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numbers of “educated” job applicants because they cannot write coherent sentences

or perform simple mathematical calculations. Part of the problem, as Greenspan

notes, is that the educational bureaucracy bars people who have proficiency in math

from teaching that subject, if they lack a degree in education. He refers to a paper for

the Hamilton project of the Brookings Institution that points out that the certification

of teachers – which generally requires a degree in education – has little to do with

whether a teacher is effective (Greenspan, 2007). The Hamilton Project is named after

Alexander Hamilton, an immigrant who was born into poverty and was self-schooled

in his early years, yet rose to become the nation’s first treasury secretary. At the

launching of the Hamilton Project, Senator Barack Obama (D-IL) discussed a paper

that laments the inadequate investment in key growth enhancing areas like education

(Brookings, 2006).

As our primary and secondary schools continue to graduate students lacking

the mix of skills needed to succeed in a high-tech global economy, the U.S. economy

itself becomes hostage to the failure of leadership in American education. It would be

difficult for American higher education to continue to function at a high level if

increasingly large numbers of high school graduates and college freshmen are

unprepared for college. With so much at stake, the accrediting agencies have recently

moved educational assessment to center stage. For example, the Southern Association

of Colleges and Schools, SACS, and the Association to Advance Collegiate Schools

of Business, AACSB, now require the implementation of assessment programs as a

condition for accreditation, or for retaining accreditation.

2



The relatively low performance of our students in standardized tests has raised

an alarm in the U.S. Congress. In 1978, Congress passed Goals 2000: Educate

America Act that aimed to improve educational outcomes and to develop teachers’

instructional skills. More recently, President Bush made the No Child Left behind Act ,

the centerpiece of an educational initiative that compels schools to meet federally

required results (U.S. Department of Education, 2006).

Online education presents unusual challenges and opportunities for educators

and students alike (Moskal, and et al, 2006). Increasingly, students at all educational

levels (primary, secondary, post-secondary, continuing education), participate online

in hybrid, mixed mode, and Web-enhanced face-to-face courses. The increased

capability of digital communication in all formats has brought a strong shift from

people working individually towards people who can work collaboratively

(Larreamendy-Joem, & Leihardt, 2006). As the world moves into the information age

and away from the industrial age, and as the economy becomes progressively more

global, collaboration has become a necessity. The new workplace model often

requires that employees work together and effectively as a team (U.S. Department of

Labor, 1999). It is important to determine to what extent online education empowers

students and imparts to them the skills needed in order to succeed.

There is a growing recognition that social presence affects students’ perceived

learning (Benjamin Kehrwald, 2008).

Background of the Problem

When something new comes along sometimes it is embraced to excess. Online

education is the new kid in the metaphorical block of higher education. Schools face

pressure from students who demand more online courses. They also face budget

3



constrains, and in this regard online courses are a godsend since enrollments and

revenues may grow without a concomitant increase in outlays for physical plant.

Members of the learning community are demanding Internet-based classes for widely

varied reasons. Online courses have gained in popularity among non-traditional

students who appreciate online courses because of the flexibility, including learning

outside the normal classroom schedule constraints ( Ruth Lapsley, Rex Moody 2006).

Numerous nontraditional students are now seeking higher education. An Internet-

based course allows these students to attend class at their convenience. Typically,

nontraditional students are funding their educations themselves and often have limited

financial resources. Internet-based classes can also be less expensive than traditional

on-campus classes, though this is not always the case. Reduced cost and convenience

may mean an education to someone who otherwise would not have such an

opportunity (Almala, 2006). In this pressure-cooker environment, online course

offerings will experience supernormal growth. Therefore, it is extremely important to

attempt to measure exactly what is being gained by this phenomenal growth (Moskal,

and et al, 2006).

Statement of the Problem

Online education is the fastest growing segment of the higher education

industry. This growth is not limited to the United States. According to Debeb (2001)

over 90 million students enrolled worldwide in 986 distance teaching institutions in

2001. He projected this number will grow to at least 120 million by the year 2025

(Debra Spague, 2007). According to a recent study by the Sloan Consortium, an

online education group, nearly 3.48 million students took at least one online course

4



during the fall of 2006 semester compare to 2.35 million reported in 2005 ( Sloan,

2007). The proportion of institutions that believe that online education is important to

their long-term strategy continues to increase, growing from 48.8% of all institutions

in 2003 to 53.5% in 2004, 56% in 2005 and 58.4% in 2006 .Although almost all types

and sizes of institutions show an increase in the importance of online education to

their long term strategy institutions, two year colleges show the highest level of

agreement, 67% in 2006 two year colleges show the highest level of agreement(67%

in 2006) (The Sloan Consortium, 2006).

Given the spectacular growth of online education, it is important to inquire if

this growth will simply exacerbate the educational deficits that others have

documented in traditional education. Will online education contribute to the further

debasement of American education? Is online education neutral in the sense that it is

a perfect substitute for traditional education because it achieves the same results?

These are important questions that go to the heart of issues in educational leadership.

This study attempts to provide a small picture of the overall puzzle via a

survey of online students at Lone Star College-Tomball – a community college

located in Tomball, Texas. Once viewed as the backwater of American education,

community colleges are now in the vanguard as evidenced by the dramatic increase in

enrollments (Greenspan, 2007). Greenspan reports that student enrollment in two-

year colleges rose from 2.1 million in 1969 to 6.5 million in 2004. Community

colleges also have been at the vanguard in offering online classes. A recent report

from the Sloan Foundation indicates that 72% of two-year institutions recognize

online education as an integral part of their long-term growth strategy (Allen and

5



Seaman, 2005). This growth obeys both demand and supply forces. On the supply

side there is evidence that community colleges view online delivery as a way to tap

new markets and expand enrollments, see, e.g., Allen and Seaman (2006). Along the

same lines, Keenan (2007) indicates that some university administrators, in their

hiring decisions, give preference to those whose experience with online education

would ensure growth for the department.

If colleges take the path of least resistance and expand their online course

offerings without taking the necessary steps in training faculty to recognize the

special needs of online students, both students and their prospective employers will

pay a price. Clearly, administrators must be made aware of the requirements needed

to sustain a high quality online learning experience. This is an issue of the highest

priority in educational leadership.

Most institutions (64%) agree with the statement “Students need more

discipline to succeed in an online course than in a face to face course” as the most

significant barrier in online education (The Sloan Consortium, 2007). This is greatest

in private for profit institutions where 78.8% responded that students need more

discipline to succeed in online courses. In community college this issue has been cited

as a very important factor as well. This is an interesting finding, given that

Community Colleges are among those with both the most positive views on online

education and have the highest penetration rates and account for over one-half of all

online enrollments for the last five years (The Sloan Consortium, 2007). Clearly,

these schools do not view the need for increased student discipline as a strong

inhibiting factor for online education.

6



Vermeil & Berge (2000), indicate that a technologically-driven global

economy in the 21st century contributes to the emergence of online education and the

growth of electronic communication, particularly the use of the internet, in

institutions of higher education.

The importance of learners’ attitudes toward the learning environment and the

subject of study have been highlighted by researchers documenting students’

attitudes, perceptions, and experiences is important as it will help faculty improve the

design of online courses, and provide administrator with information about

recruitment and educational assessment (Lao & Gonzales 2005). An investigation is

required to identify the perceptions and attitudes of community college students

toward online-learning (Almala, 2006, & Moskal et. al, 2006).

Instructors spend time and energy developing online courses, with an

assumption that students will take advantage of them and thereby benefit from

utilizing these online resources. This assumption, however, may not be warranted,

since there is little research that has examined how students actually use, perceive and

benefit from online courses (Rosen & Petty, 1997). Moreover, some students may

benefit more from online courses than others due to past internet experience, attitudes

toward computers and learning style. An understanding of how students utilize and

perceive online courses and how different factors influence their use and perceptions

will provide valuable input to instructors. Based on this knowledge, instructors can

justify their effort and design online courses to maximize the utility to all students,

not just those who are particularly computer literate (Zembylas, M., & Varsidas, C.,

2007).

7



While online instruction is gaining popularity, it is not free from criticism.

Many educators and trainers do not support online instruction because they do not

believe it actually solves difficult teaching and learning problems (Conlon, 1997),

while others are concerned about the many barriers that hinder effective online

teaching and learning. These concerns include the changing nature of technology, the

complexity of networked systems, the lack of stability in online learning

environments, and the limited understanding of how much students and instructors

need to know to successfully participate (Brandt, 1996 & Carr-Chellman, 2006).

Online instruction also threatens to commercialize education, isolate students and

faculty, and may reduce standards or even devalue university degrees (Gallick, 1998

& Kraut, et al., 1998).

Seventy- seven percent of prospective college students in the United States

would consider enrolling in a distance education program (Sloan Report, 2005). This

report identified convenience and flexibility as driving consumer interest in online

program. The study also found a great concern about online education. Students

remain concerned about the quality of online education. When asked about this, 38%

of those surveyed were unsure of the quality of online education relative to classroom

instruction, and 29% believed online education is inferior to classroom instruction.

Additionally, some students surveyed were worried that an online degree would not

be as acceptable to potential employers as a more traditional-based degree

(Tabatabaei; Schrottner; & Reichgelt, 2006).

8



Research Questions

1. Does the online learning experience contribute to feelings of isolationamong students?

2. What factors influence student satisfaction in online classes?

3. Is the online learning experience detrimental to students’ motivation?

4. What factors influence learning outcomes?

5. Is perceived learning related to social presence?

6. What are the perceived strengths and weaknesses of online education?

Null Hypotheses

The extensive data set allows testing as many as 50 null hypotheses. In the interest of

parsimony, the following only lists a few of these.

H01. There is no statistically significant difference between the personal

experience of the online course and that of the classroom.

H02. There is no statistically significant relationship between labor force

activity as measured by average weekly hours of work, and the decision

to enroll in online courses.

H03. There is no statistically significant relationship between commuting time

to school and the decision to enroll in online courses.

H04. There is no statistically significant relationship between student

satisfaction with the educational experience and the instructor’s social

presence.

H05. There is no statistical evidence that students feel isolated by the online

experience.

9



H06. There is no statistical evidence that students find the online medium to be

a poor way to communicate with the instructor.


threatening.

H08. There is no statistically significant relationship between perceived

learning and social presence in online education.

Purpose of the Study

The purpose of the study is to examine the role of social presence in online

courses at a community college. Specifically, the study examines the relationship of

social presence in online courses to students’ perceived learning and to their

satisfaction with the instructor. The result of this study will help educational leaders

to more effectively utilize online instruction.

Significance of the Study

The study provides information that administrators and faculty may use to

improve the design and delivery of online education. The study is significant because

online education is the fastest growing segment of higher education. According to a

survey conducted by Eduventures Inc. half of prospective college students are

interested in earning a degree online (The Chronicle oh Higher Education, 2006). For

example, during 2003-2004 online enrollments grew by 18.2 %. In comparison, the

National Center for Education Statistics projections for total enrollment growth for all

degree-granting postsecondary institutions during 2003-2004, ranged from a low of

0.87% to a high of 1.31% (The Sloan Consortium, 2005).

10



Gaining knowledge about the processes and outcomes of online instruction

will help administrators, educators, and researchers make more informed decisions

about future online course development and implementation. With little empirical

knowledge about Internet-based education outcomes, the need for research in this

area is not only timely, but also imperative (Moskal & et. al., 2006 & Caudill,

2007).The online education experience is of recent vintage; therefore, additional

information underscoring students’ satisfaction, problems encountered, and

educational achievement under this new medium should be useful to administrators,

faculty and future students (Tallent-Runnels, et. al., 2006).

Assumptions

1. The sample data is large enough to draw valid statistical inferences.

2. The instrument used to collect student responses is valid.

Delimitations of the Study

The study was conducted on students in the Department of Business and

Technology at Lone Star College-Tomball in Houston, Texas. The results of the study

may be generalized to the population of students at Lone Star College-Tomball.

Because the study was conducted on the students of a college in an area whose

demographic characteristics are not representative of all areas of the country, the

results may not generalize to community college students in other areas.

Limitations of the Study

The empirical results may reflect survivor bias because the drop-out rate in

online classes typically is much higher than that in traditional classes. Based on the

personal experience of the researcher, the drop-out rate in some online classes is as

11



high as 40% to 50%; therefore, the results reflect mainly the survivor’s opinions.

Students were given a chance to volunteer for participation in the study. Only about

53% of those eligible to participate actually completed the survey.

Definition of Terms

Asynchronous: “Communication in which interaction between parties does not take

place simultaneously” (e.g., email, mail, threaded posting) (Glossary, n.d.).

Collaborative Learning: A learning environment in which individual learners support

and add to an emerging pool of knowledge of a group; emphasizes peer relationships

as learners work together creating learning communities (Moore Michael & Kearsley

Greg 2005).

Computer-assisted instruction: Instruction delivered with the assistance of a

computer. The student interacts with the computer and proceeds at his or her own

speed. CAI software is commonly classified into these categories: drill-and-practice;

tutorial; simulation; educational games; problem solving; applications (Glossary,

Oregon Network Education).

Computer-mediated instruction: When computers are used as the media that delivers

the course content from the instructor to the student (e.g., web-based courses, e-mail,

chat rooms, and videoconferencing (Berge, Z.L. and Collins, M. 1995).

Correspondence Course: A distance learning environment where the course content

and communications between the instructor and the student are provided using the

U.S. postal system (Moore and Kearsley, 2005).

Distance Education: “the organizational framework and process of providing

instruction at a distance. Distance education takes place when a teacher and student

12



(s) are physically separated, and technology (i.e., voice, video, data, or print) are used

to bridge the instructional gap” (Willis 1994).

Distance Learning: “education or training offered to learners who are in a different

location than the source or provider of instruction” (Porter, 1997).

Learner Autonomy: "Concept that learners have different capacities for making

decisions regarding their own learning." Relates to the structure and interactive

expectations of a distance education course. (Moore & Kearsley, 1996).

Social Presence: “Social presence theory, a sub-area of communication theory,

postulates that a critical factor of a communication medium is its “social presence,”

which is defined as the “degree of salience of the other person in the (mediated)

interaction and the consequent salience of the interpersonal relationships” (Short &

Chritie, 1976). This is interpreted as the degree to which a person is perceived as

“real” in mediated communication. Originally construed as an inherent feature of

differing media, social presence may also be explored by examining a variety of

issues which may contribute to the social climate of the classroom (Gunawardena,

1995). Consequently, it has been argued that social presence is a factor of both the

medium and the communicators’ perceptions of presence in a sequence of

interactions (Gunawardena, 1995). The construct of social presence in this

construction appears to have subsumed that of teacher immediacy by taking into

consideration the fact that some media, such as computer, interactive video,

audiotape, alter learning environments.

