance education. Whereas early researchers

questioned whether student outcomes were “hurt” by distance education, later researchers

focused their studies on whether or not technology could improve student outcomes (Russell,

2001). Regardless of how the question was formed, Russell (2001) stated that in over 325

studies, spanning the early 1900s until recent times, no significant difference could be found

between distance and traditional courses that covered the same educational material.

However, Russell (2001) went further to explain that when course content was adapted to fit

the technology, a positive significant difference can be attained. Of equal importance to note,

is that Russell’s review contains literature that is not exclusive to online courses. As

previously mentioned online courses have a rather brief history in comparison to distance

education. Distance education can include a wide range of implementation methods including

online course, webinar forums, telephones, radio, video, and correspondence. Therefore, in

assessing the significant difference in success of online and traditional courses, Russell’s

literature review cannot be generalized to any one type of distance education.

The history of online course offerings, though brief, has grown rapidly. The

foundation of online courses has largely been rooted in distance education and as such, been

intertwined with other distance education technologies and media. Thus previous studies,

which focus on distance education, the umbrella term which incorporates a variety of

pedagogies, have varied on the definition of online, e-learning, and distance education.

Simply put, not all online courses are offered exclusively through the internet and not all e-

learning environments incorporate online courses. Furthermore, some online courses are

comprised of differing degrees of online offering. For example, some online courses require

50% participation online and 50% in class participation. While some colleges may refer to

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this as a blended course offering, other schools have loosely defined any percentage of online

course offerings as an online course.

Thus while it is true that distance education can be delivered in various formats, the

rapid expansion and incorporation of online courses in college curriculum remains

unmatched. In fact, student enrollment in online courses continues to expand at growth rate

of 3% and online students now represent 17% of all postsecondary students (Allen &

Seaman, 2006). Thus, a distinct need for research focusing solely on online courses is

demonstrated. Therefore, the following analysis of literature carefully examines the relevant

literature with regard to who these students are and how student success and learning are

explored within online courses. Student learning and success were found to be measured in a

variety of ways and thus, will be reviewed comparatively by (a) student performance, (b)

student perspectives, and (c) student retention . However, first, the demographic

characteristics of online students shall be examined as referenced by the literature.

STUDENT DEMOGRAPHICS

The claim that “the demographic differences between online and traditional students

has been duly noted” (Diaz, 2002, p. 1) has mixed implications. For instance, while Gibson

and Graff (1992) and Thompson (1998) concluded that online students are generally older,

have a higher GPA, and have completed more credits than traditional students. There are

several unidentified assumptions. First, these demographic characteristics portray a trend

rather than a fixed number. Observing online education over time has indicated that students

are getting younger and demographic populations are shifting (Instructional Technology

Council, 2009). The Instructional Technology Council (2009) has reported that in 2008, 52%

of students were considered traditional age; whereas, only 46% of students were considered

traditional age in 2006. The second assumption is that methods of identifying online students

are universal. As mentioned earlier, what constitutes an online student from a traditional

student may vary from institution to institution or from course to course. Lastly, demographic

characteristics vary largely across the United States and should be taken into consideration

when applying theory to practice. For instance, Iowa’s minority population constitutes only

9.4% of the student population and was ranked the fifth lowest state in terms of diversity in

2007. Thus the number of students represented by any one ethnic category is likely to be very

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different than population numbers from other states and vary highly from states outside of the

Midwest.

STUDENT PERFORMANCE

For the purpose of this review, student performance is defined by grade acquisition in

course work, GPA, or by other identified instruments to measure learning. While some

studies have collectively regarded these two measures along with student completion or

student retention, the volume and depth of research which has been done in student retention

alone, warrants its own analysis and will be discussed later.

Comparisons across student’s GPAs were conducted in Ridley and Husband’s (1998)

study. Course GPAs were analyzed and compared in both traditional and online settings.

They found that traditional students earned on average a higher GPA. However, this study

lacked variable control. By controlling for the instructor, student, course subject, or

instructional method, this study’s conclusions could have been strengthened.

In his dissertation, Bangurah (2004) compared students with passing grades in

traditional and online courses. Student’s grades were compared across courses where the

same instructor taught both online and traditional formats. Within this study, 3,601 students

participated and Bangurah (2004) found that in each course and context, mean GPAs were

highest among traditional students. He also noted females who were enrolled in web-based

courses outnumbered their male counterparts by nearly two-thirds. This ratio of female to

male students was not found within the traditional course setting.

The question of student performance has also been further reviewed along lines of

gender (Price, 2006; Yates, 2001;). Whereas, previously women were presumed to have an

online disadvantage due to access (Kirkup & von Prümmer, 1997) or family commitments

(Wolf, 1998), studies have shown that enrollment is greater among females in online courses

and females may in fact be more successful in the online setting (Price, 2006). In her study,

Price (2006) sought to uncover gender differences in female and male students who are

enrolled in online courses. In order to do so, she compared the same course in both a

traditional and online setting. From 2002 to 2004, 1,991 students participated in the study

from the Open University. Two questionnaires were utilized to demonstrate course

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experience and academic engagement. From her study, Price (2006) was able to conclude

that women were more likely to out perform their male counterparts in online course settings.

While the use of surveys in conjunction with empirical data can often provide a more

complete picture, surveys as a sole means of predicting student success and learning has been

less than successful (Hall, 2008). Employing two different survey instruments, Hall (2008)

attempted to uncover which instrument would be the most accurate in determining online

student success. Two hundred and twenty-eight students participated in the study which

encompassed three regional community colleges in the midwest. These students were all

enrolled in at least one online course in the following areas: business, computer information

services, criminal justice, and early childhood development. Hall (2008) found that the class

categories were a better predictor of student success than either of the two survey

instruments. In fact, the surveys showed little than an 8% accuracy in predicting final grades

for these students.

STUDENT PERSPECTIVES

The analysis of online learning and success has also been observed through student

perceptions (Kelly, Ponton, & Rovai, 2007). Thus, students’ reactions, evaluations, and

assessments are taken into consideration. Such measures may provide insight into dependent

and independent variables for further research. For instance, Kelly et al. (2007) reported on

the differences of student evaluations of teaching (SET) for online and traditional courses.

They found this research to be important in revealing biases of SET towards online course

offerings. Kelly and colleagues found that instructors’ knowledge, experience, and perceived

competency were more important to traditional students; whereas, online students more

highly valued course material and course structure. Therefore, the role of the instructor and

method of course delivery has also been analyzed (Cragg, Dunning, & Ellis, 2008). In their

study, Cragg et al. (2008) were able to demonstrate more similarities in instructor style than

differences in online and traditional courses.

In an effort to compare student engagement among traditional and online students,

Robinson and Hullinger (2008) constructed a modified version of the National Survey of

Student Engagement (NSSE). This widely accepted instrument has been commonly used to

gauge student involvement and activity in academic settings. Findings were representative of

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201 respondents; all participated in online courses, 85% were less than 25 years old, 84%

were White, and more than half were upper classmen. Benchmarks of engagement were

provided by the 2006 NSSE results of on-campus students. In both the senior and freshmen

respondents, their NSSE results were higher than traditional NSSE on-campus students. The

students’ NSSE scores were still considered modest; however, overall the online students’

scores showed that faculty-student interaction was no lower than traditional student

reporting.

STUDENT RETENTION

Expanding options in online courses and offerings have also brought about higher

attrition rates (Allen & Seaman, 2006; Carr, 2000; Diaz, 2000). According to Diaz (2002),

“Drop rates are among the characteristics that have routinely prompted distance education

studies,” (p.1), though Diaz (2002) warned that high levels of attrition do not necessarily

translate into academic non-success as some students simply attempt to enroll in more online

courses. In 2000, Diaz’s study indicated that of 231 students enrolled in general health

education, online students tended to be older and had completed more credit hours than

traditional students. Further analysis revealed that while drop rates were dramatically higher

in online courses, those online students who did complete course work received twice as

many “A’s” than did traditional students who received twice as many “D” and “F” marks.

While, Diaz’s study cannot be generalized to all online courses since he only examined one

course type, it is important to note that when calculating retention, completion and successful

completion (attaining a “C” or higher) may not be the same measurement.

Other studies have supported Diaz’s (2000) findings that successful online students

tend to be older (Doherty, 2006) and have completed more credit course hours (Aragon &

Johnson, 2008; Doherty, 2006). Such studies have further investigated the demographic

backgrounds of online students to find predicators of success. Demographic information was

collected from institutions on 10,466 students enrolled in online courses for Doherty’s (2006)

study. He found that by comparing unsuccessful students and successful students generated

unequal comparison group sizes. He used Spearman’s rank correlation coefficient to test for

correlations between success, age, and number of credits. A chi-square was performed to

compare gender and successful online students. Doherty found significant results that

14

positively correlated age, number of credits enrolled in the semester, and total credits

completed with success in online course. Conversely, there was no significant relationship

between gender and online course success.

