Muilenburg, L.Y. and Berge, Z.L. (2001). Barriers to distance
education: A factor-analytic study. The American Journal of Distance
Education. 15(2): 7-22.
Barriers to Distance Education: A Factor-Analytic Study
Lin Muilenburg and Zane L. Berge
This article reports on a large-scale (n = 2,504),
exploratory factor analysis that determined the
underlying constructs that comprise barriers to
distance education. The ten factors found were (1)
administrative structure, (2) organizational change, (3)
technical expertise, (4) social interaction and quality,
(5) faculty compensation and time, (6) threat of
technology, (7) legal issues, (8)
evaluation/effectiveness, (9) access, and (10) student-
While numerous studies have discussed barriers to the successful
implementation of distance education, many are based on the
examination of one instructor’s experience, one distance learning
environment, or one type of distance learning program. The findings
provide useful information, but it is difficult to piece these studies
together to create a holistic picture of the barriers to distance
Some quantitative studies have been conducted (Berge 1998; Cegles
1998; Dickinson et al. 1999; Rockwell et al. 1999; Yap 1996), but
they tap a small or very focused population group. A larger-scale
study was still needed to consider simultaneously the many
dimensions of barriers to distance education as perceived by people
from a wide variety of backgrounds.
The survey study reported in this article sought to represent the
perceptions of people who differed on six demographic variables: (1)
workplace (e.g., community college, government, nonprofit
organization, K–12 education); (2) job function (e.g., support staff,
manager, researcher, student); (3) type of delivery system used (e.g.,
audiotape, computer conferencing, interactive television [ITV]); (4)
expertise regarding distance education; (5) the stage of the
respondent’s organization with regard to capabilities in delivering
distance education (from no distance education activity to distance
education being the way the organization does business); and (6) the
area in which the respondent primarily works (e.g., fine arts,
engineering, education). More than 2,500 survey respondents rated
the severity of sixty-four separate barriers to distance education on a
1–5 Likert scale (from no barrier to very strong barrier, respectively).
conduct an exploratory factor analysis to determine the underlying
constructs that comprise barriers to distance education. We believed
that developing a research-based framework for barriers would be
helpful in several areas. First, it would simplify the further analysis of
the data gathered in this survey by reducing the number of dependent
variables. Second, it would provide a meaningful and useful
framework for discussing the barriers that organizations and
individuals are likely to encounter when implementing a distance
learning program. Finally, in our literature review, we did not find
any frameworks reported to have been developed through a factor-
analytic study. We believed that a perspective derived using a
different statistical treatment would be valuable, regardless of
whether conclusions drawn were similar to or different from those of
One goal of this study, and the subject of this article, was to
We conducted a review of the literature on barriers, issues, and
success factors in distance education. The literature fell into two
broad categories: articles that listed numerous specific barriers to
distance education, and articles that provided a framework or
categorization to facilitate the discussion of barriers. Although it is
important to discover the range and variety of barriers addressed in
the literature, it is difficult to study or draw meaningful conclusions
on so many specific barriers. For this reason, a framework, or a
smaller number of categories for discussion of barriers, is highly
desirable. A literature review revealed several articles that contained
some form of categorization or a formal framework. These articles
are briefly summarized below to provide an overview, and to later
relate the categories identified by a selected number of the authors to
the factors we identified in this study.
enrollment into three categories. Situational barriers result from an
Rezabek (1999) grouped the barriers to distance education
individual’s general situation or environment, and include such issues
as transportation, age, time constraints, and family responsibilities.
Institutional barriers are created by an institution’s programs,
policies, and procedures, and include problems with admissions,
registration, scheduling of courses, financial aid, and support
services. Dispositional barriers result from an individual’s personal
background, attitude, motivation, learning style, and self-confidence.
four barrier categories were used: situational, institutional,
dispositional, and epistemological. The first three categories in this
study are the same as those described by Rezabek. Epistemological
barriers were that the course was difficult; that the course was too
technical, theoretical, or abstract; that students lacked prerequisite
knowledge; and that the content lacked personal interest or relevance.
In Garland’s (1993) study of barriers to student persistence,
barriers relating to the infusion of technology in the K–12 classroom.
They found that teachers have consistently cited four basic barrier
categories: time, access, resources, and expertise. The authors then
added a fifth category: support.
Leggett and Persichitte (1998) investigated the history of
Merrill et al. (1992) used three basic categories to discuss barriers:
ethical issues, legal issues, and cultural issues.