Synchronous: “Communication in which interaction between participants is

simultaneous time (e.g., videoconferencing, chat rooms) (Glossary, n.d.).

13



Telecourse: A strategy of distance learning that provides instruction to the students

using television broadcasts or pre-recorded tapes. (Glossary, Oregon Network

Education).

Web-Enhanced Instructions: the use of course management system tools (i.e.,

Blackboard, WebCT) to augment the traditional face–to-face classroom (Hayward,

Lorna M, 2004).

World Wide Web: “A system of Internet servers that support specially formulated

documents. The documents are formatted in a markup language called HTML (Hyper

Text Markup Language) that supports links to other documents, as well as graphics,

audio, and video files…Not all Internet servers are part of the World Wide Web.”

(Webopedia, n.d.).

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CHAPTER II

REVIEW OF LITERATURE

Overview

Distance education has existed in various forms for centuries; it began with

the invention of writing and evolved to make use of new technologies. This chapter

focuses on the online form that became possible in the last two decades due to

advances in information technology. However, a brief historical overview of the

antecedents of online learning is in order. Sloman (2002) identified four generations

in the evolution of distance learning.

History of Distance Education

Distance learning has been in existence for two centuries in Europe and for

more than one century in the United States. During the past two centuries, the

introduction of new technologies has provided new options for delivering distance

learning opportunities to potential distance learners. Sloman (2002) described the

periods of technological advancement as follows:

First Generation

The medium of communication was text and instruction was by postal

correspondence during the first generation. Distance education programs date from

the nineteenth century (Nasseh, 1997; McIsaac & Gunawardena, 1996). Sir Issac

Pitman, regarded as the first modern distance educator, began teaching shorthand by

correspondence from the English City of Bath in 1840 (Lau, 2000). Charles

Toussaint, a Frenchman, established a correspondence language instruction in the mid

1850 in Europe (Moore & Kearsley, 2005). The University of London founded its

15



correspondence college at around this time, and other private correspondence colleges

began in the late 1880s (Levenburg, n.d.). In the USA, corresponding through the

mail was first used in higher education by the Chautauqua Correspondence College in

1881 (Dede, 1990). This institution offered a 4-year correspondence course of

readings to supplement the summer school (Moore & Kearsley, 2005). By 1910,

International Correspondence Schools in the USA already had around 184,000

students (Glatter & Wedell, 1971).

The motive for the early correspondence educator was to reach people who

wanted to study at home or at work through the use of new technology, cheap and

reliable postal services. This included women, and perhaps for this reason, women

played an essential role in the history of distance education (Moore & Kearsley,

2005). Anna Eliot, established one of the first home study schools in 1873 (Moore &

Kearsley, 2005). According to Nasseh (1997), this school was formed to help women

who were denied formal education. In 1990, Cornell University with the help of

Martha Van developed a program for women in rural up-state New York that enrolled

more than 20,000 women (Cornell University, 2001).

By 1930, 39 American universities, with the enrollment of two million

students, offered correspondence courses with four times the number enrollment in all

colleges, universities and professional schools in the United States (Moore &

Kearsley, 2005). In 1969, the name correspondence education was changed to

“independent study” (Moore & Kearsley, 2005).

While the correspondence course movement opened doors to higher education

for thousands of adults who otherwise would have been excluded from educational

16



advancement opportunities, several critics began to question the effectiveness of

correspondence courses (Glatter & Wedell, 1971). In fact, many elitists looked down

on this form of education and argued that correspondence courses of study were

inferior and designed for those intellectually incapable of functioning within

traditional university environments. For example, William Harper, a Yale University

professor of Hebrew who taught correspondence courses from 1883 to 1891, believed

that even if correspondence studies could supplant oral instruction, they would always

be viewed as a substitute (Matthews, 1999). In response to the criticism, many

institutions began to question and assess the effectiveness of distance learning.

Several colleges and universities decided to reconstruct their correspondence courses

to include direct contact sessions. While the criticism of correspondence courses

seems insignificant, the argument concerning the effectiveness of distance learning

courses has continued from the correspondence course movement to distance learning

delivery methods being used today (Glatter & Wedell, 1971).

Second Generation

Newer technologies have been used since the start of the twentieth century.

During the second generation advanced electronic communications technologies

allowed colleges and universities to deliver correspondence courses using radio and

television (Sloman, 2002). The State University of Iowa began experimenting with

transmitting instructional courses and offered the first credit radio courses as early as

1925, seven years before television was introduced at the New York World's Fair

(Pittman, 1986). Another example was Wisconsin's "School of the Air," it featured

17



ten programs per week to campuses in the 1930s, and continued on-air until the 1970s

(Bianchi, 2002).

In the USA, radio, which was exhibited by commercial broadcasters as a

medium for advertising, did not live up to expectations. In the mid-1920s, the

Department of Education in the UK began to provide radio based instruction, and

soon 10,000 schools were using BBC radio programs to support classroom teachers

(Demiray & Isman, 1999).

Educational television, as a medium of communication, started in 1934. The

State University of Iowa offered courses such as hygiene and astronomy in 1934 and

by 1939 the university’s station had broadcasted about 400 educational programs in

the USA (Moore & Kearsley, 2005). After World War Two, 242 of the 2,053

channels were given to non-commercial use by which some of the best educational

television was offered. Educational television was more successful than radio partly

because of hundreds of millions of dollars contributions by the Ford Foundation

(Moore & Kearsley, 2005). Some of the educational television success includes the

construction of educational television stations by the Federal Educational Television

Facilities Act in 1962, The Corporation for Public Broadcasting in 1965, and the

involvement of community colleges in teaching by television by Chicago TV College

in 1956 (Moore & Kearsley, 2005).

Television and especially radio were used to a greater degree after the war,

though not, according to Cambre (1991) with too much success, owing to the

unimaginitive way in which lectures were filmed and presented.

18



In 1952, the first cable television began to operate (Freed, 1999). In 1970,

California used the funding obtained through a Title I provision to form a task force

to design distance learning courses that would be delivered using television networks

(Freed, 1999). The project involved all California community colleges and the

University of California. The task force developed the concept of the “telecourse,”,

educational programs delivered by broadcast or cable television, a distance learning

vehicle whereby the instructor sits before a camera in a classroom or studio and the

students receive asynchronous or synchronous television transmissions (Freed, 1999).

Because the communications in this environment were one-way, provisions were

developed to provide instructor and student interaction, to arrange student submission

of assignments to the instructor, and to facilitate the return of graded assignments to

the students (Freed, 1999). Among the early leaders in telecourses were the

Appalachian Community Service Network based at the University of Kentucky, The

Pennsylvania State University’s Pennarama Network, the privately funded Mind

Extension University, The Electronic University Network, and the International

University Consortium (Wright, 1991).

California created Coastline Community College, in 1972 in order to

coordinate the development, distribution and licensing of telecourses (Freed, 1999).

Coastline Community College developed telecourses that were broadcasted from a

public television station, to colleges, universities and libraries located in Los Angeles.

By 1976, Coastline Community College was serving 18,500 students within a 150-

square-mile area of southern California (Freed, 1999).

19



Dallas Community College also began to develop telecourse on video cassette

for distribution to other colleges (Freed, 1999). This way the students were able to

view the cassettes at any time and place where there was a video cassette player

available. This strategy allowed students to receive instructions in diverse location.

The commercial success of Dallas Community College motivated Coastline

Community College to produce and license pre-packaged telecourses for other

colleges (Freed, 1999).

Subsequently, other states such as Arizona, Colorado, Oklahoma, and Florida

began to offer variations of video on demand, telecourses (Freed, 1999). In 1972 the

Federal Communications Commission required all cable operators to provide an

educational channel (Moore & Kearsley, 2005). By the 1980s, the Public

Broadcasting Service (PBS), a quasi-government broadcasting studio, began

producing and broadcasting telecourses. However, when PBS began to experience

budget shortfalls in the telcourse production market, the broadcasting company

started purchasing licensed educational content and assumed the role of satellite link

broadcasting the programs to local schools and colleges (Freed, 1999).

Following PBS, several cable television and satellite companies were formed

in the late 1980s to broadcast educational programs to supplement existing

curriculums rather than providing pre-packaged telecourses (Freed, 1999). Because of

the success and journalistic integrity of these programs, there are approximately 240

consortia of public and private educational and creative enterprises in the United

States that produce educational programs that are used by thousands of colleges and

universities (Freed, 1999).

20



Broadcasting and programming channels like Mind Extension University, the

Discovery Channel, the Learning Channel and CNN’s Cable in the Classroom have

increased their market shares via the delivery of instructional video using satellite

communication. For example, Time Warner’s Cable in the Classroom is a coalition of

cable companies that offering programs that educators can record and replay without

paying licensing fees. This attempt to educate primary and secondary school students

represents commitment to public education within the cable industry. For example,

Cable in the Classroom reaches more than 90 percent of the public primary and

secondary schools in the United States (Pittman, 2003; Feasley, 2003; Bunker, 2003).

Third Generation

Distance education went through critical changes in the late 1960s and early

1970s due to the introduction of new technology, leading to new instructional

techniques and new educational theories (Moore & Kearsley, 2005). The two most

important projects were the University of Wisconsin’s Articulated Instructional

Media Project (AIM) in the USA and the Open University of Great Britain. The

University of Wisconsin, at the forefront of progress, created the Articulated

Instructional Media project (AIM), which attempted to be a complete system of

distance education, including broadcast media, correspondence and telephone (Cook,

2000). The purpose of AIM was to experiment with the idea of joining various

communication technologies to offer high quality, low cost education to off- campus

students. AIM, which was funded by the Carnegie Corporation from 1964 to 1968

and directed by Charles Wedemeyer at the University of Wisconsin, represented a

historic milestone and turning point in the history of distance education in the USA

21



(Moore & Kearsley, 2005). In the UK, the Labor Government also looked to

television to provide distance learning, and approved the setting up of the so-called

"University of the air", renamed the Open University (OU), based in Milton Keynes

(OU, 2003a). It has become the UK's largest university, with over 200,000 domestic

and international adult students (OU, 2003a; OU, 2003b). The Open University model

has been adopted by many countries in both the developed and developing world

(Keegan, 1996). The Open University was created in 1969 and was identical to AIM,

according to Wedemeyer (1982). The UK Open University is considered a world

class university and a model of a total systems approach to distance education. This is

100 percent distance education, charging students about 40 percent of the average

cost of the traditional universities and has first come first service policy. It is ranked

near the top of UK universities in both teaching and research. The idea of the UK

Open University system has spread globally. Countries such as China, India,

Indonesia, Iran, Korea, Spain, Thailand, and Turkey adopted the UKOU model to

offer distance education courses. These universities are usually “mega-universities”;

meaning they have more than 100,000 students (Daniel, 1996).

The United State is one of the few countries that did not set up a national

open university. However, the third generation of distance learning started after

educators began to explore the possibilities of using new technologies which would

offer synchronous communications between teacher and students (Sloman, 2002).

During the 1970’s, Athabasca University in Canada began to provide distance

learning opportunities that combines synchronous telephone-based communication

technologies and home study techniques (Dede, 1990). Following this lead, Nova

22



University, University of Wisconsin, Empire State College, and Oklahoma State

University began to offer distance learning courses that employed teleconferencing

technologies, a method of communication that enabled colleges to offer synchronous

interaction among multiple locations using teleconferencing (Dede, 1990). This

learning environment allowed the instructor and the student to communicate audibly

in real-time, and represented a breakthrough over the first and second generations of

distance learning. Nevertheless, skeptics believed that this environment was inferior

to the traditional learning environment (Dede, 1990). This spurred instructional

designers to continue searching for a learning environment that closely emulated the

intimacy of the traditional education setting.

During the 1980’s educators began to take advantage of the availability of

cable television to offer videoconference courses between different campuses

(Hancock, 1999). This environment allows the instructor and students to

communicate in real-time using cameras and sound production technologies. This

way, the instructor and the student may be in separate classrooms or studios that are

capable of transmitting and receiving live visual and audible communications.

By the 1990s, most colleges and universities had discarded the postal service

delivery mode (Hancock, 1999). Instead, colleges and universities shifted to

teleconferencing and videoconferencing technologies. Iowa and North Carolina

developed comprehensive networks that connected public schools, community

colleges, and universities providing videoconferencing sessions. In this environment

one instructor could reach students enrolled in the same class at different institutions.

This feature was beneficial for colleges seeking to offer courses unavailable on their

23



campuses due to financial constraints or the lack of qualified instructors. This

modality has gained popularity. For example, the University of Kebangsaan in

Malaysia installed a videoconferencing system that enables them to communicate

with universities located in New Zealand and Canada (Hancock, 1999).

Fourth Generation

The fourth generation of distance learning began in the late 1950’s when

technology-based distance learning started using mainframe computers to meet the

demands of companies, such as IBM. For example, in 1959 IBM assisted in the

development of the first program to teach mathematics (Inglis et al., 1999). In 1963,

Stanford University and IBM released COURSEWRITER , a programming language

software, designed for using computers to deliver individual lessons (Pittman, 2003;

Feasley, 2003; Bunker, 2003). Computer-assisted instruction served to deliver

interactive, responsive, and convenient education. Therefore, teaching and learning

were based on asynchronous activities without limitations of time and place.

In the 1990’s, the University of Illinois developed a computer-assisted

instruction system using PLATO, Programmed Logic for Automatic Teaching (Inglis

et al., 1999). The university provided student access to PLATO using terminals for

mainframe computer-assisted instruction. These were developed to provide college-

level instruction and to provide supplemental instruction to students located at

correctional institutions and basic skills centers. This approach continued until the

introduction of the first personal computer in the early 1980’s (Pittman, 2003;

Feasley, 2003; Bunker, 2003).The advent of the personal computer promoted the

widespread usage of computers in colleges and universities. Since then, distance

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learning delivery methods have been adapted to numerous developing computer-

based technologies that include powerful operating systems, networking technologies

and compact discs (Pittman, 2003).

The advent of the internet in the mid 1990’s allowed colleges to deliver web-

based programs and courses directly to the workstations, residences, and mobile

computers of learners. Increasingly, distance learning utilizes computer-mediated

technologies (Driscoll, 2002; Sloman, 2002). Today, colleges view distance learning

as an integral part of higher education as they seek to enter the highly skilled

workforce (Pantazis, 2002). Enthusiasm is not universal; critics argue that most

distance learning courses offered by postsecondary institutions via the Internet are

crude. Different opinions prevail. Among the critics, Shank and Sitze (2004) argue

that most web-based courses are sub-standard because they are based largely on

lecture notes, assignments and reserved readings and fail to recognize the unique

needs of distance learners. On the other hand, private colleges such as Capella

University, Cardean University, Nova Southeastern University, and the University of

Phoenix allegedly design their web-based courses using experts in the fields of adult

learning theory, instructional design, and technology (Farrell, 2003). These

institutions seek to deliver web-based courses that are both entertaining and effective

in transferring knowledge (Sloman, 2002).