However, some studies do show a significant and positive correlation between student

completion and gender (Aragon & Johnson, 2008; Nesler, 1999; Price, 2006). Recently,

Aragon and Johnson (2008) obtained the student records of 305 students enrolled in a rural

Midwestern community college. In their study, they analyzed the student demographic,

enrollment, academic readiness, and course completion data. Aragon and Johnson found no

significant difference among age, ethnicity, and financial aid eligibility between online

completers and non-completers. However, there were significant differences among gender,

hours enrolled, and GPA. Thus, aside from age and credit hours, the literature remains

conflicted on significant correlations between student demographics and success in online

courses.

TRANSCRIPT ANALYSIS

As described, the literature has heavily analyzed students’ learning, perspectives, and

success in online courses. Researchers have investigated these concepts by means of grades,

surveys, completion rates. Moreover, they have controlled for the type of discipline, the

instructor, demographic and enrollment histories and compared these characteristics among

online students to traditional students. However, they have not used the individual student as

a control measure. By examining the student’s own success in traditional and online settings,

this study proposes a new approach to online course studies through transcript analysis.

Transcript-analysis is arguably one of the most reliable forms to track and measure

student performance at the community college level (Hagedorn, 2005b). Adelman (1999)

utilized this type of analysis to generate a national longitudinal study of the college course

taking behaviors of the class of 1972 and 1982. The importance of this technique, while it is

not a new concept, has been revived by Clifford Adelman’s work. Furthermore, Adelman’s

work has utilized transcripts to determine student patterns as well as answer “how we are

doing in postsecondary education” (Adelman, 1999). As Adelman stated,

Transcripts are unobtrusive records. As such, they do not lie, they do not exaggerate, and they do not forget. They tell us what really happens, what courses

15

students really take, they credits and grades they really earn, the degrees they really finish and when those degrees are awarded. (Adelman, 1999, p. 10)

Transcript analysis is defined as “the coding and use of enrollment files, college

application data, financial aid records, and other data that community colleges must routinely

collect to comply with state and federal reporting mandates,” (Hagedorn & Kress, 2008, p.

7). Specifically, transcript analysis allows for researchers to track the history and progress of

postsecondary students. While this method of research remains relatively underutilized, it

provides an accurate account of students’ higher education progression. Furthermore,

transcript analysis is of particular importance in assessing inquires regarding community

college students. Due to the busy schedules and other commitments of many community

college students, time spent on campus and engagement in campus activities remains limited

and community college students are ideal candidates for transcript analysis (Hagedorn &

Kress, 2008).

SUMMARY

The literature discussed online students in terms of their demographic characteristics,

performance, perspectives, and retention. Since the definitions of what constitutes an online

course vary from institution to institution and since performance is measured in a variety of

ways, it is difficult to determine a clear and concise picture of what an online student is and if

and how they succeed. Transcript analysis was identified as a methodology that has not been

widely used in analyzing online student success and might be best suited for studies like this

one, involving community college students. Thus, the following chapter will provide a

detailed account of this methodology and its application within this study.

16

CHAPTER 3

METHODOLOGY

DESIGN OF THE INVESTIGATION

This chapter will provide a description of how the research was conducted including

the sample population, how data was attained, the research methodology, and a description of

the data analysis techniques. First, the research questions will be presented and a detailed

framework will follow with descriptions of the independent and dependent variables.

RESEARCH QUESTIONS





In Kozeracki’s (1999) work, she found that Iowa was one of the top three states in

online community college offerings. She concluded that there were no significant differences

in student achievement between online and traditional courses but hypothesized that this

could change over time as course possibilities grew. Since 1999, online student enrollments

have grown to over 12 million (Parsad & Lewis, 2008).

Thus, this study reevaluates the work started by Kozeracki (1999) in online and

distance education. While, the study will retain the null hypothesis that no significant

difference will be found between traditional and online course offerings in student

achievement, the study will utilize a systematically different approach. Specifically, this

study draws upon Adelman (1999, 2004, 2006), Hagedorn’s (2005b) and Hagedorn and

17

Kress (2008) work in transcript analysis to explore in-depth the differences of student

achievement through the analysis of grades and course completion rates.

POPULATION

A sample of students enrolled in both traditional and online courses was selected

from DMACC, the largest community college system in Iowa with over 18,000 students and

five campuses. Due to its size, various locations, and diverse course offerings, it provided an

appropriate environment to collect data.

Eleven thousand nine hundred and seventy students met the criteria of having

enrolled in both an online and traditional course during the time span of fall 2005 through fall

2008. Of this number, 7,696 were female, 4,253 were male, and 21 were unidentified; 10,196

were Caucasian, 446 were black, 299 were Hispanic, 419 were Asian, 53 were Native

American, and 53 were unidentified; 33.9% received Pell Grants; and 96.4% were Iowa

residents.

TREATMENT

Data was gathered through the assistance of the Office of Institutional Research at

DMACC in the form of existing student files. Due to the sensitive and confidential nature of

student files, The Office of Institutional Research at DMACC conducted the query for these

files. Students who had enrolled in at least one online and one traditional course during the

academic years spanning fall 2005 through fall 2008 were included in the sample. DMACC

released these files electronically once all student identification information had been

removed and was replaced by an anonymous identification (ID) number. Two files were

generated from the information present in the student files. The first was called an enrollment

file which included the anonymous student ID number, the campus in which the student was

enrolled, the academic year of the course, the semester, an indication of whether or not the

course had been dropped, the subject, the course number, the grade received, the number of

credits, and the type of course (online or traditional). This file used the course as the unit of

analysis. Thus there were multiple lines of data for each student. The second file was deemed

the demographic file and contained information on the student’s anonymous ID number, sex,

birth date, first term of enrollment, race, Pell Grant status, residency, major, and

18

classification of instructional program (CIP) Code. This file used the student as the unit of

analysis.

DEPENDENT VARIABLES

The dependent variables measured in this study were grades and course completion

ratios. These two dependent variables were selected due to their consistency throughout

previous literature as benchmarks of measuring students’ success in distance education. A

careful distinction between grades and course completion ratios being two separate measures

of success was made due to the unique manner in which many community college students

progress through their academic track. As Hagedorn’s (2005b) and Hagedorn and Kress

(2008) has described, community college students more frequently experience periods of

stop-out, transfer, and part-time enrollments.

Success in grade achievement was established through calculating the student’s GPA

and distinguished by the student obtaining a course grade of “C” or higher; whereas, success

in course completion ratios (CCR) was measured by dividing the number of courses

attempted into the number of courses completed with a passing grade. In both GPA and

CCR, student measures were calculated for traditional and web success separately. These

measures were relative for each individual student and determined based on the student’s

unique GPA and CCR. In other words, this method measures the student’s goals and goals

are stated by enrollment in a course. For example, a student who enrolls in ten courses has a

CCR of 100%, whereas, a student who completes two courses also has a CCR of 100% if

they have only enrolled in two courses.

INDEPENDENT VARIABLES

The demographic variables included for purpose of analysis were sex, age, race, Pell

grant status, and CIP code. Students’ sex was coded as 1 for Males and 2 for Females. Age

was calculated from birthdates and categorized into subgroups from Hagedorn’s age model

(Hagedorn, 2005a). Students age 17 through 21 were coded as 1, students age 22 through 30

were coded as 2, students age 31 through 45 were coded as 3, and students age 46 and over

were coded as 4. Race was coded as 1 for Caucasian, 2 for Black, 3 for Hispanic, 4 for Asian,

5 for Native American, and 6 for unknown or other. If student’s received Pell grants they

were coded as 1 and those did not receive Pell grants were coded as 0. CIP codes were

19

condensed into the major discipline of study by utilizing the first two digits provided rather

than the entire sequence. For instance a student with CIP code 190402 – Consumer

Economics would be categorized as CIP family 19 – Consumer and Human Sciences.

COURSE MODE

In order to account for the discrepancy that occurs among students who have taken

many courses online as opposed to those who have only taken a few or even one, this study

has developed a technique for categorizing students’ online course enrollment. In this

manner, students can be compared to other students who are most similar to them in course-

taking behaviors. The percent of online courses was calculated for each student. From this

percentage, three categories were developed: traditional-dominant, web-balanced, and web-

dominant. Traditional-dominant students were defined as those students whose online course

percentage was 30% or lower of their class taking percentage. Traditional-dominant students

were coded as 1. Web-balanced students had an online class percentage of 40 to 60% and

were coded as 2. Web-dominant students had an online class percentage of 70% or above and

were coded as 3.