Web-based instruction allows postsecondary institutions to provide a greater

volume of courses to more students at a lower cost than traditional classroom-based

instruction (Silberman, 1998). The business model of the University of Phoenix is

based on a new generation of educational delivery system that is not constrained by

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the traditional education. In 1994, the University of Phoenix became the largest and

fastest growing private university in the United States with 145,000 students of which

63,500 are enrolled in distance learning programs (University of Phoenix, 2004).

Next Generation

Futurists predict that by 2010 the next generation of distance learning will

allow unobtrusive broadband access to interactive courses which will be cheaper and

more convenient than the same course offered on college campuses (Dunn, 2000).

During the next decade, distance learning will rely on broadband wireless

technologies that will allow students access to learning programs at any place and any

time. The distance learning student of the future will be able to travel anywhere and

receive access to email, electronic documents, and media, audio and video clips. The

student will use voice activated handheld computers to participate in interactive

videoconference sessions with classmates and save a copy of the transcript from the

sessions on an interactive course web site. Distance learning courses will become

student-centered, whereby students will have access to interactive computer-mediated

technologies that continuously assess and adapt to the student’s aptitude (Driscoll,

2002). For example, a student will log into class and select a simulated lesson that

constantly adapts to his/her personal level of achievement. Dunn (2000) predicts that

by 2018 computers will enable international students to communicate with their

foreign instructors or peers in their native language using computers that will

accurately translate languages in real-time.

Those who view online education as a perfect substitute for traditional

education argue that the way in which colleges and universities adapt to the changes

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in computer-mediated course delivery technologies may determine whether they

remain competitive in the future. Given the demand and response, education will

become a commodity, making consumers of students and putting them in a position to

shop for the best deal (Dubois, 1996; Johnstone, Ewell, & Paulson, 2002).

A different outcome is possible – one in which employers find online-

educated graduates inferior to those graduating from traditional university. Under this

scenario graduates of online colleges may face poorer employment prospects and

lower salaries than graduates of traditional universities. If so, online colleges will

begin to experience difficulty attracting and retaining bright and ambitious students.

Of course, if U.S. public colleges succumb to the same failure of leadership endemic

in primary and secondary education, and seek to imitate the for-profit providers of

online degrees, this will simply accelerate the outsourcing of skilled jobs to other

countries.

As innovative for-profit universities become more and more successful, there

is evidence that political leaders throughout the nation are beginning to question the

efficiency and effectiveness of state-supported higher education during an era when

business and industry are restructuring to meet the demands of globalization, a

phenomenon whereby the barriers of time and space separating individuals and places

are diminished (Jones, 2003; Levin, 2001). McGuinness (2001) postulates that

college and university leaders are ill-prepared to deal with the education reform

movements triggered by the shift in political and economic power at the state level.

McGuinnes reports the gap between the external and internal definitions and

expectations for quality assurance is placing untenable political pressure on

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postsecondary institutions. Dunn (2000) projected that the number of degree-granting

distance learning institutions will continue to grow, while the number of traditional

campuses will decline. By 2025, half of today’s existing independent colleges will be

closed, merged, or significantly altered in mission (Dunn, 2000). The combination of

growing markets, increased costs and increased competition will force postsecondary

institutions to become market driven in order to compete with institutions such as

Capella University, Cardean University, Nova Southeastern University, and the

University of Phoenix (Dunn, 2000). In order to understand the challenges that the

future will bring it is necessary to review the theories of Distance Learning.

Theories of Distance Learning

There are four theoretical frameworks that have provided a framework of

understanding for Distance Education: Transactional Distance, Interactivity Theory,

Social Context, and Control.

1. Transactional Distance

In the early 1970s Michael Moore came to the conclusion that the two key

factors in independent learning are structure and dialog (Moore, 1973). Moore

defined structure as "a measure of an educational program's responsiveness to

learners’ individual needs." He defined dialog as "the extent to which, in any

educational program, learner and educator are able to respond to each other." Put

another way, structure refers to the design of the instructional program while dialog

refers to the interaction through communication between the learner and the

educator.A transaction is a mutual exchange between parties. Moore recognized that

in a course high in structure, such as a pure lecture course, there is generally little

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dialog between educator, learner and transactional distance is maximized.

Conversely, as dialog is increased, the structure decreases, thereby minimizing the

transactional distance between educator and learner (Moore, 1973).

Saba and Shearer (1994) supported this dynamic relationship between

structure and dialog by conducting a controlled experimentation. This research

supported the concept that the distance between teacher and learner in distance

education was not a geographical distance, but a pedagogical distance determined by

the balance of structure and dialog.

Instructional designers of distance education should aim for the optimum

balance of structure and dialog. It is important to note that there is no magic ratio of

structure to dialog that will fit every course. It is the task of the designer to evaluate

and plan for dialog and structure as dictated by the nature of the course. Although it

originated in the domain of distance education, the concept of acceptable balance of

structure and dialog is the key to all successful instruction and learning. Care must be

taken during the design of instruction to account for this balance (Bird, 2007).

Distance is not determined geographically, but by the variety of transactions

that occur between the leaner and teacher. This continuum challenges the idea of

traditional versus distance education. Saba and Shearer (1994) conclude that as

dialogue increases, transactional distance decreases. It is not location that determines

the effect of instruction, rather the interaction between student and instructor.

Transactional distance was advanced by Michael Moore (1990). Here, "distance" is

determined by the amount of communication or interaction which occurs between

learner and instructor, and the amount of structure which exists in the design of the

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course. Greater transactional distance occurs when a course has more structure and

less communication (or interaction). A continuum of transactions might exist in this

model, from less distant, where there is greater interaction and less structure, to more

distant where there may be less interaction and more structure. There is, these days,

the problem of combining of distance learning with e-learning. It could be argued that

e-learning provides such a high level of interaction that the "distance" is necessarily

smaller. According to Martindale (2002), "'transactional distance' requires a learner,

teacher, and a communication channel" (p.4). Different teaching situations involving

different transactional distances require different instructional techniques. Stein et al.,

(2005) used Moore's (1993) theory of transactional distance as a conceptual

framework, to study learners in six courses that varied by course format, structure,

and opportunities for interaction. The obtained result indicated that learner

satisfaction with the course structure-activities, assignments, and instructor guidance

and encouragement increased greatly with perceived knowledge gained. Interaction

was highly correlated with structure. Interactions initiated by the learners contributed

to their satisfaction with perceived knowledge gained. Technical expertise had no

effect on satisfaction with perceived knowledge gained.

Dron (2007) in his book “Control and Constraint in E-learning” looks

backward at the theoretical balances between structure, control, power and

transactional distance. He believes that many of these concepts are fuzzy, hard to

prove empirically and often misunderstood or misinterpreted by both authors and

readers. Dron creates the six forms of interaction: learner-teacher; learner-control;

teacher-content; teacher-teacher; and content-content.

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2. Interaction Theory

Moore (1989) discusses three types of relationships, which are essential in

distance education.

a. Learner -instructor (dialogue between the student & teacher). The instructor

should be the facilitator of knowledge and provides feedback, support and

encouragement to the student.

b. Learner- content (How students obtain intellectual information from the

text).The manner how the student obtains the information. Learner should be

given the content so that they can construct their own understanding.

c. Learner-learner (Exchange of ideas between the students)

3. Social Context

McIsaac (1993) discusses the social context where the learning takes place

and how the environment affects motivation, attitudes, teaching, and learning.

McIsaac and Gunawardena (1996) summarize the characteristics of distance

education from their own review of the literature as: education imparted where the

learner is physically separated from the teacher. Many writers have looked at the

higher level of independence or "learner control" which is a feature of distance

education (Holmberg, 1995). Baynton (1992) developed a model to examine the

concept of learner control in terms of independence, competence and support. She

notes that "control" is more than "independence". It is also affected by competence

(ability and skill), and support (both human and material).

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4. Control (Locus of control)

According to Moore (1973), this theory relates to how the student views his

role in learning; whether the student has an internal locus of control or an external

locus. Does the student feel that his/her academic success is based on his own

personal accomplishments or that success is related to outside forces or fate?

Online Learning: Potential Advantages and Drawbacks

To review the advantages and drawbacks of online learning we presently turn

to: Online Learning: Potential Advantages, Drawbacks and Enrollment Growth.

Potential Advantages

Online learning has been defined as anywhere, anytime computer-mediated

instruction (Harasim, 1990). According to The Sloan Consortium (2006), 62 percent

of academic leaders believe that the quality of online instruction is the same or

superior to that of traditional face to face classes. According to Harasim (1990) and

McComb (1993), online learning has the characteristics of asynchronicity (time and

place independent), text-based and many-to-many communication with vast and

efficient information access. Progressive online multimedia environments facilitate

the effective delivery of online instruction because they mimic the dynamics involved

in high-quality, face-to-face classroom instruction. For example, well-designed

Internet-based instructional models will continue to flourish because they support

problem solving and allow detail-oriented instructional guidance using highly

structured tasks (Larreamendy-Joerns & Leinhardt, 2006). According to Carr-

Chellman (2006), students will not see lower course fees but they will at least save on

transportation and child care. Some of the advocates believe that the efficiency in

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online education outweighs the quality. Due to these attributes of online learning, it is

considered to have the following potential advantages for student learning.

Flexibility, Convenience of Access, and Sense of Control

Students reported flexibility, convenience of access and sense of control as

reasons to register for an online course instead of a face-to-face course (Richards &

Ridley, 1997). Since online instruction frees students from the constraints of time and

space, learners find it more convenient to take courses or earn degrees online while

they work full or part-time (Burge, 1994; Harasim, 1987; Hiltz, 1997). At the same

time, learners reported increased learning as a result of the availability and flexibility

of the classes (Harasim, 1987). The flexibility of access, for example, allows students

control over the learning situation so that they can learn at the time when they

function best. They can control the nature and time of their interactions with learning

materials and participate to the degree they wish (Harasim, 1987; Burge, 1994). Thus

there is more time for reflection and thinking in online courses (Garrison, 1997;

Harasim, 1990). Online instruction has given a diverse group of citizens increased

access to educational opportunities, reducing educational inequality. This could be

beneficial for non-native English speakers since students can control the time they

take to reflect, and compose their postings in online classes (Bulger, 2005). The

online learning has extended new advantages to business-people, such as the ability to

complete continuing education courses online (Bulger, 2005).

Democratic Learning Environment

Due to the text basis of most online courses, some physical and social cues of

students, such as appearance, gender, race, education, and social status, are reduced.

33



The absence of these physical and social status cues diminishes stereotyping

associated with these cues, and may overcome gender and race-based discrimination

(Harasim, 1987; McCreary, 1990; Ruberg, Moore, & Taylor, 1996). According to

Carr-Chellman (2006), the underlying spirit of open access in online education, holds

the promise of equity. Modern technology brings education to students rather than

forcing them to subsidize fancy campuses. Online education makes it possible for

students all over the world to study at the prestigious schools without leaving their

home (Moore & Kearsley, 2005). The online instructional environment can also

provide for the equal participation of all students. Since in online environments there

is no competition for “air time,” students’ anxiety levels are lowered (Harasim, 1987).

Students with different learning styles or personalities can participate as they wish.

This is especially true for students who are shy or less assertive and for those whose

native language is not English (Chen, 1999; Harasim, 1987; Kamhi-Stein, 2000; Yi

& Majima, 1993). More equal opportunities for participation are provided and

students feel more confident participating in the discussion (Chen, 1999; Kamhi-

Stein, 2000; Warschauer, 1997; Yi & Majima, 1993).

Enhanced Level of Interactivity within the Learning Community

According to Harasim (1990), Mason and Kaye (1990), and Stacey (1997),

online education is distinguished by the social nature of online learning environments

and their potential for creating interactive communities of learners. Many students

reported that they had more communication in online classes than in traditional, face-

to-face classes (Schutte, 1996; Turgeon, Biase & Miller, 2000). Generally speaking,

students possessing computer training and experience were more satisfied with online

34



courses (Kim & Moore, 2005; Tallent-Runnels et al., 2006). Students also felt that

asynchronous learning networks provided them with better access to their professors,

and that they got more individual attention from instructors online (Guernsey, 1998;

Hiltz, 1997). Harasim (1987) reported students felt not only that there was more

interaction, but also that there was more intense learning interaction in an online

learning situation, At the same time, teachers likewise reported online education

provided time for details and individualized instruction with students (Cifuentes &

Shih, 2001; Kashy, Thoennessen, Albertelli, & Tsai, 2000). After online training,

teachers changed their attitudes toward online instruction as well, and considered it

more participatory and interactive than face-to-face instruction (Gold, 2001). Many

teachers have reported being more satisfied with online instruction because of

increased interaction with students or among students (Hartman, Dziuban, & Moskal,

2000; Fredericksen et al., 2000). Indeed, researchers agree that online education

supports interactive group communication with all its social, affective, and cognitive

benefits (Burge, 1994; Beauvois & Eledge, 1995/96).

Potential for Collaborative Learning

The online environment supports collaborative learning among diverse and

dispersed learners (Garrison, 1997, Jonassen, Davidson, Collins, Campbell, & Haag,

1995). The nature of many-to-many communication in an asynchronous learning

network allows students access to large and diverse group knowledge and support

(Burge, 1994; Kamhi-Stein, 2000). Students are more active in group work and

discussion (Barreau, Eslinger, Mcgoff, & Tonnesen, 1993 & Hiltz, 1997). Kamhi-

Stein (2000) found online instruction promoted a high degree of peer support,

35



assistance, and collaboration. Online instruction may be more learner-centered. The

instructor is more a facilitator than a disseminator of learning (Cifuentes & Shih,

2001; Kamhi-Stein, 2000) and online discussions are driven by the needs and

interests of the students. With multiple perspectives on given topics and access to a

vast array of information, students are actively engaged in context-rich, social

negotiation of ideas. Harasim (1987) stated that she found within online environments

an increased sense of group responsibility from students and increased cooperation

among students. Hiltz (1997) reported 55% of students felt more motivated to work

hard on their assignments because others would be reading them. Students were

found to have formed good working relationships, felt equality in their contributions,

and produced higher quality projects within the groups (Barreau et al, 1993; Burge,

1994; McConnell, 1994).

Facilitates Higher Level Learning

The nature of text-based communication in most online education makes it

necessary for learners to communicate through writing and allows time for reflection

on that writing. These processes encourage higher-level learning and promote clearer

and more process-orientated thinking and encourage analysis, synthesis, and

evaluation (Garrison, 1997; Jonassen, 1996). Wegner et al. (1999) reported that

students in their internet-based instruction group felt that they had acquired skills of

collaboration, problem solving, locating and using information, and communication.

In addition, text-based asynchronous discussion produces a record of the discussion to

which students can refer to reflect on negotiated meanings and to find the thread of

their thinking and their own position within it (Ganeva, 1999; Harasim, 1987).

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Through this process, students are engaged in the self-regulatory, self-mediated and

self-aware learning processes that constructivists suggest support optimal learning.

In summary, it is considered that online learning has the potential for

encouraging collaboration, interactivity, and reflectivity which are important for

knowledge construction according to social learning theory (Vygotsky, 1978). In

addition, empirical studies in online learning found that students who have high levels

of interaction with peers and the instructor regarded their online classes as more

satisfactory and of higher quality than their face-to-face classes (Hiltz, 1990;

Picciano, 2002; Swan, 2002a; Swan, 2002b; & Berge, 1999). They also reported that

interaction is important to learner satisfaction and that interaction assists in

maintaining student persistence in courses. Northrup (2002) found, in her study, that

students reported that instructor’s timely feedback was valued most by participants. In

Jiang and Ting’s (2000) study, students reported better learning experiences in

courses which emphasized online discussion. It can be concluded that discussions

play an important role in online learning environments and more discussions among

students occurred in online environments than in face-to-face classrooms. Teachers

act more as facilitators or guides in online discussions.

Induction for online distance learning requires that students are successfully

introduced to the new learning environment for self-study and are also introduced to

online group work and activities. One of the perceived benefits of CMC for distance

education is the opportunity it brings for geographically distanced students to

communicate and interact with peers (Vrasidas & McIsaac, 1999). Hailed as "third

generation distance learning" (Nipper, 1989, p. 67), computer conferencing offers the

37



potential for active group participation and reconstructs distance learning as a social

process. While pre-online distance education has been reported by some as an

autonomous, isolated experience (Eastmond, 1995; Stacey, 1997) others have

recognized its capacity for interactivity, notably activities and questions in print-

based texts (Lockwood, 1992; Rountree, 1990), audio teleconferencing (Robertson,

1987), and student participation at residential study schools (Morgan & Thorpe,

1993). While interactivity need not necessarily be deployed in distance education,

following a revival of interest in Vygotskian social constructivism, student interaction

has come to be regarded as significant in facilitating and consolidating learning

(Garrison, 1992; Laurillard, 1993).

According to McConnell (1992), a clear stance taken in the literature reviewed

is that collaborative learning methods encourage dialogue, which facilitates "deeper"

understanding, greater skill development, and the construction of knowledge (as cited

in Nixon & Salmon, 1996; Fung, 2004; Garrison, 1993; Hodgson & McConnell,

1995; Marjanovic, 1999). It is in this learning space that students often find

reassurance, build relationships, and use each other as a "cognitive resource" (Wilson

& Whitelock, 1998, p. 92). Indeed, online communication between distance students

is purported by some authors as lessening student's feelings of isolation; bolstering

peer support networks; and producing more reflective, critical and informed written

responses due to the medium's asynchronicity and/or more effective synthesis of

knowledge (Eastmond, 1995; Garrison & Anderson, 2003; Rovai, 2001; Rovai, 2002;

Wegner et al., 1999). The benefits and fruitfulness of online communities, however,

are doubted by other commentators who report inconvenience, frustration, and

38



difficulties in access for some students; information overload; the overly time-

consuming nature of asynchronous communication; forced participation; and/or low

levels of user involvement (Hara & Kling, 2000). In addition to assessing student

learning, it is equally important to assess the quality of the learner's experience

commonly referred to as “student satisfaction” (Moskal, Dziuban, Upchurch,

Hatrman, & Truman, 2006).

Potential Problems: Failure of Leadership

One potential problem that begs for attention is that online education may

increasingly fall pray to a marketing strategy − yet another way to increase

enrollment. When educational success is measured by the number of new students,

we witness the ultimate failure of educational leadership. The farmer who grows the

most potatoes may be the object of envy, but students are not potatoes. Devoid of

educational assessment and without empirical studies documenting what is being

gained and lost, marketing-driven growth may be more damaging to the U.S. than

what foreign terrorists could do.

Online Learning Barriers

The literature also reveals some drawbacks of online education. Pajo (2001)

identified a number of barriers to uptake of web-technology by university staff. Chief

among these were the time required in learning how to use web-based technology and

develop appropriate courses, the lack of training, and monitoring of web-based

teaching.

Some students may not be suited for the online environment. These would

include students who quit when something goes wrong and those who are not self-

39



motivated, disciplined, or committed enough to learning (Weiss, 2000). It takes a lot

of discipline to regularly log on and complete assignments on time. Online courses

out of sight can also be out of mind and students who get behind later get

overwhelmed (Frankola, 2001).

According to Sprague et al. (2007), the growth in online education courses

reduces the enrollment in traditional programs especially master degree programs.

There is a problem of how best to evaluate online courses according to Chapman

(2006). Lack of community and student isolation in online education needs a lot of

attention according to Sprague et al. (2007). Social isolation has become a source of

depression, frustration, and decreasing social interaction in online education (Kraut et

al., 1998). Online programs have the potential to be efficient but the impact on

effectiveness and quality is not addressed by web-based educators. According to

Berube (1998), when you are teaching 10,000 students by satellite or via internet, you

basically cannot read, make necessary corrections, provide recommendation and

grade their papers, work and advise them on their career, and advise them on their

courses. Personal, individual contact with each student is one of the most inefficient

and costly services that a university can provide. According to Carr-Chellman (2006),

online programs attract students that are looking for answers that can apply to their

jobs, not who are interested in learning about theoretical constructs. Therefore, the

distance learner is not suited to traditional degree programs especially at the doctoral

level. Perhaps one of the most important issues in online education is the area of

intellectual property (Carr-Chellman, 2006). Once a faculty posts the course material

online, the knowledge and the course design skills will be transferred to the machine

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and will be placed in the hand of the administration which is now in the position to

hire less skilled and cheaper instructors to deliver the prepackaged course.

Powell et al. (1990) developed a conceptual framework that attempts to

account for some aforemetioned drawbacks the framework focuses on predisposing

characteristics of student success. Powell classified the factors contributing to success

and retention in distance education into two general categories. These are:

1. Predisposing characteristics: including prior education, socio-economic and

demographic status, and motivational and other personal attributes.

2. Institutional: including quality and difficulty of instructional materials,

access to and quality of tutorial support and the administrative and other support

service provided.

In their study of drop-outs from a Hellenic Open University course in

education studies, Vergidis and Panagiotakopoulos (2002) found that the main

problems stemmed from family or work obligations, rather than from factors intrinsic

to the course or its delivery. Other studies (Carr et al., 1996; Goodman et al., 1990;

Lazin & Neumann, 1991) all indicate that demographic variables were less predictive

of completing an educational program than attitude and the degree of social support

received.

Some work has been carried out with regard to barriers that prevent full

participation in online courses and ITV, even for students who complete them.

Howard, Caroline, Discenza, Richard, Schenk, and Karen (2002), identified several

barriers with regard to online interaction, the school principal was an insurmountable

social-psychological barrier. Technical problems were also blamed for a lack of

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interaction, with the sound often of poor quality and difficulties in manipulating

cameras and microphones. They also noted a certain degree of alienation, brought

about by the lack of physical presence and the reluctance to use the technology. The

latter finding reflects earlier research by, for example, Comeaux (1995) and McHenry

and Bozik (1995), which indicated low levels of interactivity often resulting from

technological problems. Most concerns are based upon the perceived lack of social

interaction and immediate feedback, inability to address the learning needs of a

diverse group of students, lack of transparent academic activities by for-profit online

schools (e.g., diploma mills).

According to The Sloan Consortium (2006), about two-thirds of the

administrators believe that student discipline is a critical barrier in online education.

Also faculty issues, such as acceptance of online education and the need for greater

time and effort to teach online are important barriers.

Enrollment Growth

According to Clouse and Goodin (2001-2002), growth in online education

resulted from three paradigm shifts. The first shift was due to the growth in

investment, primarily from governmental sources, toward integrating information

technology with instruction. The second paradigm was the tremendous growth in the

number of college students demanding distance learning courses. Finally, the growth

in the number of research studies in online education.

The literature reveals that enrollment growth in online courses has been

spectacular (Tham & Werner, 2005). Allen and Seaman (2004) observed that

enrollments in online courses continue to grow at a rate more rapid than predicted by

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institutions of higher education. With e-learning now occupying about 20 percent of

corporate training, a recent study by Sloan Consortium (2005), concluded that it

would be useful for company training executives to explore ways in which academic

and corporate online learning officers might collaborate. A report from (Sloan

Consortium, 2005) showed a 22.9% overall increase in the number of students taking

one or more online courses, growing from 1.60 to 1.98 million students. Schools were

optimistic about future growth as well, with 74.8% reporting that they expected their

online enrollments to increase (Sloan Consortium, 2005).

According to the Sloan Consortium (2006), 58.4 % of all institutions rated

online education as important for their long-term strategy. This is not consistent

across all types of institutions; small schools, and private nonprofit institutions were

the least likely to support this view (Sloan Consortium, 2006). The evidence from

higher education’s academic leaders suggests that there is a strong trend upwards in

considering online education as part of a school’s long-term strategy (Sloan

Consortium, 2006). There is growth among all types of schools: The overall percent

of schools identifying online education as a critical long-term strategy grew from

48.8% in 2003 to 58.4% in 2006 (Sloan Consortium, 2006). The largest increases

were seen in Associates degree institutions where 67.0% now agree that it is part of

their institution’s long-term strategy, up from 57.7% in 2003 (Sloan Consortium,

2006). Associate institutions are making more inroads in online education than four-

year institutions.

According to Sloan Consortium, (2006) larger institutions are most likely to

offer more online programs. Institutions with enrollments of over 15,000, offer twice

43



as many online programs as the smallest institutions. The difference in online

programs offerings among institutions is interesting. Doctoral/Research institutions

offer more online programs than four year degree institutions.

The increase in the overall number of online learners was the same in 2007 as

in 2006 (an increase of around 360,000 each year) for an overall enrollment growth

rate of 18.2%. This growth rate greatly exceeds the overall growth rate in the higher

education student body. The overall size of the higher education student body is about

17 million with online students representing 3.48 million or almost 20 percent of total

enrollments in higher education (Sloan, 2007).

According to The Sloan Consortium (2006), 62 percent of Chief Academic

Officers believe that online courses are of equal quality or better than face-to-face

courses, and that students are as satisfied with online as with face-to-face courses.

The largest schools continue to be the most positive toward online education.

Chief Academic Officers believe, in general, that it takes more effort to teach

online. A large majority of them (63.6%) believe that it takes more discipline for a

student to succeed in an online course. Although online education continues to

penetrate into all types of institutions, a relatively stable minority of Chief Academic

Officers (28% in 2003 compared with 31% in 2005) continue to believe that their

faculty fully accept the value and legitimacy of online education. Eighty-two percent

of respondents believe that it is no more difficult to evaluate the quality of an online

course than one delivered face-to-face (Sloan Consortium 2006).

In 2006 (The Chronicle of Higher Education), reported that Congress is about

to remove the "50-percent rule," which bars any college that provides more than half

44



of its courses via distance education from participating in federal student-aid

programs. Elimination of the 50-percent rule removes one more barrier to the

acceptance of on-line distance education as a legitimate vehicle for the delivery of

degree programs. As these barriers fall, more on-line degree programs will be

developed (The Sloan Consortium, 2006).

In 2006 (Sloan), eighty-nine percent of all institutions offer face-to-face

undergraduate-level courses, and 55% of all institutions offer online undergraduate-

level courses. This means that 62.5% of all those institutions that offer undergraduate

face-to-face courses also offer the same level course online; in other words, online

has a 62.5% penetration rate for undergraduate level courses. Far fewer institutions

provide graduate-level courses (only 26%), but the percentage of these that also have

an online offering is actually slightly higher (65%) than the penetration rate for

undergraduate courses. This analysis does not address the number of courses that

institutions offer in face-to-face and online modes, only if they offer or do not offer

them (Sloan, 2006).

Social Presence

The issue of “social presence” in online education has become an important

issue. Brownrigg (2002) identifies five characteristics of social presence. These

defining characteristics are interactivity, mediated communication, immediacy,

reciprocal awareness, and connectedness.

The genealogy of the phrase “social presence” can be traced back to

Mehrabian’s (1969) concept of “immediacy,” which he defined as "those

communication behaviors that enhance closeness to and nonverbal interaction with

45



another." His research suggested that nonverbal cues such as facial expressions, body

movements, and eye contact, increase the sensory stimulation of interlocutors. This,

in turn, would lead to more intense, more affective, more immediate interactions.

Mehrabian’s (1969) work was followed up by communication theorists who

studied a variety of media including facsimile machines, voice mail, and audio

teleconferencing in organizational settings. Short, Williams, and Christie (1976)

argued that the inability of these media to transmit nonverbal cues would have a

negative effect on interpersonal communication.

It was Short et al. who introduced and defined the term social presence as “the

salience of the other in a mediated communication and the consequent salience of

their interpersonal interactions” (Short, Williams, & Christie, 1976). This is

interpreted as the degree to which a person is perceived as “real” in mediated

communication. Originally construed as an inherent feature of differing media, social

presence may also be explored by examining a variety of issues which may contribute

to the social climate of the classroom (Gunawardena, 1995). Consequently, it has

been argued that social presence is a factor of both the medium and the

communicators’ perceptions of presence in a sequence of interactions (Gunawardena

& Zittle, 1997). Short, et al. (1976), the initial investigators of social presence,

hypothesized that users of communication media are in some sense aware of the

degree of social presence of each medium and tend to avoid using particular

interactions in particular media. Specifically, users avoid interactions requiring a

higher sense of social presence in media which lack such capacity. Social presence,

they contend, varies among different media, it affects the nature of the interaction and

46



it interacts with the purpose of the interaction to influence the medium chosen by the

individual who wishes to communicate (Short et al., 1976).

Gunawardena and Zittle (1997), researchers in the area of social presence and

computer-mediated conferencing, argued that in reviewing social presence research, it

is important to examine whether the actual characteristics of the media are the causal

determinants of communication differences or whether users’ perceptions of media

alter their behavior. They found that social presence could be cultured among

teleconference participants, a position different from the view that social presence is

largely an attribute of the communication medium. Their research demonstrated that

social presence is both a factor of the medium and of the communicators and their

presence in a sequence of interactions (Gunawardena & Zittle, 1997).