DATA ANALYSIS PROCEDURES

Throughout the analysis of this study’s data, both descriptive and inferential statistics

techniques were utilized. All statistical analysis was performed on the Statistical Package for

the Social Sciences (SPSS) 17.0 and the level of significance established was p 0.05.

Initially, frequencies were viewed to create a better picture of the sample. Demographic and

course success variables were then selected based on the research questions to be

investigated further through paired sample T-tests and One-way analysis of variance

(ANOVA). Correlation coefficients were calculated to determine the strength of relationship

between variables and success in online courses.


The findings of this study do not seek to generalize to all community college students.

Instead, this study’s findings are limited to the community college students of DMACC who

have enrolled in at least one traditional and online course. Additionally due to the purpose

and location of DMACC, the sample was limited by its unique characteristics. In particular,

20

this study was limited by the breath of academic offerings at the community college level.

Whereas, one might see a larger amount of variation in academic majors, the overwhelming

majority of students in this sample were Liberal Arts students. Additionally, due to the

location of DMACC in the Midwest, there was a higher concentration of Caucasian students

in comparison to other ethnicities. Lastly, the sample size of this population of students was

relevant only for the academic years of 2005 to 2009.

21

CHAPTER 4

RESULTS

Chapter four is subdivided by descriptive and inferential findings. The organization of

data in this manner provides a clear picture of the DMACC sample and subsequently

attempts to answer the research questions in consecutive order. Thus, student success is

analyzed by GPA first and CCR last.

DESCRIPTIVE ANALYSIS

Overall, the DMACC sample included 11,970 students of which 36% were male and

64% were female. The overwhelming majority of students were Caucasian (85%). Table 1

shows the percentages of students’ gender and ethnicity for this sample.

Table 1. Percentage of DMACC Students Who Enrolled in both Online and Traditional Courses

The students’ demographic information was further analyzed by financial aid status

and by age. The only information available to determine financial aid status gathered from

the students’ enrollment file was the receipt of a Pell Grant. In this sample, 4,061 students

were reported recipients of the Pell Grant (N=11970). Age groups were determined by

Hagedorn’s (2005a) age model that categorized college students into four subgroups. The

frequency distribution was highest among those students ages 22 through 30 inclusive as

reflected in Table 2 (N = 11953). Corresponding percentages are noted within parentheses.

Gender

Male (%) Female (%)

Asian 1.6 1.9

Black 1.5 2.2

Caucasian 29.2 56.0

Hispanic 0.9 1.5

22

Table 2. Frequency and Percentage of Age Group Distribution

Upon initial analysis, students varied greatly in their enrollment of online courses.

Some students had only enrolled in one online course whereas; others had six or seven online

course experiences. Due to the variation among students who heavily enrolled in online

classes compared to those students who had only taken a few or even one online class, this

study developed a technique for further analysis. This technique proposed that students’

course enrollment behavior could be analyzed through observation of the course mode.

Students’ course enrollment behavior for online courses was subdivided and analyzed by:

traditional-dominant students whose online course percentage accounted for 30% or less of

their total classes (coded as 1), web-balanced students whose online course percentage

accounted for 40% -60% of their total classes (coded as 2), and web-dominant students

whose online course percentage accounted for 70% or higher of their total classes (coded as

3). Respectfully, enrollments showed 6,281 students were considered traditional-dominant,

2,771 students were web-balanced, and 877 students were web-dominant. The remaining

2,041 students, who fell into either 31%-39% or 61%-69%, were removed from the sample to

reinforce the integrity of the course mode model. Table 3 indicates the results of a cross

tabulation of students’ course mode and age group. Percentages are indicated in parenthesis

and represent the percentage of an age group within a course mode (N=9913).

Table 3 indicates that students ages 22-30 represent the largest number of students in

each course mode. Similarly, students ages 17-21 represent the second largest number of

students in each course mode except web-dominant. The second largest number of students

in the web-dominant course mode is represented by an older age group of students rather

than by a younger age group. While the oldest age group, 46 and over students represent a

relatively low percentage of students in each category, they demonstrate their

Age Frequency (%)

17-21 2796 (23.4)

22-30 6411 (53.6)

31-45 2115 (17.7)

46 and over 631 (5.3)

23

Table 3. Frequency and Percentages of Age Groups Represented in Course Mode Model

highest student population percentage in the web-dominant course mode. A Pearson Chi-

Square (N=9913) of this cross tabulation revealed a significant value of 160.57.

In order to determine the financial aid status of students within these respective

course modes, an additional cross tabulation was run and found to have significant results. As

Pell Grant status was the only indication of financial aid present on student enrollment files,

students’ Pell Grant status was crossed with their course mode and can be found in Table 4

(N=9929). Percentages are represented in parenthesis and students that received the grant

were coded as 1. As Table 4 indicates, the largest number of Pell Grant students is

represented by those in the “Traditional-Dominant” course mode (N=2260); however, the

largest population percentage composed of Pell Grant students is found in the “Web-

Dominant” course mode (42.8%).

Table 4. Frequency and Percentage of DMACC Students Receiving Pell Grants by Course Mode

GPA ANALYSIS

While the frequencies mentioned begin to provide a clearer picture of DMACC

students who enroll in both traditional and online courses, further statistical analysis was

used to deepen the understanding of which students succeeded in these courses. A paired

sample t-test was calculated on students’ GPA in both online and traditional courses.

Course Mode

Age Traditional-Dominant (%) Web-Balanced (%) Web-Dominant (%)

17-21 1657 (26.4) 615 (22.3) 110 (12.6)

22-30 3379 (53.9) 1475 (53.4) 463 (52.9)

31-45 909 (14.5) 535 (19.4) 239 (27.3)

46 and over 329 (5.2) 138 (5.0) 64 (7.3)

Course Mode

Pell Grant Traditional-Dominant (%) Web-Balanced (%) Web-Dominant (%)

0 4021 (64.0) 1996 (72.0) 502 (57.2)

1 2260 (36.0) 775 (28.0) 375 (42.8)

24

Table 5 illustrates the results of this t-test by demographics (N=10297). In general, females

performed better than males in both traditional and web courses. However, it was the age

group “46 and over” that displayed the highest mean web GPA and the “17-21” age group

that had the lowest mean web GPA. Regardless, all demographic categories garnered a

higher mean GPA in traditional courses than in web courses.

Table 6 (N=10297) displays the correlation coefficients and paired differences among

demographic categories. All demographic categories were determined to be significant and

demonstrated a moderate correlation. For the most part, the mean differences across

demographics are similar. However, the most variation occurs within Age Group.

Subsequently, this category contains the lowest mean difference of -.19 by students ages 46

and over and the highest mean difference of -.38 by students ages 17-21.

As previously stated, CIP codes were analyzed to demonstrate how students

performed within disciplines. For the purpose of this study, CIP codes were condensed from

six digits into the first two digits or CIP code family. The CIP code family represents the

discipline related field rather than the individual’s specific major which was represented by

the entire six digit sequence. By condensing the CIP codes, the researcher had a more concise

and manageable data set. A complete listing of these CIP code families can be found in

Appendix A. Table 7 displays the students’ mean GPAs by traditional and web based courses

and their correlation coefficients. As is indicated by the table, sample size varies highly from

CIP code 46 with a population of six to CIP code 24 with a population of 6,997.

Respectfully, the two largest populations of CIP codes are represented by 24 – Liberal Arts

and Sciences (N=6997) and 52 – Business, Marketing, Management and other Support

Services (N=1661). Such variances are reflective of the community college students’ majors

and the limited course offerings in particular majors as course levels advance. Comparisons

of web and traditional GPA across all CIP codes reveal students on average obtain a higher

mean GPA in traditional courses than web courses with one exception CIP code 16 – Foreign

Languages, Literature, and Linguistics. All CIP codes showed a modest correlation

significance except CIP 26 and 46, both which had very small samples.

25

Table 5. DMACC Students’ Mean GPA Scores in Traditional and Web Courses by Demographics

Table 8 illustrates the mean GPAs and correlation coefficients of students in both

traditional and web courses by ethnicity. It is important to note that Caucasian students

drastically outnumber other ethnicities. Specifically, the sample populations are as follows:

Caucasian N=8846, Black N=344, Hispanic N=239, and Asian N=359. As can be seen in

Table 8, all ethnicities demonstrate a higher mean GPA in traditional classes than in web

classes. Caucasian students attained the highest GPA in traditional classes (2.76) while Asian

students attained the highest web GPA (2.52) and showed the least amount of standard

deviation (SD=1.33). Modest correlation was demonstrated across all ethnicities.