Related to the research on social presence is the research conducted on teacher

immediacy behaviors. The concept of teacher immediacy, originated by Wiener and

Mehrabian’s work, is a measure of the psychological distance that a communicator

puts between him/herself and the object of their communication (Wiener &

Mehrabian,1968). The majority of research in instructional communication related to

teacher immediacy behaviors has focused on teachers’ use of verbal and nonverbal

immediacy and the impact of those behaviors on students in traditional, face-to-face

communication. For example, highly immediate behaviors have been associated with

attitudinal changes, such as increase in student motivation (Christophel & Gorham,

1995; Christophel,1990) and student satisfaction (Moore, Masterson, Christophel, &

Shea, 1996).

47



Teacher immediacy behaviors seem to take into account the same phenomena

as social presence without the intermediating variable of media. It may be that

instructors and students involved in asynchronous communication develop a set of

immediacy behaviors that enhance social presence in online courses as Gunawardena

and Zittle suggest (Gunawardena & Zittle, 1997).

Andersen (1979) looked at the role of immediacy in post-secondary education

and proposed the following definition of teacher immediacy: “Teacher immediacy is

conceptualized as those nonverbal behaviors that reduce physical and/or

psychological distance between teachers and students.” She found that engaging in

eye contact with students, adopting a relaxed body posture, using gestures, and

smiling, improved students attitudes toward in the course, the subject matter of the

course, and the course instructor.

Gorham (1988) expanded the definition of teacher immediacy behaviors to

include verbal behaviors such as talking about experiences that have occurred outside

class, using humor, addressing students by name, and praising students' work or

comments. Her results suggest that these types of behaviors also contributed

significantly to students’ affective learning.

Sanders and Wiseman (1990) extended this relationship to include behavioral

and cognitive learning. The authors operationally defined cognitive learning as how

much students thought they had learned in a course. The authors defined behavioral

learning as the likelihood that students would actually attempt to use the behaviors,

practices, or theories studied in the course. Positive correlations between both

48



nonverbal and verbal teacher immediacy behaviors and student affective, behavioral,

and cognitive learning were significant.

The designation of this line of research as teacher immediacy implies an

instructor-centered perspective of the teaching-learning relationship in which the

teacher plays a central and authoritative role in the classroom. According to this

perspective, the creation of a warm, open, and trusting environment is regarded

primarily as the responsibility of the teacher. In this setting, teachers and learners

participate in a learning transaction that is more readily identified with constructivist

rather than instructivist orientations. Furthermore, it should be noted that teacher

immediacy research has concentrated on the investigation of nonverbal and verbal

behaviors in the face-to-face classroom.

The literature finds that humor is a contributive factor to immediacy and

subsequently to learning (Christenson & Menzel, 1998; Christophel, 1990; Gorham,

1988; Gorham & Zakahi, 1990; Sanders & Wiseman, 1990). Christophel and Gorham

(1995) liken humor to an invitation to start a conversation; it aims at decreasing social

distance, and it conveys goodwill. Research by Eggins and Slade (1997) reinforces

the importance of humor as an indicator of social presence. They found humor to be a

pervasive characteristic of casual conversation, in contrast to its infrequent

occurrence in formal, pragmatic interactions. They also postulate a connection

between humor and critical discourse: The construction of group cohesion frequently

involves using conversational strategies such as humorous banter, teasing, and joking.

These strategies allow differences between group members not to be viewed as

serious challenges to the consensus and similarity of the group.

49



The psychological explanation of social attraction and bonding between

individuals includes self-disclosure. Cutler (1995) explains that the more one

discloses personal information, the more others will reciprocate, and the more

individuals know about each other the more likely they are to establish trust, seek

support, and thus find satisfaction. Shamp (1999) applied these notions to computer

mediated communication (CMC) and built on Turkle’s (1997) observation that people

have a tendency to attribute human characteristics to computers (anthropomorphism).

Shamp suggested that people have an inverse tendency to attribute characteristics of

computers to humans (mechanomorphism). Shamp (1999) discusses the negative

implications of this tendency that relate directly to the facilitation of a community of

inquiry. He notes that although CMC augments the number of people with whom an

individual can interact, it does not necessarily augment the degree of exposure to the

multifaceted nature of adult participants. For Shamp (1999), the lack of perceived

diversity in communication partners that CMC fosters has the potential to turn CMC

into a closed system which allows little new and different information about the

world to enter. The negative implications for the construction of knowledge are

apparent. In regard to social presence, Shamp (1999) notes mechanomorphism could

lead to computer communication that is not fulfilling or successful. He recommends

the exchange of personal information to reduce feelings of social isolation and thus

contribute to the formation of individualized impressions of interlocutors.

Teacher immediacy literature has provided an empirical justification for

extending Shamp’s conclusions to educational applications of computer conferencing.

50



Christenson and Menzel (1998), Gorham (1988), Gorham and Christophel (1990),

Gorham and Zakahi (1990), and Sanders and Wiseman (1990) found positive

correlations between use of personal examples, personal anecdotes, and self-

disclosure, affective, cognitive and behavioral measures of learning.

The concept of interactivity is also related to social presence. Interaction can

happen between instructors and students, among students, or basically between two

individuals. Social presence is not present if interaction does not occur (Short,

Williams, & Christie, 1976).

Finally the concept of connectedness is another facet of to social presence.

This concept was introduced by Rouke et al., (1999) as a “community of inquiry” in

which there is a sense of affiliation among the group members and a sense of

solidarity of the group. Rovai (2001) refers the concept of connectedness to a “sense

of involvement and engagement.” DeGreef and Ijsselsteijin (2001) define

connectedness as “sense of being together” and finally Bibeau (2001) refers the

concept to getting connected and collaborated.

51



CHAPTER III

METHODOLOGY

Introduction

Online education presents unusual challenges and opportunities for educators

and students alike. In the context of educational leadership, it is important to

determine to what extent online education motivates students to learn and provides an

overall educational experience similar to that of traditional face-to-face education.

For example, it is important to establish to what degree students find the technology

threatening, experience feelings of isolation, are less likely to communicate with the

instructor, and tend to learn less than they would in a traditional setting. All of this

relates to the issue of social presence. The study also addresses measures that may be

taken to mitigate the negative aspects of online education.

This chapter describes the various statistical techniques that were used in an

attempt to identify those attributes conducive to a high-quality learning experience, as

well as those attributes that influence student enrollment in online courses.

Research Questions

1. Does the online learning experience contribute to feelings of isolation

among students?






52



Null Hypotheses










presence.


experience.




threatening.



Research Methodology

The investigator used various data-analytic methods, including descriptive

statistics, multiple regression analysis, ANOVA, and logit analysis of binary

dependent variable models.

53



This section has a two-fold objective: (1) it explains the equivalence of the

traditional analysis of variance (ANOVA) model and the dummy or qualitative

independent variable regression model; and (2) it presents a brief description of the

logit regression model.

Models with a single qualitative variable have been traditionally formulated as

ANOVA models. Typically, we have observations on a variable Y that may be

grouped into two or more groups. The hypothesis tested with the ANOVA model is

that there is no significant difference between the groups, or equivalently that the two

groups are drawn from the same population. However, this test may also be carried-

out within a regression model using binary variables, also known as dummy variables

─ the method of choice in this dissertation. Such variables take only a value of zero

or one (0, 1). Kmenta (1971) shows that the two approaches are equivalent and lead

to the same test results. Because the observed variables are binary, the mean of each

group is also the proportion of respondents. We wish to test if the respective group

means differ significantly.

The following example demonstrates the equivalence of the traditional

ANOVA t-test for the significance of the difference between means, and the

regression approach. Suppose that we have six observations on variable Y as shown

in Table 1.

54



Table 1. Hypothetical Data

Y X __________________________________________

20 1

35 145 1

30 0

20 010 0

__________________________________________

Notice that the mean of the first three observations, 33.33, differs from that of

the last three observations, 20. The investigator wants to determine if the difference in

means is significant. The null is that all of the observations are drawn from the same

population, i.e. that the difference in means is zero. The alternative is that the first

three and the last three observations are samples from different populations, i.e., that

the difference in means is significantly different from zero. Using Excel, we treat the

first three observations as one variable and the last three as another variable, and do a

t-test for two samples assuming unequal variances. The results in Table 2 indicate that

the test statistic is 1.4368, and the P-value for a two-tailed test is 0.2241. The P-value

is the probability of obtaining the observed difference in means under the null. In

order to reject the null we require a low P-value, say, 0.05, or even lower 0.01.

However, in this case it shows that the probability that the two samples are drawn

from the same population is 0.2241, thus we cannot reject the null hypothesis with a

high level of confidence.

55



Table 2. ANOVA Test Results Obtained with Excel

t-Test: Two-Sample Assuming UnequalVariances

Y X Mean 33.33333 20

Variance 158.3333 100

Observations 3 3

Hypothesized Mean Difference 0

df 4

t Stat 1.436842

P(T<=t) one-tail 0.112061

t Critical one-tail 2.131847

P(T<=t) two-tail 0.224122

t Critical two-tail 2.776445

Using a regression model, instead of treating the first three and last three observations

of Y in Table 1 as two different variables, all six observations are treated as one

variable. However, we introduce an independent binary variable X such that X=1 for

the first three observations and is zero otherwise. See Table 1. We then estimate the

following regression model,

0 1i i iY X β β ε = + + (1)

In this case, the binary variable X, also called a dummy variable, acts as a switch, i.e.

when X=0, the mean of Y is β0, the intercept, also the mean of the last three

observations in Table 1, and when X=1, the mean of Y is β0 + β1 (the mean of the first

three observations in Table 1). Table 3 below shows the estimation results obtained

with Excel.

56



It is noted that the difference in means is 13.3333, the t-statistic of this

coefficient is 1.4368, and its P-value is 0.2241. These are exactly the same values

obtained earlier with the traditional ANOVA model and shown in Table 2. This

concludes the demonstration of the equivalence of these two methods. It is important

to note that the simple regression method in equation (1) may easily be generalized to

allow the inclusion of additional independent variables in a multiple regression model

that permits the simultaneous testing of several null hypotheses.

57



The Logit Model

The logit model has found wide applications in research and in business in

situations when the dependent variable is binary. It derives its name from the logistic

distribution. Consider first a model designed to account for individual choice, e.g.,

whether students enroll in online courses or not. The dependent variable is qualitative

taking only two possible values one or zero depending on the given choice. Although

such a model may be estimated by ordinary least squares (OLS), resulting in the

linear probability model (LPM), there are serious problems associated with such a

model. First, the disturbances are not normally distributed, violating one of the

assumptions needed for statistical inference. Second, the disturbances do not exhibit

constant variance, i.e., are heteroscedastic, also violating another assumption needed

for valid statistical inference. Finally, the probabilities estimated under the LPM are

not constrained to fall between zero and one. Probability values greater than one or

less than zero are nonsense. Alternative estimation methods, such as probit, logit,

tobit, and discriminant analysis have been developed that eschew the pitfalls of the

LPM. See Gujarati (Basic Econometrics, 4th edition, McGraw- Hill, 2003).

The logit model transforms the dependent variable to an index constrained to

fall within the bounds of the cumulative logistic distribution. Estimation of the logit

model results in coefficients for each independent variable; the statistical significance

of each coefficient is tested using a t-statistic. The independent variables may be a

mixed set consisting of both qualitative and quantitative variables. Moreover, the

significance of a group of coefficients may be tested via a likelihood (LR) ratio

statistic analogous to the F test in the linear regression model (Gujarati, 2003).

58



In the context of this dissertation, Yi may alternate between 0 and 1 depending on

whether students are satisfied with the learning experience or not. The explanatory

variables may include, among others, whether they feel isolated or not, whether they

missed not seeing and hearing the instructor, and whether they felt part of a group or

not.

Suppose that we wish to quantify the effect of feelings of isolation on perceived

learning. We could specify a model,

Yi = β0 + β1 Xi + ei , (1)

where, Xi =1 is the student felt isolated, and X i =0 if the student did not feel isolated,

and Yi =1 or Yi =0 depending on whether perceived learning increased or decreased.

Finally, β0 and β1 are parameters to be estimated. Since all observations on the

dependent variable equal 0 or 1, we are interested in estimating the probability of an

outcome conditional upon an observed value of the independent variable, X i.

One possible interpretation of equation (1) is that the probability of success is

a linear function of Xi. However, if we try to estimate equation (1) by ordinary least

squares, the error term will not be normally distributed, its variance will not be

constant, the regression coefficient will be biased, and predicted values of Yi may be

negative or greater than one. In order to sidestep this minefield of problems,

statisticians developed the logit model, which is based on the logistic function. One

desirable property of the logistic function is that it is bounded by 0 and 1. Another

interesting property is that although it is non-linear, it can be easily transformed into a

linear function by taking logs. Figure 1 shows the logistic response function.

59



Let P1 denote the probability that Y = 1, and P0 denote the probability that Y = 0,

then,

)(

)(

110

10

1X

X

e

e P

β β

β β

+

+

+

= (2),

and,

)(0 101

1 X

e P

β β ++

= (3).

Suppose we write,

P' = P1/(1 – P0) (4).

Equation (4) is the logistic or logit transformation. Inserting (2) and (3) into (4)

yields,

60



)(' 10 X e P

β β +

= (5).

And now, taking natural logs of both sides of (5) yields equation (6) which is

estimated by maximum likelihood to obtain values of the parameters β0 and β1. The

parameter of interest is β1which expresses the effect of a one unit change in Xi (in the

present example, the proportion of students reporting feelings of isolation) on the

probability that perceive. D learning increased.

=

'

lni

P β0 + β1Xi (6).

Research Design

Data analysis was carried out by means of descriptive statistics, analysis of

variance, multiple regression analysis, and logit analysis.

Subject of Study

The study was conducted on students enrolled in online courses in the

department of Business and Technology at Lone Star College-Tomball in Tomball,

Texas. The results of the study may be generalized to the population of students at

Lone Star College-Tomball.

Instrumentation

After careful analysis of several developed instruments, questions that have

been used in other published studies were selected for the instrument thus minimizing

the need for validation. The modified instrument consists of 48 questions. The first 42

questions are multiple-choice, and the last six requires written responses. The

modified instrument focuses on students’ demographic data, students’ enrollment,

learning environment, and evaluation criteria in both online and face-to-face courses.

The instrument appears in Appendix 1.

61



Procedures

The experimentally accessible population for the study included students

enrolled in online sections of Economics, Accounting, Management and Computer

Information Studies courses. In the spring semester of 2008, a letter was sent to Dr.