Further analysis on was performed on students’ ethnicities. Comparisons between

ethnic groups can be seen in Table 9. The difference in traditional GPA was found to be

significant between all comparisons with black students, whom had the lowest mean GPA

Traditional GPA Web GPA

N Mean SD Mean SD

10297 2.74 1.05 2.42 1.36

Gender

Male 3536 2.60 1.08 2.25 1.40

Female 6747 2.81 1.03 2.50 1.34

Age Group

17-21 2412 2.58 1.06 2.20 1.39

22-30 5498 2.66 1.05 2.33 1.36

31-45 1827 3.00 1.01 2.75 1.26

46 and over 549 3.31 .85 3.12 1.19

Course Mode

Traditional-Dominant 5280 2.66 .95 2.32 1.42

Web-Balance 2446 2.81 1.14 2.55 1.30

Web-Dominant 761 2.91 1.23 2.55 1.14

Pell Grant

0 6840 2.82 1.05 2.53 1.36

1 3457 2.58 1.04 2.20 1.35

26

Table 6. DMACC Students’ Correlation and Paired Difference Statistics in GPA Scores of Traditional and Web Courses by Demographics

and thus highest degree of difference in GPA. Black students varied by about half a GPA

point less than Caucasian students (-.52). Conversely, all web GPAs were found to have a

significant mean difference with an exception between Asian and Caucasian students (mean

difference=.055).

COURSE COMPLETION RATIOS (CCR) ANALYSIS

Students’ course completion ratio (CCR) was also examined as a factor of success. In

Table 10 (N=11970), CCRs were examined across demographics of gender, age, course

mode, and Pell Grant status. In each of these demographic categories, students obtained

higher CCRs in traditional courses than they did in web courses. However, CCRs in web

based courses did fluctuate within demographic categories. Table 10 shows that females had

Paired Differences

N Correlation Mean SD t df

10297 .58* -.32 1.14 -28.58* 10296

Gender

Male 3536 .58* -.35 1.17 -17.72* 3535

Female 6747 .58* -.31 1.12 -22.40* 6746

Age Group

17-21 2412 .59* -.38 1.14 -16.56* 2411

22-30 5498 .56* -.33 1.16 -21.04* 5497

31-45 1827 .55* -.25 1.10 -9.76* 1826

46 and over 549 .57* -.19 .99 -4.42* 548

Course Mode

Traditional-Dominant 5280 .59* -.34 1.15 -21.67* 5279

Web-Balance 2446 .57* -.27 1.15 -11.49* 2445

Web-Dominant 761 .58* -.37 1.09 -9.23* 760

Pell Grant

0 6840 .59* -.29 1.12 -21.28* 6839

1 3457 .56* -.38 1.16 -19.34* 3456

27

Table 7. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web GPA’s by CIP Code

a higher ratio of course completion than male students in both traditional and web courses.

Within age groups, higher CCRs were found to positively correlate with older student groups

across both traditional and web courses. However, there was one exception to the age group

CCRs; students ages 17-21 (M=.70) had a higher traditional CCR than students ages 22-30

(M=.69). Web-dominant students had the highest CCR in web courses (M=.65); although,

this was still lower than their traditional CCR (M=.69). Lastly, those students receiving Pell


CIP Code N Mean SD Mean SD Correlation t df

01 89 2.74 .82 2.11 1.34 .50* -5.12* 88

10 26 2.62 1.14 1.73 1.37 .71* -4.59* 25

11 50 2.84 .90 2.18 1.32 .66* -4.69* 49

12 109 3.18 1.05 2.59 1.19 .52* -5.52* 108

14 7 2.05 1.56 1.84 1.86 .89* -.68* 6

15 34 2.61 1.03 1.94 1.35 .68* -3.93* 33

16 21 3.35 1.08 3.36 .95 .46* .02* 20

19 122 2.89 1.05 2.35 1.31 .59* -5.49* 121

22 128 3.03 1.03 2.62 1.38 .66* -4.50* 127

24 6997 2.70 1.07 2.42 1.37 .58* -20.57* 6996

26 7 2.93 .97 2.62 1.68 .70 -.67 6

31 43 2.39 1.10 1.86 1.50 .51* -2.62* 42

43 359 2.55 1.06 2.17 1.39 .61* -6.40* 358

44 155 2.81 1.00 2.33 1.41 .50* -4.77* 154

46 4 2.44 .52 2.23 1.55 .92 -.39 3

47 81 2.93 .84 2.51 1.35 .43* -3.05* 80

48 31 2.90 .74 2.48 1.38 .40* -1.83* 30

50 22 2.89 .86 2.24 1.40 .51* -2.51* 21

51 321 2.86 .89 2.8266 1.32 .49* -.53* 320

52 1661 2.83 1.02 2.41 1.34 .60* -15.47* 1660

28

Table 8. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web GPA’s by Ethnicity

Table 9. Comparison of DMACC Students’ Mean Differences in GPA by Ethnicity


Ethnicity N Mean SD Mean SD Correlation t df

Caucasian 8846 2.76 1.04 2.45 1.35 .58* -26.35* 8845

Black 344 2.24 1.14 1.84 1.44 .57* -6.08* 343

Hispanic 239 2.61 1.05 2.21 1.44 .50* -4.81* 238

Asian 359 2.69 1.05 2.52 1.33 .47* -2.68* 358


Ethnicity Mean Diff. Std. Error Mean Diff. Std. Error

Caucasian

Black .52* .05 .64* .07

Hispanic .15 .06 .25* .09

Asian .07 .05 -.05 .07

Black

Caucasian -.52* .05 -.64* .07

Hispanic -.37* .08 -.39* .11

Asian -.45* .07 -.70* .10

Hispanic

Caucasian -.15 .06 -.25* .09

Black .37* .08 .39* .11

Asian .08 .08 -.30* .11

Asian

Caucasian -.07 .05 .05 .07

Black .45* .07 .70* .10

Hispanic .08 .08 .30* .11

29

Table 10. DMACC Students’ Mean Course Completion Ratios (CCR) in Traditional and Web Courses by Demographics

Grants (coded as 1) received lower CCRs than those students who did not receive Pell Grants

(coded as 0) in both traditional and web courses.

Table 11 illustrates the modes correlation coefficients and paired differences across

demographics. For the most part, the mean differences across demographics are similar.

However, the most variation occurs within Course Mode. Subsequently, this category

contains the lowest mean difference of .04 within Web-dominant students and the highest

mean difference of .16 within Traditional-dominant students.

Comparisons of web and traditional CCRs across CIP codes are displayed in Table

12. Students on average obtained a higher CCR in traditional courses than in web courses

across all CIP codes. Furthermore, CIP codes showed a modest correlation significance

except in CIP codes 16 (Foreign Languages), 26 (Biology), and 46 (Construction Trades).

Traditional CCR Web CCR

N Mean SD Mean SD

11970 .71 .33 .58 .43

Gender

Male 4253 .67 .35 .53 .45

Female 7696 .73 .32 .61 .42

Age Group

17-21 2796 .70 .34 .55 .45

22-30 6411 .69 .34 .57 .43

31-45 2115 .76 .32 .65 .40

46 and over 631 .81 .28 .71 .40

Course Mode

Traditional-Dominant 6281 .71 .29 .54 .46

Web-Balance 2771 .72 .37 .63 .41

Web-Dominant 877 .69 .42 .65 .33

Pell Grant

0 7909 .74 .33 .62 .44

1 4061 .66 .33 .52 .42

30

Table 11. DMACC Students’ Correlation and Paired Difference Statistics of Course Completion Ratios (CCR) in Traditional and Web Courses by Demographics

Students’ traditional course and web course mean CCR and correlation coefficients

are depicted in Table 13. All ethnicities performed better in their traditional CCR than in

their Web CCR. Asian students attained the highest web CCR (.61). The correlation

coefficients for all ethnic groups were found to be significant with moderate strength in

relationship.

Table 14 illustrates the further analysis of CCRs between ethnic groups. Black

students had the largest mean difference in CCR across ethnicities in both traditional and web

courses. The mean difference in black students’ CCR was significantly negative in all

traditional courses when compared to other ethnicities but was only significantly negative in

comparison to Caucasian and Asian students’ web CCRs. Hispanic students also had

significantly lower mean CCRs when compared to Caucasian and Asian students’ web CCR.

Paired Differences

N Correlation Mean SD t df

Gender

Male 4253 .50* .13 .41 21.46* 4252

Female 7696 .48* .12 .39 27.22* 7695

Age Group

17-21 2796 .51* .15 .40 19.88* 2795

22-30 6411 .50* .12 .40 24.92* 6410

31-45 2115 .45* .10 .39 12.40* 2144

46 and over 631 .41* .10 .38 6.92* 630

Course Mode

Traditional-Dominant 6281 .50* .16 .40 32.74* 6280

Web-Balance 2771 .51* .09 .39 11.56* 2770

Web-Dominant 877 .48* .04 .39 2.91* 876

Pell Grant

0 7909 .49* .12 .40 26.16* 7908

1 4061 .47* .14 .39 23.09* 4060

31

Table 12. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web Course Completion Ratios (CCR) by CIP Code

SUMMARY OF FINDINGS

This study found that the majority of students were female (64%), Caucasian (85%),

and ages 22-30 (46%). While the majority of these students did not receive a Pell Grant, 34%

were recipients. Additionally, this study also looked at students’ course taking behaviors and

found that traditional-dominant students constituted the largest population of students.

However, older students demonstrated their highest population percentages in web- balanced


CIP Code N Mean SD Mean SD Correlation t df

01 107 .70 .29 .49 .44 .46* 5.35* 106

10 28 .68 .36 .47 .46 .52* .27* 27

11 56 .74 .29 .56 .42 .64* 4.10* 55

12 116 .80 .29 .73 .38 .52* 2.11* 115

14 8 .56 .50 .46 .50 .73* .83 7

15 41 .70 .29 .48 .48 .56* 3.59* 40

16 28 .76 .40 .50 .33 .22 2.95* 27

19 138 .76 .31 .60 .43 .56* 5.21* 137

22 142 .72 .29 .62 .42 .51* 3.44* 141

24 8116 .70 .34 .59 .43 .50* 26.07* 8115

26 10 .73 .35 .50 .52 .38 1.44 9

31 50 .63 .32 .50 .44 .47* 2.32* 49

43 415 .65 .33 .55 .43 .50* 5.68* 414

44 176 .69 .32 .57 .42 .38* 3.81* 175

46 7 .60 .44 .43 .53 .63 1.03 6

47 89 .76 .27 .70 .41 .38* 1.48 88

48 39 .74 .28 .57 .47 .65* 2.90* 38

50 24 .77 .28 .52 .45 .47* 2.91* 23

51 412 .81 .25 .58 .44 .21* 10.55* 411

52 1934 .71 .33 .57 .42 .53* 16.30* 1933

32

Table 13. DMACC Students’ Correlation Coefficients of their Mean Traditional and Web Course Completion Ratios (CCR) by Ethnicity

Table 14. Comparison of DMACC Students’ Mean Differences in Course Completion Ratios (CCR) by Ethnicity Traditional CCR Web CCR

Ethnicity Mean Diff. Std. Error Mean Diff. Std. Error

Caucasian

Black .17* .02 .22* .02

Hispanic .04 .02 .14* .03

Asian .00 .02 -.01 .02

Black

Caucasian -.17* .02 -..22* .02

Hispanic -.12* .02 -.08 .03

Asian -.16* .02 -.23* .03

Hispanic

Caucasian -.04 .02 -.14* .03

Black .12* .02 .08 .03

Asian -.04 .03 -.15* .03

Asian

Caucasian -.00 .02 .01 .02

Black .16* .02 .23* .03

Hispanic .04 .03 .15* .03


Ethnicity N Mean SD Mean SD Correlation t df

Caucasian 10196 .72 .33 .60 .43 .49* 30.82* 10195

Black 446 .55 .35 .38 .42 .51* 9.24* 445

Hispanic 299 .67 .34 .46 .43 .40* 8.61* 298

Asian 419 .71 .35 .61 .43 .41* 4.99* 418

33

and web-dominant categories. In analyzing CIP Code families, it was discovered that over

half the students (6,997) were Liberal Arts majors.

Inferential statistics were used to analyze students’ demographic differences in

comparison to student success as measured by GPA and CCR. These findings revealed

modest correlations in gender, age, Pell Grant status, ethnicity, and course mode for both

GPA and CCR when traditional and online averages were calculated. Although all students

performed better in traditional courses than online courses (a) females outperformed males,

(b) older students outperformed younger students, (c) web-dominant students outperformed

traditional-dominant and web-balanced students, and (d) students who did not receive a Pell

Grant outperformed recipients in online settings obtain both a higher average GPA and CCR.

When ethnicity was considered, Caucasians performed the best in traditional courses but

Asian students outperformed all other ethnicities in online courses. Consequentially,

Hispanic and Black students had the lowest GPAs and CCRs. By analyzing the students’

academic majors, an instance where students performed better in an online setting than in a

traditional setting was indicated in CIP Code 16 (Foreign Languages). This was the only

category in the entire study where this occurred and was isolated to students’ GPA averages

and not CCR averages.

These findings indicate a clear difference in students’ online and traditional

performance as well as their performance when considered within demographic, academic, or

course mode categories. While there was a rather large amount of variance at times between

population size, this data was representative of the entire DMACC population who have

enrolled in both online and traditional courses. Therefore, as a population study, these

findings, while not generalizable to all higher education institutions, are an accurate depiction

of DMACC. Additionally, these findings may be helpful to Midwestern community colleges

similar to DMACC in size, course offerings, and student population. Further discussion and

the potential implications of these results will be continued in the conversation in the

following chapter.

34

CHAPTER 5

SUMMARY, CONCLUSION, AND

RECOMMENDATIONS

This final chapter restates the research problem and reviews the major methods used

in the study. The major sections of this chapter summarize the results and discuss their

implications for application and future research.

STATEMENT OF THE PROBLEM

While Iowa’s public high school diploma attainment percentage is above the national

average, its college degree attainment falls below both the Midwest and National averages

(Iowa Department of Education, 2008). Like many Midwestern states, Iowa has a large rural

population. This population, in particular, has an expressed demand for community colleges

as compared to Iowa’s urban population (Iowa Department of Education, 2008). Thus, in

investigating the lack of successful college degree attainment among Iowans, it is particularly

important to examine the community college system in Iowa.

Consistent with their missions, community colleges have generally been open to

adopting technologies that show promise of extending educational opportunities for students.

In recent years, the growing number of online courses has provided students additional

access particularly, those students who are limited by time, transportation, work, disability or

family constraints. However, the literature reveals that online courses have considerably

higher drop-out rates than traditional face-to-face courses (Aragon & Johnson, 2008;

Doherty, 2006). Thus, it is necessary to understand the trends in online community college

courses within Iowa.

PURPOSE OF THE STUDY

In 2006-2007 there were over 6 million students enrolled in community colleges in

the U.S. and 1,045 public community colleges (Provasnik & Planty, 2008). Additionally, in

2005-2006 over 1.5 million students enrolled in community colleges had taken at least one

online course (Allen & Seaman, 2006). In fact, with 3.1 million students enrolled in at least

35

one online course in the U.S., the majority of online students are enrolled in community

college (Allen & Seaman, 2006). Thus, due to the overwhelming large percentage of drop-

out rates for online courses (Maxwell, 2003) and the rapidly increasing number of online

courses (Allen & Seaman, 2006), the literature strongly suggests a need to understand

enrollment patterns and characteristics of these students (Aragon & Johnson, 2008; Doherty,

2006; Maxwell, 2003).

In states that have large rural populations like Iowa, successful retention in online

courses will be beneficial to students who are at a disadvantage due to physical access and

transportation. Furthermore, Iowa has a particular need to focus on online course success due

to the large percentage of students who intend on attaining postsecondary training at the

community college level. This percentage has increased since 1998 when 30.9% of public

high school graduates were pursuing or intending to pursue higher education at a community

college to 39.2% in 2008 (Iowa Department of Education, 2008). These students now

constitute Iowa’s largest population of postsecondary bound students. Furthermore, the

community colleges themselves, believe that the successful implementation and offering of

online courses will keep them accessible and competitive (Cox, 2005). Thus, community

colleges may be well advised to explore and better understand the students who are taking

online courses.

In reviewing the literature, studies on online courses have predominantly examined

demographic background (Nesler, 1999; Parker, 1999; Carr, 2000; Diaz 2002), course

completion (Bangurah, 2004; Doherty, 2006; Aragon & Johnson, 2008), and GPA analysis

(Ridley & Husband, 1998). However, all of the literature examined that comparisons were

drawn across types of courses, instructors, or types of students. None of the research

examined the student as the unit of analysis. Thus, the research failed to ascertain if

differences were due to the student attributes that lead to online enrollment or if the

differences were due to course delivery options. Previous studies have also overlooked the

individual’s success and differences in these two settings. Thus this study has analyzed the

transcripts of students who have enrolled in both online and traditional courses within the

academic years of 2005 until 2009. In doing so, the study has utilized demographic

information in the form of student files which contained students’ records of grade, course

type, major, and retention.