Raymond Hawkins, president of the Lone Star College at Tomball requesting

permission to administer the instrument to students. Dr. Hawkins granted permission,

and data were collected during the summer and fall semesters of 2008. Student

participation was voluntary. The instrument was placed with Wonder Survey Inc. and

students logged on to the Wonder Survey web site where they answered the questions

directly. Students provided consent via submission of the electronic survey to Wonder

Survey.

A total of 150 students, 52.14%, of those invited completed the survey.

Subsequently, Wonder Survey tabulated the responses and provided to me the

students’ final responses. It is worth noting that since the students communicated

electronically with Wonder Survey, the investigator was not involved with the data

gathering or tabulation of the responses.

Reliability and Validity

The variables in this study measure perceptions, attitudes, and educational

outcomes. The validity of an empirical model is judged by measures of goodness-of-

fit, such the t-statistics of the regression coefficients. These are universally used as

measures of model validity, reliability, and adequacy.

62



Data Collection

To ensure confidentiality, the instrument was placed with Wonder Survey Inc.

and students logged on to the Wonder Survey web site where they answered the

questions directly. Subsequently, Wonder Survey tabulated the responses and

provided to the researcher the final responses of students. It is worth noting that since

the students communicated electronically with Wonder Survey, the researcher was

not involved with the data gathering or tabulation of the responses. The collected data

has been stored in a password-protected file in the researcher’s personal computer

which only the researcher may access. The data will be stored for seven years and

will be deleted afterwards.

63



CHAPTER IV

ANALYSIS OF DATA

The data set permits answering a large number of research questions, and

testing a large number of hypotheses. For convenience’s sake the research questions

and null hypotheses are reproduced below.

Research Questions


among students?






Null Hypotheses








64





presence.


experience.




threatening.



Hypothesis No. 2

Considering the relationship between labor force activity and the decision to

enroll in an online course, Table 4 shows the percent of respondents and the number

of weekly hours of labor force activity. No clear pattern emerges; for example, 18.7

percent worked only 1-10 hours, while 24% worked over 40 hours, and this

difference in proportions is not statistically significant.

Table 4

______________________________________________________ Hours/Week Percent of Respondents t-Stat P-value

______________________________________________________

1-10 18.7 -1.12 0.2611-20 14.0 -2.22 0.03

21-30 10.7 -3.09 0.00

31-40 32.7 1.66 0.1Over 40 24.0

65



The responses to another question shed corroborating evidence on the

relationship between labor force activity and the decision to enroll online;

specifically: “Would you take another online course if offered?” Table 5 shows the

responses.

Table 5 ____________________________________________________________

Hours/Week Percent of respondents willing t-Stat P-value

to take another online course

____________________________________________________________ 1-10 89 -0.75 .46

11-20 95 0.11 .92

21-30 94 -0.08 .93

31-40 90 -0.77 .44Over 40 94

The test statistics show that the differences in means between each group and the

reference group, those working over 40 hours per week, are not statistically

significant.

Hypothesis No. 3

Considering the relationship between commuting time to school and the

decision to enroll in online courses, table 6 summarizes the responses. Lone Star

College draws its students from a narrow geographical area, as commuting time for

44% of respondents is 15 minutes or less, and for an additional 30.7% it is within 16-

30 minutes.

66



Table 6

______________________________________________________

Commuting Time Percent of t-Stat P-value(minutes) Respondents

______________________________________________________

0-15 44 9.67 0.0016-30 30.7 6.10 0.00

31-45 17.3 4.35 0.00

46-60 5.3 1.18 0.24Over 60 2.7

“Would you take another online course if offered?” Table 7 shows that a

uniformly high percentage of respondents is willing to take another online course

regardless of commuting time.

Table 7 ____________________________________________________________

Commuting Time Percent of respondents willing t-Stat P-value

(minutes) to take another online course ____________________________________________________________

0-15 95 -0.33 .74

16-30 96 -0.32 .74

31-45 73 -1.91 .0646-60 100 0.00 1

Over 60 100

The results in Table 5 and 7 indicate that, for the data sample under study,

neither labor force activity nor commuting time, are important determinants of the

decision to enroll in online courses at Lone Star College. What then matters?

Question # 34 in the instrument reads: “I took the online course because it

allowed me more flexibility in managing my time and schedule.” Table 8 shows that

94% of the students strongly agreed, or agreed with the statement. Therefore, these

students flock to online classes mainly because of flexibility.

Table 8. Took the online course primarily because it allowed me more

flexibility in managing my time and schedule

67



______________________________________________________________

_

Strongly agree 64%Agree 30%

Strongly disagree 0.7%

Disagree 5%

Given the spectacular growth of online education, one important issue in

educational leadership concerns the educational outcome. This is a complex and

multi-faceted issue and one aspect of which involves social presence. In question #

11 in the instrument, students were asked to rate their overall educational experience

in taking an online course; five choices were available: excellent (EA1), very good

(EA2), good (EA3), satisfactory (EA4), and poor (EA5). Analysis of the responses to

this question sheds light on the factors that contribute to student satisfaction with

online education as well as those that detract from it.

Hypothesis No. 4

This hypothesis posits that: “There is no statistically significant relationship

between student satisfaction with the educational experience and the instructor’s

social presence.” One way to shed light on this issue is via regression analysis within

the framework of the logit model that was introduced earlier in the methodology

section. The logit models below were estimated using the Oxmetrics software

package because the data set is too large to be read with the student version of SPSS.

Students rated their overall educational experience in taking an online course

as follows: Excellent (17%), Very Good (19%), Good (24%), Satisfactory (31%), and

Poor (8%). Altogether, 70% of respondents fall in the first three groups, it will be

interesting to see how these three groups differ from the last two. In the following

model the dependent variable (EA) equals one for the first three groups, and is zero

68



otherwise. In question # 33, the independent variable (miss) is one if the student

missed not seeing and hearing the instructor, and is zero otherwise.

Modeling Satisfaction with the Educational Experience by Logit

The estimation sample is 1 - 150 ____________________________________________________________________

Coefficient Std.Error t-value t-prob

Constant 1.35239 0.2897 4.67 0.000miss -1.64007 0.3701 -4.43 0.000

log-likelihood -89.6610156 no. of states 2

no. of observations 150 no. of parameters 2 baseline log-lik -100.5324 Test: Chi^2( 1) 21.743 [0.0000]**

____________________________________________________________________

The coefficient of the independent variable is -1.64 and is significantly

different from zero. The interpretation of this coefficient is straightforward, it

indicates for each 1% increase in the proportion of students that missed not seeing

and hearing the instructor, the probability that a student would rate the educational

experience as excellent, very good, or good decreases by 1.64% as compared to the

alternative. The null hypothesis of no relationship is rejected, since the t-stat of the

coefficient is -4.43 and its probability value (t-Prob) is 0.000. In other words, this

indicates that if the null hypothesis were true one would obtain results like those

shown above in zero of 1000 cases. Thus we can decisively reject the null. This has

established that there is a clear, statistically significant, inverse relationship between

social presence and satisfaction with the educational experience. Finally, the reported

Chi-squared statistic is a measure of overall goodness-of-fit.

Another way to shed additional light on the relationship between satisfaction

with the educational experience and social presence is via question # 29 in the

instrument, “I learned a great deal about the instructor in the online course.” In the

69



following logit model, the independent variable (ins) equals one if the students

strongly agreed, or agreed with the statement, and equals zero otherwise. The

regression coefficient shown below is positive (0.987) indicating that the more

students learn about the instructor, the more favorable the educational experience

becomes. Here the t-statistic, 2.82, is also significantly different from zero, with a

probability value of 0.005.

Modeling Satisfaction with the Educational Experience by LogitThe estimation sample is 1 – 150

____________________________________________________________________

Coefficient Std.Error t-value t-prob

Constant 2.37308e-016 0.2236 0.00 1.000ins 0.987387 0.3496 2.82 0.005


no. of observations 150 no. of parameters 2

baseline log-lik -100.5324 Test: Chi^2( 1) 8.3068 [0.0039]**

Social presence has many dimensions and it extends to the presence of other

students as well. Question #21 in the instrument reads: “Even though we were not

physically in a traditional classroom, I still felt like I was part of a group in the online

course.” In the following model the independent variable (group) equals one if the

students strongly agreed, or agreed with the statement, and is zero otherwise. The

coefficient of the independent variable is 1.32 and is significantly different from zero.

This indicates for each 1% increase in the proportion of students that feel part of a

group, the probability that a student would rate the educational experience as

excellent, very good, or good increases by 1.32% Thus there is statistically significant

direct relationship between belonging to a group and satisfaction with the educational

experience.


70



The estimation sample is 1 - 150

____________________________________________________________________

Coefficient Std.Error t-value t-probConstant -0.374693 0.2770 -1.35 0.178

group 1.31296 0.3581 3.67 0.000


no. of observations 150 no. of parameters 2

baseline log-lik -100.5324 Test: Chi^2( 1) 13.994 [0.0002]**

Yet another way of looking at social presence is in terms of the extent to

which students experience feelings of isolation. Question # 31 reads: “I felt isolated

and alone while taking an online course.” The independent variable (isol) equals one

if the students strongly agreed, or agreed with the statement, and is zero otherwise.

The coefficient of the independent variable is -1.71 and is significantly different from

zero as evidenced by the t-statistic and associated probability level. This indicates for

each 1% increase in the proportion of students that experienced feelings of isolation,

the probability that a student would rate the educational experience as excellent, very

good, or good decreases by 1.71%. There is a statistically significant inverse

relationship between “isolation” and satisfaction with the educational experience.


The estimation sample is 1 - 150

____________________________________________________________________

Coefficient Std.Error t-value t-probConstant 1.02165 0.2244 4.55 0.000

isol -1.71480 0.3796 -4.52 0.000


no. of observations 150 no. of parameters 2 baseline log-lik -100.5324 Test: Chi^2( 1) 22.063 [0.0000]** ____________________________________________________________________

_

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The following section tests a larger number of null hypotheses concerning the

statistical significance of the difference between group means. The tests go beyond

those reported above.

As shown above, students rated their overall educational experience in taking

an online course as follows: Excellent (17%), Very Good (19%), Good (24%),

Satisfactory (31%), and Poor (8%). Table 9 shows the proportions, or means (%), of

students that fall into each of the above in relation to several statements or questions

from the instrument, so we can test for the statistical significance of the difference

between group means by each of the instrument items in column 1. Appendix D

shows the computer output from which Table 9 was constructed.

The first item in Table 9 reads: “Learned a great deal about the instructor.”

Column 2 shows that 50% of the students who rated their educational experience as

excellent agreed with the statement, and as we move to the right we find the

following numbers: 66, 52, 40, and 0 – those are the percentage of the students in

each group agreeing with the statement. The proportion in the last group – the poor

raters – is precisely zero. We observe a tendency for the proportion of students who

learned a great deal about the instructor, to decrease as their perception of the

educational experience worsens. It is worth inquiring if the difference between each

group mean and those who rated their experience as poor, the benchmark group in

column 6, is statistically significant. Table 10 shows the test statistics (t-stats) and

their significance levels. The t-statistics for question # 1 in columns 2, 3, and 4 are all

significant, indicating that we can reject the null hypothesis that the difference in each

group mean relative to poor raters in Table 9, column 6 is zero. We observe that all

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of the t-stats for question #1 are significant at the 0.01 level or lower. Please note that

Table 10 does not have a column for the “poor” group because that group is the

benchmark. The t-stats in Table 10 allow us to identify the characteristics and

perceptions that distinguish those who rated their online educational experience as

“poor” from the other four groups. This is worth doing. It will help faculty members

understand why some students perform poorly; moreover, it will help professors

design their online courses so as to minimize failure. Before proceeding, a caveat is

necessary, to-wit, the empirical findings are tainted by survivor bias. The reason is

that the drop rate in online classes approaches 50%; this means that those who were

most disappointed with their online educational experience had dropped the course

before the instrument was administered.

The main characteristics that distinguish the poor raters from the first three

groups are as follows. They tend to feel threatened, isolated, and miss not seeing and

hearing the instructor. Also, they do not feel part of group, are less motivated to

participate and to learn, and they report that the online educational experience is very

different from that of the classroom. Finally, only a small percentage of the poor

raters, 17%, report that they enjoyed the online course. See question #17, column 6.

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74



75



76



The next section examines the relationship between perceived learning and

several variables that proxy for social presence. One question allowed students to

indicate how much they learned in the course; specifically, they could select one of

five choices regarding the amount learned: (1) increased (29%); (2) increased

somewhat (10%); (3) no change (38%); (4) decreased somewhat (16%), or (5)

decreased (7%). The above percentages in parentheses show the distribution of

responses; for example, 29% indicated that the amount learned increased, 10%

indicated that it increased somewhat, and 7% indicated that it decreased. Table 11

cross tabulates the proportion (percent) of respondents that selected one of the above

in relation to other questions. The questions in Table 11 are relevant to the issue of

social presence, and allow testing for the relationship between perceived learning and

social presence.

77



78



Question # 7 in Table 9 reads: “Missed not seeing and hearing the instructor.”

A significant proportion of the students in all five groups (columns 2 through 6)

missed not seeing and hearing the instructor, suggesting that this is one aspect of the

personal experience of online education that differs significantly from traditional

education. It is worth noting that as perceived learning decreased, the percentage of

students who missed the instructor’s presence increased. For example, question #7 in

Table 11, column 2 shows that of those indicating that the amount learned increased,

only 37% missed the instructor’s presence, as compared to 63% of those reporting

that the amount learned decreased somewhat (column 5), and 100% of those who

indicated that the amount learned decreased (column 6). Is 37% significantly different

from 100%? Table 12, column 2, shows the test statistic (-3.61) which is significantly

different from zero; its significance level is 0.00. This indicates that we can decisively

reject the null hypothesis that the difference in means is zero. The same holds for the

test-statistics in columns 3, 4, and 5 of Table 12. Each of those tests for the difference

in means of each group versus the reference group – those indicating that the amount

learned decreased.

79



80



Fifty-seven percent of the students in Table 11, column 2 opine that they

learned a great deal about the instructor, as compared to zero percent of those in

column 6. This difference is highly significant. See Table 12. Looking at questions 8,

9, 10, 11, 12, 13 and 14 in Table 11 we find a striking difference in the responses

shown in columns 2 and 6. All in all, the results in Tables 11 and 12 confirm those

reported in Tables 9 and 10, and provide statistically significant evidence that

educational outcomes are adversely affected by a diminution of social presence in

online class.

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CHAPTER V

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

Introduction

This chapter summarizes the major findings of the work and conclusions.

Recommendations are presented for mitigating the special problems that students face

in online courses.