36

RESEARCH QUESTIONS

This study analyzed the transcripts and college records of approximately 12,000

students who have enrolled in both online and traditional courses within the academic years

of 2005 to 2009 at Des Moines Area Community College (DMACC). This is a unique group,

as the sample only included an individual if they have completed in full at least one online

course and one traditional course at DMACC. In this manner, students were used as their

own controls. The study utilized demographic information as recorded in student files as well

as transcript level data containing grades and course types. The specific research questions

were:





THEORETICAL BASES AND ORGANIZATION

The framework of this study is based on the work of three scholars. First, the work of

Carol Kozeracki provides a historical framework to the purpose of this study. Kozeracki

(1999) published the work of the Center for the Study of Community Colleges which,

examined distance education and provided insight to the method of transmission and

geographical concentration of these courses. At the time, online courses were not the most

common form of distance education. However, the overwhelming majority of distance

education courses offered at community colleges was concentrated in the central region of

the United States. In particular, Iowa was among the top three states offering opportunities

for distance education. Kozeracki’s work concluded that online courses and distance

education remained a small percentage of the overall community college curriculum at less

than two percent of the offerings but had significantly different levels of activity and

37

offerings than traditional four-year institutions. Thus, due to the tremendous growth in online

course offerings and the significant reputation of Iowa’s distance education programs,

comparing Kozeracki’s analysis with this study’s findings will provide insight into how

online courses have shifted or maintained the paradigm of traditional and distance education.

Second, this study draws upon the foundation of transcript analysis as described by

Clifford Adelman. To date, Adelman remains the only scholar to analyze transcripts at a

national level (Adelman, 1999, 2004, 2006). Through his work in the U.S. Department of

Education, Adelman has laid the foundation for and supported the validity of predicting

grades, course completion, and student retention through transcript analysis. More

specifically, Adelman (2006) detailed that student academic momentum can be traced

through transcripts. In essence, academic momentum describes the student’s course of

movement through curriculum as forward, backward, static, or a combination of movements

during an academic term (Adelman, 2006).

Third, this study is guided by the expertise of Linda Serra Hagedorn, who has focused

much of her research specifically on the analysis of community college student transcripts.

The potency of transcript analysis is confirmed at the community college level by Hagedorn

and Kress (2008) when they stated “the only trace of the presence of some community

college students is found in their transcripts.” As Hagedorn and Kress (2008) further

explained, transcripts are the primary markers of student engagement for many community

college students. Unlike traditional students at 4-year institutions, community colleges

struggle to engage their students in campus life. Due to the many work, family, and financial

commitments of community college students, they tend to spend less time on campus. Thus,

transcript analysis provides an ideal opportunity to measure the success of such students. In

this study comparisons will be drawn from the transcript analysis of Hagedorn’s (2005b)

previous framework; utilizing student enrollment records and student demographic

information.


This study’s findings are limited by the unique population within its sample.

Specifically, since DMACC is a community college, there was a dearth of scope and breadth

in the students’ academic majors. In particular, the concentration of Liberal Arts studies was

38

higher than one might expect to find in a four-year institution. Additionally, due to

DMACC’s location in the Midwest, there were a particularly high number of Caucasian

students in comparison to other ethnicities. Moreover, the sample size of this population of

students is relevant only for the academic terms of spring 2005 through fall 2008. Thus, this

study could not be generalized to all community college students. Instead, this study’s

findings are limited to the community college students of DMACC who have enrolled in both

a traditional and online course.

METHODOLOGY

Transcript analysis of enrollment files was performed on a sample of students

enrolled in both traditional and online courses was selected from DMACC. Eleven thousand

nine hundred and seventy students met the criteria of having enrolled in both an online and

traditional course during the time span of fall 2005 through fall 2008.

TREATMENT

Data was gathered through the assistance of the Office of Institutional Research at

DMACC in the form of existing student files. Due to the sensitive and confidential nature of

student files, The Office of Institutional Research at DMACC conducted the query for these

files. DMACC released two files were generated and released from the information present in

the student files. The first was called an enrollment file which included the anonymous

student ID number, the campus in which the student was enrolled, the academic year of the

course, the semester, an indication of whether or not the course had been dropped, the

subject, the course number, the grade received, the number of credits, and the type of course

(online or traditional). This file used the course as the unit of analysis. Thus there were

multiple lines of data for each student. The second file was deemed the demographic file and

contained information on the student’s anonymous ID number, sex, birth date, first term of

enrollment, race, Pell Grant status, residency, major, and classification of instructional

program (CIP) Code. This file used the student as the unit of analysis.

DEPENDENT VARIABLES

Grades and course completion ratios were analyzed as the two dependent variables

due to their consistency throughout previous literature as benchmarks, measuring students’

39

success in distance education. Success in grade achievement was established through

calculating the student’s GPA and distinguished by the student obtaining a course grade of

“C” or higher; whereas, success in course completion ratios (CCR) was measured by dividing

the number of courses attempted into the number of courses completed with a passing grade.

In both GPA and CCR, student measures were calculated for traditional and web success

separately. This method provided the study with unique student goals based upon individual

student’s enrollment. Goals were stated by enrollment in a course and the degree of success

could be further analyzed by GPA.

INDEPENDENT VARIABLES

The demographic variables included for purpose of analysis were sex, age, race, Pell

grant status, and CIP code. Students’ sex was coded as 1 for Males and 2 for Females. Age

was calculated from birthdates and categorized into subgroups from Hagedorn’s age model

(Hagedorn, 2005a; See Appendix B). Students age 17 through 21 were coded as 1, students

age 22 through 30 were coded as 2, students age 31 through 45 were coded as 3, and students

age 46 and over were coded as 4. Race was coded as 1 for Caucasian, 2 for Black, 3 for

Hispanic, 4 for Asian, 5 for Native American, and 6 for unknown or other. If students

received Pell grants they were coded as 1 and those did not receive Pell grants were coded as

0. CIP codes were condensed into the major discipline of study by utilizing the first two

digits of the CIP code family (See Appendix A).

Course mode

In order to account for the discrepancy that occurs among students who have taken

many courses online as opposed to those who have only take a few or even one, this study

developed a technique for categorizing students’ online course enrollment. In this manner,

students can be compared to other students who are most similar to them in course-taking

behaviors. The percent of online courses was calculated for each student. From this

percentage, three categories were developed: traditional-dominant, web-balanced, and web-

dominant. Traditional-dominant students were defined as those students whose online course

percentage was 30 % or lower of their class taking percentage. Traditional-dominant students

were coded as 1. Web-balanced students had an online class percentage of 40 to 60 % and

40

were coded as 2. Web-dominant students had an online class percentage of 70 % or above

and were coded as 3.

DATA ANALYSIS PROCEDURES

Throughout the analysis of this study’s data, both descriptive and inferential statistics

techniques were utilized. All statistical analysis was performed on the Statistical Package for

the Social Sciences (SPSS) 17.0 and the level of significance established was p<0.05.

Initially, frequencies were viewed to create a better picture of the sample. Demographic and

course success variables were then selected based on our research questions to be

investigated further through paired sample T-tests and One-way analysis of variance

(ANOVA). Correlation coefficients were calculated to determine the strength of relationship

between variables and success in online courses.

DISCUSSION

Currently, there exists very little research that examines success in online courses by

community college students. Still fewer studies have simultaneously examined both GPA and

course completion factors defining success in online courses. In either case, those studies that

have examined such relationships did so by comparing courses or instructors and did not use

the student as the study’s control. In doing so, the literature reveals differences when either

the type of course or instructor is held as constant and the success of students are compared

against another group of students. By utilizing the student as the constant, the student’s

performance in a traditional setting is compared to their performance in an online setting.

This minimizes the differences in personal level, intellect, and achievement that can exist

from person to person. Moreover, the literature has underutilized transcript analysis, a

technique that this study employed to analyze the data and examine the research questions.

These research questions will guide the discussion of this study’s findings and provide a

context for comparison to the existing literature.

The first research question prompted analysis of descriptive statistics in order to

reveal information regarding students’ demographic information. What were the

demographic characteristics of students enrolled in both online and traditional courses and

how did they differ in terms of gender, age, ethnicity, and financial aid?

41

Consistent with Bangurah’s (2004) findings, nearly two-thirds of the students were

female. The overwhelming majority of students were Caucasian (85%). This large

discrepancy in ethnic diversity might be indicative of the location as Robinson and Hullinger

(2008) found a startling close resemblance in their Midwest study with 84% of online

students being Caucasian. Additionally, 4,061 (34%) of the students who enrolled in both

online and traditional courses received a Pell Grant. While the Pell Grant is not the only

means of denoting financial aid, it was the only indication within the constraints of students’

enrollment files. Therefore, it is not an inclusive measure of all students who may require

additionally financial assistance. It is likely that the financial aid need is greater than what is

expressed by Pell Grant recipients. Lastly, the ages of the students were sub-divided into four

categories drawing upon Hagedorn’s (2005a) age model. Analysis indicated that 53% of the

total sample was represented in ages 22-30 and the smallest minority was represented by

5.3% of students ages 46 and over.

These demographic markers reinforce the concept that online students are a moving

target (Instructional Technology Council, 2009). The growth and availability of online

classes has ultimately influenced access. Whereas, females were once considered at a

disadvantage due to access (Kirkup & von Prümmer, 1997), they clearly represent the

majority of online students. Moreover, where female students were thought to be limited by

family constraints (Wolf, 1998), online courses may be providing an important alternative.

Similarly, Pell Grant recipients represent a commonly perceived disadvantaged population

that has shown promising enrollment in this study. Public institutions on average have a Pell

Grant student enrollment of 19% (Heller, 2004). Thus this sample shows a rather large

proportion of Pell Grant recipients (34%) indicating that the options of online courses may be

increasingly more attractive to students with financial need and constraints. Lastly, this

sample shows with over half the DMACC population (11,970 students) enrolling in at least

one online course, that online courses are a popular alternative to some traditional courses.

Where some studies indicate that online students are older (Gibson & Graff, 1992;

Thompson, 1998; Doherty, 2006), the majority of students in this sample were ages 22-30

and students ages 17-21 categorized the second largest population. Thus, this study supports

the trends cited by the Instructional Technology Council (2009) that online students are

getting younger. It is likely that due to access, the growth of technology, and the prevalence

42

of online courses that the enrollment growth of students of who are limited by financial,

work, or family constraints will continue; however, the rate at which enrollment increases

may begin to plateau.

The second research question explored the distribution of online and traditional

courses and the differences among students within course mode categories. In order to better

understand the distribution of web and traditional courses, course modes were created to

identify those students who heavily enrolled in traditional courses, web courses, or who

evenly participated in both. In this manner, students can be compared to other students who

are most similar to them in course-taking behaviors. The percent of online courses was

calculated for each student. From this percentage, three categories were developed:

traditional-dominant, web-balanced, and web-dominant. Traditional-dominant students were

defined as those students whose online course percentage was 30% or lower of their class

taking percentage and were coded as 1. Web-balanced students had an online class

percentage of 40 to 60% and were coded as 2. Web-dominant students had an online class

percentage of 70% or above and were coded as 3.

Discrepancy within the literature suggested that inquiry to the difference in students’

age with regard to course mode would be insightful (Gibson & Graff, 1992; Instructional

Technology Council, 2009; Thompson, 1998). The data revealed that while students ages 22-

30 represent the largest number and percentage within each category, that students 31-45 and

students 46 and over demonstrated their highest category percentages within the web-

dominant category. So while Robinson and Hullinger (2008) found that 85% of all students

enrolled in online classes were less than 25 years old, this percentage may not accurately

reflect the motivation and willingness of older students to participate in online courses. It is

likely that younger students will have an overall greater presence by their sheer number in

any course mode category; however, in observing course mode populations, older students

have an increasingly greater presence in online courses.

Similarly, percentages of students who received Pell Grants within each course mode

provided more clarity to understanding course taking behaviors. Within all course modes,

those students who did not receive a Pell Grant were greater in number than those who did.

However, the category where Pell Grant receiving students received the highest total

percentage and had the small disparity compared to students who did not receive a Pell Grant

43

was within the web-dominant category with 42.8%. Again, since Pell Grant students are

disadvantaged due to financial constraints, online courses may provide an opportunity to

balance these financial constraints with work and family.

Ultimately, course mode sample populations may more accurately depict course

taking behavior. By categorizing students by their course mode, analysis can be conducted

revealing the likeness of certain groups of students to enroll more heavily in either traditional

or online courses. Consequentially, this information may be able to uncover the trends and

reveal those student groups who are most likely to succeed in a particular course setting.

In the third research question, differences in demographics and course mode were

called into question when students’ success was measured by GPA in online and traditional

courses. The GPAs of students who are currently enrolled in traditional and online courses

was compared. Across gender, age, course mode, Pell Grant status, and ethnicity a higher

mean GPA score was attained in traditional courses than in web courses. Additionally, the

correlation of each of demographic categories proved to have a significant relationship. This

finding is consistent with Bangurah’s (2004) study which found that among 3,601 students,

traditional GPAs were higher than students’ web-based courses. Comparisons within these

demographic categories confirmed that women did attain higher GPAs in web courses than

men indicating that scholarly arguments which expressed concern that women were not

performing as well as men in online settings may no longer be valid (Kirkup & von

Prümmer, 1997; Wolf, 1998). Instead, Price’s (2006) findings that women are more

successful in online settings than men were supported. Likewise, the positive correlation in

students’ age and GPA found in this study was also cited in the findings of Diaz (2000,

2002).

The only instance where a traditional GPA was not found to be higher than web GPA

was found within CIP code families. Interestingly, Hall (2008) found that the type of class a

student enrolled in was a better predictor of success in online class settings than traditional

survey methods. In this study, CIP code 16 - Foreign Languages, Literature, and Linguistics

showed a slightly higher mean web GPA by a .01 increase. Twenty-one students were

classified in CIP code 16 of a possible 10,295 students. The sample sizes within these CIP

codes varied greatly from six students in CIP code 46 to 6,997 students in CIP code 24.

Respectfully, the two largest populations of CIP codes are represented by 24 – Liberal Arts

44

and Sciences (N=6997) and 52 – Business, Marketing, Management and other Support

Services (N=1661). Since DMACC is a community college, the high concentration of liberal

arts and business students is not alarming. Instead, it would be expected that CIP code

populations would vary more widely at 4-year institutions and that specialized schools would

see a higher concentration among certain CIP code families. Thus, CIP code 16’s (Foreign

Languages) population is small in comparison to the other CIP code categories. However,

since the only instance where a web GPA was higher than a traditional GPA, Hall’s (2008)

study may have a more substantial claim that the type of academic program may be a better

determinate of student success particularly, in institutions where academic offerings vary

more widely.

When ethnicity was considered, again students despite classifications demonstrated a

higher mean GPA in traditional classes than in web classes. Caucasian students attained the

highest GPA in traditional classes (2.76) while Asian students attained the highest web GPA

(2.52) and showed the least amount of standard deviation (SD=1.33). Modest correlation was

demonstrated across all ethnicities. Further analysis using a one-way ANOVA and Tukey

analysis reveal significant differences between ethnic groups. Specifically, Black students

showed a significantly negative difference in web GPA than all other ethnicities and

Hispanic students showed a significantly negative difference in web GPA when compared to

Caucasian and Asian students. The struggle for minority students’ performance at the college

level has been well-documented (Nora & Cabrera, 1996). Drawing from the work of Nettles,

Theony, and Gosman (1986), it might be speculated that online courses do not provide as

strong of support system as do traditional classes. Yet minority student performance at a

largely White institution cannot be measured by GPA alone.

Thus the fourth research question asked, “Among students who take both online and

traditional courses, how do course completion rates differ between the two course types? Are

there significant differences in course completion rates based upon student demographic or

on course mode?” As Diaz (2002) stated attrition in online courses has been one of the main

influential factors that has prompted much of the research in online education. Since students

were used as their own controls, this study sought to compare the course taking behaviors in

conjunction with students’ demographic variables. Course completion ratios (CCRs) were

calculated from the percent of students’ online course enrollments and T-test were performed

45

with student’s demographic descriptors. Students obtained higher CCRs in traditional courses

than they did in web courses across gender, age, course mode, Pell Grant status, and

ethnicity.

However, CCRs in web based courses did fluctuate within demographic categories.

Consistent with the success marked within areas of GPA, females obtained higher CCRs than

male students in both traditional and web courses. While Doherty (2006) did not find gender

to be a determining factor to students online course completion success, this study found

gender to be a significant factor supporting the work of Aragon and Johnson (2008), Nesler

(1999), and Price (2006). However, whereas Aragon and Johnson (2008) did not find age to

be a significant in determining “completers” from “non-completers”, Doherty (2006) did.