Summary

Problem

Online education is the fastest growing segment of the higher education industry.

Offering an increasingly large number of online courses has become a panacea for

schools wishing to increase enrollments both inside and outside of their traditional

market areas. Nevertheless, there is a growing awareness that online education is not

a perfect substitute for traditional face-to-face education in a classroom setting.

Online students face unusual problems and challenges. Likewise, online teachers face

unusual problems. It is important for educational leaders to recognize the

shortcomings of online education in order that faculty may receive the training

needed to become more effective teachers in the online medium. Special attention

must be paid as well to course design.

Purpose of the Study

The purpose of the study is to examine the importance of social presence in

online courses at a community college. Specifically, the study examined the

relationship of social presence in online courses to students’ perceived learning and

satisfaction.

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Research Questions

The following research questions motivated the study:


among students?






Null Hypotheses










presence.


experience.



83




threatening.



Methodology

The dissertation used various data-analytic methods, including descriptive

statistics, multiple regression analysis, ANOVA, and logit analysis of binary

dependent variable models.

Summary of Findings

Research Question No. 1

The first research question reads: “Does the online learning experience

contribute to feelings of isolation among students?” The companion null hypothesis

reads: “There is no statistical evidence that students feel isolated by the online

experience.” This dissertation provides clear, compelling, and statistically significant

evidence that feelings of isolation are common to online students. Moreover, the

greater the prevalence of these feelings, the less satisfied students typically are, and

the less they perceive to learn.


The second research question reads: What factors influence student

satisfaction in online classes? The data shown in Table 9 indicates that the

instructor’s social presence contributes directly to student satisfaction. The extent to

which students feel that they are part of a group, and that they are able to

communicate effectively with the instructor and with other students also varies

84



directly with student satisfaction with the online instructional experience. On the

other hand, factors that detract from it are: feeling threatened, feeling isolated, and

missing not seeing and hearing the instructor.


Research question #3 reads: “Is the online learning experience detrimental to

students’ motivation?” The related item in the instrument reads: “The online course

stimulated my desire to learn.” Overall, 66% agreed with the statement and 34%

disagreed. The proportion that disagreed is significantly different from zero, t-stat =

8.76, probability value = 0.000.


Research question 4 reads: “What factors influence learning outcomes?” The

evidence indicates that learning outcomes are positively impacted by feeling to be

part of a group, by being able to communicate with other students and with the

instructor, and by learning about the instructor, i.e., the human dimension of the

instructor is important. On the other hand, learning outcomes or perceived learning is

negatively impacted by feelings of isolation and feeling threatened by the course and

technology.


Research Question 5 reads: “Is perceived learning related to social presence?”

The data in Tables 11 and 12 were used to answer this question and related null

hypotheses. For example, question #7 in Table 11, column 2 shows that of those

indicating that the amount learned increased, only 37% missed the instructor’s

presence, as compared to 63% of those reporting that the amount learned decreased

85



somewhat (column 5), and 100% of those who indicated that the amount learned

decreased (column 6). Is 37% significantly different from 100%? Table 12, column 2,

shows the test statistic (-3.61) which significantly different from zero; its significance

level is 0.00. This indicates that we can decisively reject the null hypothesis that the

difference in means is zero. The same holds for the test-statistics in columns 3, 4, and

5 of Table 12. Each of those tests for the difference in means of each group versus the

reference group – those indicating that the amount learned decreased.


Research Question 6 reads: “What are the perceived strengths and

weaknesses of online education?” The evidence indicates that for the students

sampled in this study, flexibility in managing their time and schedule was the greatest

perceived strength. As shown earlier in Table 8, 94% of the respondents indicated

that they took the online course because it allowed more flexibility in time

management. Consistent with this finding, the overwhelming majority of respondents

indicated that they are willing to take another online course.

The results also indicate that the respondents missed not seeing and hearing

the instructor, felt isolated and threatened, were less motivated to learn, were less

satisfied with the educational experience, reported that the amount learned decreased,

their motivation to participate decreased, the amount and quality of interaction with

the instructor and students decreased, and the online course did not provide an

educational experience similar to the classroom.

Conclusions

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The failure to recognize that effective online instruction requires a new

paradigm, has the potential to do great harm. It threatens to devalue education and to

render it subservient to convenience and to the search for higher enrollments. This is

a critical issue in educational leadership. Tax payers who fund the world’s most

expensive educational system should get an adequate return for their tax dollars, yet

the willy-nilly growth of online education will achieve the opposite unless

administrators and teachers recognize the peculiar difficulties and challenges inherent

in the online medium.

This work shows that in a statistically significant proportion of online students

the motivation to learn decreases. Also, the students tend to feel isolated and

threatened, miss not seeing and hearing the instructor, find the online medium to be a

poor way to communicate and interact with others, and in fact, report a decrease in

perceived learning.

Recommendations

In order to understand the recommendations, consider what may be an all too

common situation. Assume that a college professor who has taught a given course in

a face-to-face setting for many years is now given the opportunity to teach it online

for the first time. The school will provide training in Vista or WebCT to facilitate

posting materials online. However, he is not alerted to the special needs, or problems

faced by online students. He posts the same syllabus that he uses in the face-to-face

classes, and grades similarly on the basis of, say, three exams and one paper.

Unaware that many students feel isolated, become less motivated to learn, and in

effect learn less than they would in a traditional setting, he assumes that the online

87



students are equally motivated, and do the reading assignments on a timely basis.

Essentially, following the line of least effort, he continues to use the traditional face-

to-face instructional paradigm that is woefully inadequate for the online medium.

Later, he finds that the online students do not perform as well as he expected.

However, perhaps to avoid flunking a disproportionate number of students, he bases

the curve on the class average, in effect, lowering the standards and passing students

who learned little. On the other hand, the students who got a passing grade with less

effort, but who enjoyed the benefits of lower transportation costs, and time-

management flexibility, will now prefer to enroll in online courses. While the above

picture is unflattering, it underscores what may happen when teachers offer online

classes without the benefit of professional development or training.

First-time online teachers may be annoyed and perhaps overwhelmed by the

cornucopia of problems that they will have to deal with. For example, students fail to

follow simple instructions such as using the correct password to open posted exams,

or even opening exams during the prescribed window of time. The instructor’s mail

box will have several angry emails from these students. If the instructor provides her

home phone number to the students, as this author does, some students will call at

unreasonable hours. All of these problems will contribute to instructor burnt-out

syndrome.

The researcher’s specific recommendations are as follows:

1. Just like students receive orientation to familiarize them with the

technology of online education (Vista, etc.), teachers must receive

training to make them aware of the problems peculiar to online

88



students. There is a pressing need for instructor training or

professional development to refine and fine-tune instructional

practices appropriate to online instruction.

2. Student training should go beyond the technical aspects of the

course management system tools (i.e., Vista, WebCT). They should

be dropped from the class roster if they fail to complete the needed

training. They should be made aware, that they will experience

feelings of isolation, and decreased motivation to participate and to

learn. They should be instructed on actions that they may take to

mitigate these negative aspects.

3. The empirical findings show that many students find the online

courses threatening. Many students may not be ready to take online

courses. Thus, it is important to pre-test the students to determine if

they are ready to tackle online courses.

4. In order to mitigate isolation, instructors should assign group

projects, encouraging students to interact with others. To lessen the

free-rider problem, i.e., the tendency of some members of a group to

do nothing, the group members should evaluate each other.

5. The instructor should provide interesting discussion questions,

journal articles, cases, and other current reading materials, thus

encouraging students to present their views, and react to the views

of others in the chat room and via conferences, thus fostering a

sense of community. This should be an integral part of course

89



requirements and should carry a weight towards the grade earned on

the course. The instructor should be an active participant in the

discussions online; he must interact with the students in a way that

they feel his social presence and are stimulated by him.

6. Make use of the chat room feature in Vista/WebCT.

7. Use streaming videos so that the students may see the instructor or

at least hear his voice, in the absence of the needed camera. This

way the instructor may videotape lectures and place them in Vista

for the students’ convenience.

8. Just like traditional faculty must have office hours, administrators

should require online faculty to interact with their students over the

chat room (immediate response) a minimum number of times each

month.

9. Online faculty should be required to interact with the students, and

the students among themselves, via the discussion board.

Recommendations for Further Study

Based on the results of this study, the researcher recommends the following for the

further study:

1. A study should be conducted to include a larger sample of students

in the Lone Star College System in order to determine the extent to

which the results generalize to students in other departments besides

Business and Technology, and in other locations. The Lone Star

90



College System includes six campuses with over 51,000 students,

and is the largest higher education institution in Houston and the

third largest in Texas.

2. A study should be conducted to include undergraduate students at the

university level in order to ascertain if the results generalize to a

larger cross section of students including juniors and seniors.

3. A study should be conducted to include graduate students at the

university level in order to ascertain if the results generalize to them

as well.

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117



APPENDICES

118



APPENDIX A

LONE-STAR COLLEGE-TOMBALL STUDENTS’ SURVEY

119



This Survey is part of a graduate student’s doctoral dissertation. Your

responses are strictly confidential. The data gathered will be destroyed 3 years

after the study is completed. Sharing the answers of the survey is prohibited.Please respond to the following questions.

Tell us about yourself 1. What is your age?

18-24years_____ 25-34_____ 35-44_____ 45-54_____ 55 and above _____

2. What is your gender?

Female_____ Male_____

3. How many hours do you work as an employee in a typical week?1-10_____11-20_____21-30_____31-40_____Over 40 hours

4. How long does it typically take for you to commute to college?

0-15_____16-30_____31-45_____46-60minutes_____More than an hour

5. Where do you most frequently use a computer for your online courses?Home_____ Work_____ Other_____ If other, specify_____

Tell us about your online class

6. Did you take an orientation course to help you get familiar with the

procedure and the technology of taking courses online?

Yes_____ No_____ Not sure_____

7. How easy/difficult was it for you to use the technology to participate in an

online course?Easy_____ Somewhat Easy_____ Somewhat Difficult_____ Difficult_____

8. How easy/difficult it was for you to use WebCT/VISTA?Easy_____ Somewhat Easy_____ Somewhat Difficult_____ Difficult_____

9. About how many online courses have you completed before taking this

course?My first course_____ One_____ Two or Three____ Four or Five_____ Six or

More_____

10. Would you take another online course if offered?

No_____ Maybe_____ Definitely_____

11. How would you rate your overall educational experience in taking an

online course?

Poor_____ Satisfactory_____ Good_____ Very Good_____ Excellent_____

120



Tell us about your online course in comparison to your traditional face-to-

face course

12. The amount of interaction with other students

Increased_____ Somewhat Increased_____ No Change_____ Somewhat

Decreased_____ Decreased_____

13. The quality of interaction with other students



14. The amount of interaction with the instructor



15. The quality of interaction with the instructor



16. The amount that you learned

Increased____ Somewhat Increased_____ No Change_____ Somewhat


17. The quality of your learning experience



18. The motivation to participate



19. Your familiarity with computer technology



121



Based on your experience with the online course(s), please tell us about the

level of agreement with each of the following statements.

20. I enjoy the online course(s).

Strongly Agree_____ Agree _____ Strongly Disagree_____ Disagree _____

21. Even though we were not physically in a traditional classroom, I still felt

like I was part of a group in the online course.

Strongly Agree_____ Agree _____ Strongly Disagree______ Disagree _____

22. The online course stimulated my desire to learn.

Strongly Agree_____ Agree _____ Strongly Disagree______ Disagree_____

23. An online course provides a personal experience similar to the classroom.

Strongly Agree_____ Agree _____ Strongly Disagree _____Disagree _____

24. An online course allows for social interaction.Strongly Agree_____ Agree_____ Strongly Disagree______ Disagree_____

25. An online course allows me to express my feelings, and to learn the

feelings of others.

Strongly Agree_____ Agree _____ Strongly Disagree__________ Disagree

_____

26. An online course provides a reliable means of communication.Strongly Agree_____ Agree _____Strongly Disagree _____Disagree _____

27. An online course is an efficient means of communicating with others.Strongly Agree_____ Agree _____ Strongly Disagree ______Disagree_____

28. I found the online course threatening to me.Strongly Agree_____ Agree _____ Strongly Disagree ______Disagree_____

29. I learned a great deal about the instructor in the online course.

Strongly Agree_____ Agree _____ Strongly Disagree ______Disagree _____

30. I learned a great deal about the other students in the online course.

Strongly Agree_____ Agree _____Strongly Disagree ______Disagree _____

31. I felt isolated and alone while taking an online course.


32. The online course is more time consuming than I expect.


122



33. I missed not seeing and hearing the instructor(s) and other students as I

would have in a face to face classroom.


34. I took the online course primarily because it allowed me more flexibility

in managing my time and schedule.Strongly Agree_____ Agree _____Strongly Disagree ______Disagree _____

35. I took the online course primarily because I had no choice.Strongly Agree_____ Agree _____Strongly Disagree ______Disagree _____

36. I felt like I had control over what and how I learned when taking the

online course.Strongly Agree_____ Agree _____Strongly Disagree ______Disagree _____

Tell us about the following tools that are available to you for accessing

information and for communicating with colleagues and the instructor.

37. Course Calendar: Not used_____ Important Tool_____ Somewhat Important Tool_____ Very

Important Tool_____

38. E-Mail: Not used_____ Important Tool_____ Somewhat Important Tool_____ Very

Important Tool_____

39. Chat-Room:

Not used_____ Important Tool_____ Somewhat Important Tool_____ Very

Important Tool_____

40. Discussion Board:

Not used_____ Important Tool_____ Somewhat Important Tool_____ Very Important Tool_____

41. Online Library Resources:

Not used_____ Important Tool_____ Somewhat Important Tool_____ Very

Important Tool_____

42. Would you rate your experience to date with the online course as?

Successful_____ Not Successful_____

If Successful, what aspect of the course most contributed to its success:

____________________________________________________

____________________________________________________

____________________________________________________

123



____________________________________________________

____________________________________________________

____________________________________________________

If not successful, what aspect of the online course was most problematic:

____________________________________________________ ____________________________________________________

____________________________________________________

____________________________________________________ ____________________________________________________

43. Should Tomball College offer more online courses?

Yes_____ No_____

Please explain:

____________________________________________________

____________________________________________________ ____________________________________________________

____________________________________________________ ____________________________________________________

44. Please describe your opinion about the overall survey.

____________________________________________________

____________________________________________________ ____________________________________________________

____________________________________________________

45. In your opinion, what questions should be eliminated? Please list those

questions by the number of the questions.

____________________________________________________ ____________________________________________________

____________________________________________________

____________________________________________________ 46. In your opinion, what questions should be added to this survey. Please list

those questions.