Doherty’s findings that students’ age positively correlated to successful course completion

are aligned with this study that CCRs positively correlate with students’ age groups within

web courses. Subsequently, Aragon and Johnson also did not find that financial aid held

significance in student course completion variables. Yet, this study found that those students

receiving Pell Grants (coded as 1) received lower CCRs than those students who did not

receive Pell Grants (coded as 0) in both traditional and web courses. Since Aragon and

Johnson did conduct their study within the Midwest, regional differences would not likely

indicate the difference in findings. However, since financial aid was categorized by “eligible”

or “not eligible”, students did not necessarily have to be receiving financial aid in order to be

place in that specific category. Also, Aragon and Johnson’s study did not specify the type of

financial aid or amounts required. Thus, in this study by using a more stringent measure of

financial aid namely, the Pell Grant, it may be more indicative of the population of students

that requires and utilizes financial aid.

With regard to course mode, web-dominant students had the highest CCR in web

courses (M=.65); although, this was still lower than their traditional CCR (M=.69). Course

mode was also the dependant variable that displayed the highest amount of variance in mean

differences. This variable also contained the lowest mean difference of .04 within Web-

dominant students and the highest mean difference of .16 within Traditional-dominant

students. This suggests that students more consistently complete their online courses with the

more experience they have in online enrollment.

46

Despite Aragon and Johnson’s (2008) conclusion that ethnicity was not indicative of

course completion. This study revealed that all ethnicities fared significantly better in their

traditional CCR than in their Web CCR. Asian students attained the highest web CCR (.61)

and also showed the least amount of standard deviation (.43). The correlation coefficients for

all ethnic groups were found to be significant with moderate strength in relationship. Using a

one-way ANOVA, further analysis revealed significant differences between ethnic groups as

well. Black students had the largest mean difference in CCR across ethnicities in both

traditional and web courses. The mean difference in black students’ CCR was significantly

negative in comparison to Caucasian and Asian students’ web CCRs. Hispanic students also

had significantly lower mean CCRs when compared to Caucasian and Asian students’ web

CCR. As Nora and Cabrera (1996) accounted, numerous studies have examined why

minority students have higher rates of attrition and while there appears to be numerous

contributing factors at play, it is unclear why Asian students would excel in the online

environment. Nora and Cabrera (1996) and Muilenburg and Berge (2005) found that

perceptions could also affect student performance, thus Asian student success could be

related to the perception that Asian students are more apt in math, science, and technology

and thus more attuned to an online setting. Since social factors may also influence minority

student performance (Muilenburg & Berge, 2005) it is hard to ascertain if performance

differences are directly related to being Asian or if they are secondary to the perception of

Asian student performance.

SUMMARY OF FINDINGS

Ultimately this study looked at students’ who enroll in both online and traditional

courses. Thus although these students could not be classified as strictly “online” or

“traditional” students, the majority of these students were female, Caucasian, ages 22-30, and

enrolled in Liberal Arts studies. While the majority of these students did not receive a Pell

Grant, 34% were recipients. It is likely that these demographics can be largely attributed to

the institutional-type, the location of the institution, and convenience offered by online

courses. However, further exploration of student characteristics prompted the development of

course mode categories. Traditional-dominant students constituted the largest population of

students; although, older students demonstrated their highest population percentages in web-

47

balanced and web-dominant categories. Even within these categories, the comparably large

presence of Pell Grant recipients in the web-dominant course mode indicates that access to

technology may becoming easier for disadvantaged students.

This study found significant differences in students’ traditional and online course

performance when compared along lines of gender, age, course mode, Pell grant status, and

ethnicity. All students were more likely to perform better in online settings in terms of mean

GPA and CCR when compared across gender, age, course, mode, Pell grant status, and

ethnicity. In online courses, (a) females outperformed males, (b) older students outperformed

younger students, (c) web-dominant students outperformed traditional-dominant and web-

balanced students, and (d) students who did not receive a Pell Grant outperformed recipients

obtaining both a higher average GPA and CCR. In fact, course mode was positively

correlated with success in online classes, suggesting that it may be a useful indication of

student performance and course-taking behaviors. When ethnicity was considered, Caucasian

performed the best in traditional courses but Asian students outperformed all other ethnicities

in online courses. Consequentially, Hispanic and Black students had the lowest GPAs and

CCRs. There was one instance when students’ mean GPA was higher in an online setting

than in the traditional classroom and this occurred when students were divided by CIP code

families. Yet, in this instance the sample size was severely disproportionate to the total

population.

RECOMMENDATIONS

As over half of DMACC’s student population qualified as having enrolled in both

types of courses, it is probable that studies will see an increasing amount of students who

enroll in both online and traditional courses. Therefore, more studies should consider

exploring students who enroll in both course settings. Likewise, this study indicated that

course mode may predict success in class performance. This method would be especially

helpful in settings where the sheer number within the “majority” can overshadow the

performance of the “minority”. Thus, researchers and practitioners should further explore

students’ course modes in order to better understand student performance and course-taking

behavior. Specifically, it would be interesting to see how differences in ethnicity, age, and

gender are formed when course mode is considered. Additional research should also

48

investigate the extent to which CIP families or academic disciplines are factors in online

success. Ideally, such studies would be conducted at institutions where there are greater

sample populations of academic offerings. Differences in course taking behaviors might

provide further indicators of online success.

Lessons learned from this study would suggest that there are significant differences

between students’ performance in online and traditional courses. Students, in fact, do on

average perform better in traditional courses and there are significant differences in online

performance as measured by demographic characteristics. Therefore, the literature must be

re-evaluated in context specific settings since there is a high degree of conflict within the

literature; namely that demographic variables may constitute differences in traditional versus

online success when measure by GPA and CCR. Transcript analysis would seemingly

provide an excellent vehicle for such research particularly at the community college level

where students are often absent from other more conventionally types of research. However,

other types of research are needed to investigate the underlying reasons behind why certain

types of students are more likely to succeed than others in an online setting. Additionally,

since transcript analysis alone cannot account for differences in course delivery, it would also

be beneficial for future studies to incorporate research that can evaluate the differences by

instructor and/or support services for online students.

Although, students perform best in an online setting, this study displays the

importance of online course offerings. In particular, higher population percentages of

disadvantaged students like Pell Grant recipients are present in web-dominant classes

demonstrating the possibility that such course offerings are more attractive either because of

the financial or time constraints these students face. Thus, it would be recommended that

DMACC evaluate its online course offerings to ascertain if student success is mitigated in

comparison to traditional courses either due to differences in instructor, delivery, or support

services. Ideally, such research combined with existing literature may be able to inform best

practices for online courses and maximize student success in these courses.

Since this study is restricted to one Midwest community college, findings within this

study cannot be generalized. In fact, it is likely that the unique population of represented in

this study is limited to DMACC. Furthermore, other studies have shown that because online

course offerings differ greatly across institutions, it is hard to replicate results and/or draw

49

generalized findings (Allen & Seaman, 2006; Russell, 2001). However, this study does

provide evidence to suggest that similar institutions may have significant differences within

student performance in online and traditional courses and that course mode and academic

majors may provide further clarity in understanding online course-taking trends.

50

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APPENDIX A

CIP CODE FAMILIES

55

Table 15. CIP Code Family Chart

CIP CODE Discipline(s)

01 Agriculture, Agriculture Operations, and Related Sciences

10 Communications Technologies/Technicians and Support Services

11 Computer and Information Sciences and Support Services

12 Personal and Culinary Services

14 Engineering

15 Engineering Technologies and Engineering-Related Fields

16 Foreign Languages, Literatures, and Linguistics

19 Family and Consumer Sciences/Human Sciences

22 Legal Professions and Studies

24 Liberal Arts and Sciences, General Studies and Humanities

26 Biology and Biomedical Sciences

31 Parks, Recreation, Leisure, and Fitness Studies

43

Homeland Security, Law Enforcement, Firefighting and Related

Services

44 Public Administration and Social Service Professions

46 Construction Trades

47 Mechanic and Repair Technologies/Technicians

48 Precision Production

50 Visual and Performing Arts

51 Health Professions and Related Programs

52 Business, Management, Marketing, and Related Support Services

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APPENDIX B

HAGEDORN'S AGE MODEL

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Table 16. Hagedorn's Age Model Table

Category Age in Years

Traditional Aged Students 17-21

Young Adults 22-30

Prime Timers 31-45

Last Chancers 46 and over

transcript analysis of des moines area community college

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