____________________________________________________

____________________________________________________ ____________________________________________________

____________________________________________________

124



APPENDIX B

CONSENT TO PARTICIPATE IN RESEARCH

125



Proposal Title: Social Presence in Online Course: An

Examination of Perceived Learning and Satisfaction

CONSENT TO PARTICIPATE IN RESEARCH

You are invited to participate in a research study conducted by Nasrin Nazemzadeh,

who is a doctoral student from the Department of Educational Leadership and

Counseling at Prairie View A&M University. Ms. Nazemzadeh is conducting thisstudy for her doctoral dissertation. Her faculty advisor is Dr. William Allen Kritsonis.

You must be 18 and older to participate in this survey. Your participation in this studyis entirely voluntary. You should read the information below and ask questions about

anything you do not understand, before deciding whether or not to participate.

PURPOSE OF THE STUDY

The purpose of the study is to examine the role of social presence in online courses at

a community college. Specifically, the study will examine the relationship of social presence in online courses to students’ perceived learning and to their satisfaction

with the instructor.

PROCEDURE

If you volunteer to participate in this study, we will ask you to complete an online

questionnaire consisting of 48 questions. This survey will take approximately 25minutes to finish.

POTENTIAL BENEFIT TO SUBJECTS AND/OR SOCIETY

The study will shed light on the factors that contribute to favorable learning outcomes

in online education.

PAYMENT FOR PARTICIPATION

You will not receive any payment or other compensation for participation in this

study. There is also no cost to you for participation.

ANONYMOUS

Any information that is obtained in connection with this study will be unidentifiable

in your response. Please do not enter your name on the survey. When the study isfinished, the data gathered will be destroyed three years after the study is completed.

Sharing the answers of the survey is prohibited.

126



PARTICIPATION AND WITHDRAWAL

Once you choose to participate in this study, you may leave blank any questions youdo not want to answer and you may withdraw from participation by choosing not to

submit your survey.

IDENTIFICATION OF INVESTIGATOR

If you have any questions or concerns about the survey, please feel free to contact

Ms. Nasrin Nazemzadeh

Principal Investigator

9114 S. Pass Ln.Houston, TX 77604

281-894-0855

[email protected]

RIGHT OF RESEARCH SUBJECT

If you have any questions about your rights as a research subject, you may contact the

Prairie View A&M University Institutional Review Board (IRB) by mail at P.O.B.

519, MS # 1200, Prairie View, TX 77446, by phone at 936.261.1588, or e-mail at

[email protected]. Address your questions to Ms. Marcia Shelton, Director,Research Regulatory Compliance. You will be given the opportunity to discuss any

questions about your rights as a research subject with a member of the IRB.

The link to the survey is:

https://www.wondersurvey.com/open_survey.html?sid=ODQ5

127

mailto:[email protected]








APPENDIX C

LETTER TO THE PRESITENT OF LONE-STAR COLLEGE-TOMBALL

128



From: Nasrin Nazemzadeh, Lone Star College-Tomball

To: Dr. Raymond Hawkins, President Lone Star College-Tomball

Date: April 16, 2008

I am in the process of writing my dissertation to fulfill requirements for the doctorate of Educatio

Educational Leadership and Counseling at Prairie View A&M University in Prairie View, Texasunder the direction of Dr. William Allen Kritsonis, professor, College of Education.

Please accept this as my request for institutional approval from Lone Star College-Tomball to

undertake the study described below. If the study is approved, I will need to submit it to the PrairView A&M University Institutional Review Board.

Description of Study

The purpose of the study is to examine the role of social presence in online courses at a communi

college. Specifically, the study will examine the relationship of social presence in online courses students’ perceived learning and to their satisfaction with the instructor. The study aims at the

identifying the factors that contribute to students satisfaction with online education.

This study will provide administrators and faculty with information to improve the design and

delivery of online education.

Research Questions

The following research questions guide the study.


among students?2. What factors influence student satisfaction in online classes?


4. What factors influence learning outcomes?5. Is perceived learning related to social presence?

6. What are the perceived strengths and weaknesses of online education

Null Hypotheses



H02. There is no statistically significant relationship between labor forceactivity as measured by average weekly hours of work, and the decision




129





presence.H05. There is no statistical evidence that students feel isolated by the online

experience.

H06. There is no statistical evidence that students find the online medium to bea poor way to communicate with the instructor.


threatening.H08. There is no statistically significant relationship between perceived


Participants

The experimentally accessible population for the study will include students enrolled in sections

Economics, Accounting, Management and Computer Related Studies courses. Several faculty

members in the Department of Business and Technology will be asked to administer the instrume

to their online students The instrument will be placed with Wonder Survey Inc. and students will on to the Wonder Survey web site where they will answer the questions directly. Subsequently,

Wonder Survey will tabulate the responses and provide to me the final responses of students. It isworth noting that since the students will communicate electronically with Wonder Survey, the

researcher will not be involved in any way, shape, or form with the data gathering or tabulation o

the responses.

Confidentiality

To ensure confidentiality, data will be stored in a password-protected file in my personal computer.

Thank you for your support and assistance,

Nasrin Nazemzadeh

Contact Information: Nasrin Nazemzadeh

9114 S. Pass Ln.

Houston, TX 77064281-894-0855 Home

281-401-1804 Work

832-816-3456 Cell [email protected]

130



Appendix D

Sample Computer Output for Table 9

*Computer output for Table 9.

131



* Software package WinRats, Estima.

all 200

open data c:\progra~1\estima\current\edgycol.xls

data(for=xls,org=obs)

set ins = ins1+ins2

set tac = ta1+ta2

set noc = noc1+noc2

set joy = joy1 + joy2

set group = gr1 + gr2

set dl = dl1 + dl2

set sc = sc1 + sc2

set soc = soc1 + soc2

set comm = com1 + com2

set threat = th1 + th2

set isol = isol1 + isol2

set time = time1 + time2

set miss = mss1 + mss2

set flex = flex1 + flex2

set ease = e1+e2

set ea = ea1+ea2 +ea3

set aylr = ayl1+ayl2set qis = qis1+qis2

set ii = ii1+ii2

set qii = qii1+qii2

set fam = fct1+fct2

set webct = wc1+wc2

set is = is1 + is2

set mp = mp1 + mp2

set fee = fee1 + fee2

set std = std1 + std2

set qle = qle1+qle2

set eco = eco1 + eco1

set cosa = isol3+isol4

set x = dl3 + dl

sta(smpl=ea1) ins

sta(smpl=ea1) group

sta(smpl=ea1) sc

sta(smpl=ea1) soc

sta(smpl=ea1) comm

sta(smpl=ea1) isol

sta(smpl=ea1) miss

sta(smpl=ea1) qis

sta(smpl=ea1) ii

sta(smpl=ea1) qii

sta(smpl=ea1) is

sta(smpl=ea1) mp

sta(smpl=ea1) dlsta(smpl=ea1) qle

sta(smpl=ea1) fee

sta(smpl=ea1) std

sta(smpl=ea1) joy

sta(smpl=ea1) ea

sta(smpl=ea1) eco

sta(smpl=ea1) threat

sta(smpl=ea1) flex

sta(smpl=ea1) aylr

132



linreg dl

# constant

Linear Regression - Estimation by Least Squares

Dependent Variable DL

Usable Observations 150 Degrees of Freedom 149

Centered R**2 -0.000000 R Bar **2 -0.000000

Uncentered R**2 0.660000 T x R**2 99.000

Mean of Dependent Variable 0.6600000000

Std Error of Dependent Variable 0.4752957398

Standard Error of Estimate 0.4752957398

Sum of Squared Residuals 33.660000000

Log Likelihood -100.76640

Durbin-Watson Statistic 1.901367

Variable Coeff Std Error T-Stat

Signif

********************************************************************

***********

1. Constant 0.6600000000 0.0388077346 17.00692

0.00000000

* The following sample means are the descriptive statistics in Table

9, column 2. The computer output also

* shows the t-statistic and significance level of the null

hypothesis that the mean equals zero.

Statistics on Series INS

Observations 26 Skipped/Missing 124

Sample Mean 0.500000 Variance 0.260000

Standard Error 0.509902 of Sample Mean 0.100000

t-Statistic (Mean=0) 5.000000 Signif Level 0.000037

Skewness 0.000000 Signif Level (Sk=0) 1.000000

Kurtosis (excess) -2.173913 Signif Level (Ku=0) 0.049129

Jarque-Bera 5.119723 Signif Level (JB=0) 0.077315

Statistics on Series GROUP





Skewness -2.038340 Signif Level (Sk=0) 0.000064

Kurtosis (excess) 2.328310 Signif Level (Ku=0) 0.035100


Statistics on Series SCObservations 26 Skipped/Missing 124







133



Statistics on Series SOC








Statistics on Series COMM








Statistics on Series ISOLObservations 26 Skipped/Missing 124







Statistics on Series MISS








Statistics on Series QIS








Statistics on Series II







134




Statistics on Series QII








Statistics on Series IS








Statistics on Series MP








Statistics on Series DL








Statistics on Series QLE




t-Statistic (Mean=0) 5.838742 Signif Level 0.000004Skewness -0.330798 Signif Level (Sk=0) 0.516501



Statistics on Series FEE




135







Statistics on Series STD








Statistics on Series JOY







Statistics on Series TAC








Statistics on Series ECO








Statistics on Series THREAT

Observations 26 Skipped/Missing 124Sample Mean 0.076923 Variance 0.073846






Statistics on Series FLEX

136










Statistics on Series AYLR








sta(smpl=ea2) ins

sta(smpl=ea2) groupsta(smpl=ea2) sc

sta(smpl=ea2) soc

sta(smpl=ea2) comm

sta(smpl=ea2) isol

sta(smpl=ea2) miss

sta(smpl=ea2) qis

sta(smpl=ea2) ii

sta(smpl=ea2) qii

sta(smpl=ea2) is

sta(smpl=ea2) mp

sta(smpl=ea2) dl

sta(smpl=ea2) qle

sta(smpl=ea2) fee

sta(smpl=ea2) std

sta(smpl=ea2) joy

sta(smpl=ea2) tac

sta(smpl=ea2) eco


sta(smpl=ea2) flex

sta(smpl=ea2) aylr


9, column 3









137










Statistics on Series SC























Statistics on Series ISOL
















138








































Skewness -0.219535 Signif Level (Sk=0) 0.647237Kurtosis (excess) -2.102071 Signif Level (Ku=0) 0.041397







139



















Skewness -1.058529 Signif Level (Sk=0) 0.027354Kurtosis (excess) -0.950142 Signif Level (Ku=0) 0.356595




















Sample Mean 0.965517 Variance 0.034483Standard Error 0.185695 of Sample Mean 0.034483







140
































sta(smpl=ea3) ins

sta(smpl=ea3) group

sta(smpl=ea3) sc

sta(smpl=ea3) soc

sta(smpl=ea3) comm

sta(smpl=ea3) isol

sta(smpl=ea3) miss

sta(smpl=ea3) qis

sta(smpl=ea3) ii

sta(smpl=ea3) qiista(smpl=ea3) is

sta(smpl=ea3) mp

sta(smpl=ea3) dl

sta(smpl=ea3) qle

sta(smpl=ea3) fee

sta(smpl=ea3) std

sta(smpl=ea3) joy

sta(smpl=ea3) tac

sta(smpl=ea3) eco

141




sta(smpl=ea3) flex

sta(smpl=ea3) aylr


9, column 4








































142









































Kurtosis (excess) -1.735196 Signif Level (Ku=0) 0.054457Jarque-Bera 6.703578 Signif Level (JB=0) 0.035022







143



















Kurtosis (excess) -2.063503 Signif Level (Ku=0) 0.022192Jarque-Bera 6.714055 Signif Level (JB=0) 0.034839




















Standard Error 0.494413 of Sample Mean 0.082402t-Statistic (Mean=0) 4.719399 Signif Level 0.000037







144



















Standard Error 0.464621 of Sample Mean 0.077437t-Statistic (Mean=0) 1.434860 Signif Level 0.160203




















Statistics on Series AYLRObservations 36 Skipped/Missing 114







145



sta(smpl=ea4) ins

sta(smpl=ea4) group

sta(smpl=ea4) sc

sta(smpl=ea4) soc

sta(smpl=ea4) comm

sta(smpl=ea4) isol

sta(smpl=ea4) miss

sta(smpl=ea4) qis

sta(smpl=ea4) ii

sta(smpl=ea4) qii

sta(smpl=ea4) is

sta(smpl=ea4) mp

sta(smpl=ea4) dl

sta(smpl=ea4) qle

sta(smpl=ea4) fee

sta(smpl=ea4) std

sta(smpl=ea4) joy

sta(smpl=ea4) tac

sta(smpl=ea4) eco


sta(smpl=ea4) flexsta(smpl=ea4) aylr


9, column 5

















Statistics on Series SCObservations 47 Skipped/Missing 103







146



















Statistics on Series ISOLObservations 47 Skipped/Missing 103






























147








































t-Statistic (Mean=0) 6.639528 Signif Level 0.000000Skewness 0.043979 Signif Level (Sk=0) 0.905154







148














































149


















sta(smpl=ea5) ins

sta(smpl=ea5) group

sta(smpl=ea5) scsta(smpl=ea5) soc

sta(smpl=ea5) comm

sta(smpl=ea5) isol

sta(smpl=ea5) miss

sta(smpl=ea5) qis

sta(smpl=ea5) ii

sta(smpl=ea5) qii

sta(smpl=ea5) is

sta(smpl=ea5) mp

sta(smpl=ea5) dl

sta(smpl=ea5) qle

sta(smpl=ea5) fee

sta(smpl=ea5) std

sta(smpl=ea5) joy

sta(smpl=ea5) tac

sta(smpl=ea5) eco


sta(smpl=ea5) flex

sta(smpl=ea5) aylr


9, column 6




t-Statistic (Mean=0) NA Signif Level NA






150
















































151








































Skewness 3.464102 Signif Level (Sk=0) 0.000019Kurtosis (excess) 12.000000 Signif Level (Ku=0) 0.000000







152
















































153
















VITA

154



NASRIN NAZEMZADEH

9114 S. Pass LN.

Houston, Texas 77064

EDUCATIONAL HISTORYSoutheastern Louisiana University, Hammond, Louisiana

M.B.A., 1986

Florida State University, Tallahassee, Florida

M.A. in Higher Education, 1979

University of Isfahan, Isfahan, Iran

B.S. in Biology, 1974

EMPLOYMENT HISTORY

2002-Present Professor of Business and Economics, Lone Star College-

Tomball

1999-2002 Instructor of Economics, Prairie View A&M University

1992-1999 Instructor of Economics, Houston Community College

155

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