issues affecting distance education research and practice in higher
education. The three-round Delphi study, which included
administrators, researchers, and support-services personnel, produced
fifteen issue categories:
Cegles (1998) conducted an international study of emerging
• Learner-support and student-related issues;
• technological advancements, convergence, and appropriate
• staff-development and professional-training issues;
• curricular/instructional design and delivery of distance
• quality-assurance issues;
• alternative-teaching and learning strategies, models, and/or
• collaborative partnerships, linkages with business, industry,
and education (interinstitutional and geographical);
• access to distributed learning and teaching (student,
institutional, and geographical);
• evaluation methods and outcomes effectiveness;
• leadership and management issues in distance education
• administrative, policy-related, and legislative issues (e.g.,
copyright, intellectual-property rights);
• costs, fees, funding resources, and capital-investment issues;
• convergence of traditional and distance education;
• mass production, commodification, and globalization of
• continuous, formalized further-education issues (lifelong
barriers in her report on the Pacific Northwest Star Schools
partnership for distance education. The Satellite Telecommunications
Education Programming Network in Spokane, Washington joined
with five northwestern states to offer telecast courses for over 6,000
students in more than 500 middle schools and high schools. In a
survey of 440 superintendents and building administrators, five
implementation-barrier categories were identified: lack of equipment
and support; scheduling difficulties; program costs; instructional
concerns (e.g., interactivity, motivation); and training and technical
assistance. These frameworks are described by Dooley, Metcalf, and
Martinez (1999); Galusha (1997); Hopkins (1996); Lehman (1998);
Merrill et al. (1992); and Sherry (1996).
Yap (1996) identified program benefits and implementation
The Framework for the Current Study
Fetzner (1998), which addresses the issues of planning and policy
development for distance education programs and provides a
framework of seven policy-development areas: academic, fiscal,
geographic, governance, labor-management, legal, and student-
support services. These seven areas were listed as examples, but were
not claimed to be an exhaustive list of policy areas. Berge selected
this framework as a base for further research because it was believed
to be more comprehensive in scope than the other frameworks
reviewed. Berge (1998) surveyed instructors who teach online to
investigate the barriers they encountered. He then attempted to match
the barriers mentioned by the participants to the seven key issues
identified by Gellman-Danley and Fetzner (1998). Only 28% of the
participants’ responses seemed to fit within their categories, so Berge
added two new categories to the framework: technical and cultural.
We also reviewed the framework of Gellman-Danley and
literature review regarding barriers to online teaching in elementary,
secondary, and teacher education, using the revised nine-policy
Later, Berge and Mrozowski (1999) conducted an extensive
framework for the analysis. The primary areas of concern cited in the
literature were academic, cultural, and technical issues. Secondary
areas of concern were labor-management and fiscal issues. There was
little or no mention of geographic, governance, student-support, or
legal issues. Next, Berge and Mrozowski performed a content
analysis of seventy-two chapters from a four-book series entitled
Wired Together: Computer-Mediated Communication in K–12 (Berge
and Collins 1998). This content analysis revealed a set of concerns
similar to those categories derived from the literature, in that the
Berge (1998) framework was able to be used to categorize the
barriers found in the content analysis.
analysis described above and through the literature review was
carefully considered. Barriers that were judged to be the same or very
similar were combined. The final result was a list of sixty-four
specific barriers that were written as individual survey items. (See the
survey online at <http://cgi.umbc.edu/cgi-
bin/dharley/misc/barrier_survey.pl>.) The following section describes
more fully the survey procedures.
Each of the specific barriers identified through the content
Sample Research and Methodology
distance education—from a review of literature, from previous survey
work (Berge 1998), and from content analyses of selected case
studies (Berge and Mrozowski 1999). Berge then conducted two
rounds of beta testing using paper-and-pencil versions of the
instrument that were administered to selected members of the target
population (n > 50). Revisions were made before the final version of
the survey was released on the World Wide Web. The survey was
programmed to be accessible using standard Web browsers. It was
designed so that, as each respondent completed and submitted the
survey, the response was captured in an output file that could
relatively easily be converted to SPSS (Statistical Package for the
Social Sciences). Respondents were asked to rate each of the sixty-
four barriers on a 1–5 scale (no barrier to very strong barrier,
Berge developed the survey items—sixty-four barriers to
to personal acquaintances; to thousands of individuals collected from
participation lists and membership lists gathered over the years from
educational technology, distance education, and training conferences,
workshops, seminars, and professional organizations; and to a wide
variety of electronic mailing lists in which the topic of discussion was
believed to be related to education, distance education, and
To announce this survey, we sent individual e-mail messages
technology-enhanced learning. The announcement included
background information regarding the survey, provided the
perspective taken, and asked for volunteers to complete the online
survey regarding barriers to distance education. Given this selection
process, it is nearly impossible to accurately estimate rate of return.
Those subgroups—such as university students and persons working
in elementary and secondary schools—that were found to be
underrepresented in the early stages of data collection (June to
December 1999) were specifically targeted in the latter stage of data
collection (mid-December 1999 to January 2000). As of February 1,
2000, 2,530 surveys were collected. After data cleaning, 2,504 valid
surveys remained and were analyzed using SPSS.
Data were collected between June 1999 and January 2000.
Of the 2,504 survey respondents, 1,276 worked in higher education,
448 in corporate or business organizations, 375 in community
colleges, 129 in government, 126 in middle or secondary schools, 117
in nonprofit organizations, and 33 in elementary schools. The job
functions of the respondents included 1,150 teachers or trainers; 648
managers, directors, department chairs, or principals; 346 support
staff; 167 higher administrators such as dean, provost, vice president,
or superintendent; 102 researchers; and 91 undergraduate or graduate
students. Respondents worked in a broad range of content areas,
including education (33.0%), business (16.8%), health sciences
(10.2%), humanities (8.6%), engineering (4.8%), behavioral sciences
(4.6%), physical sciences (2.6%), humanities (1.0%), and "other"
The primary distance learning delivery systems being used by
respondents included Internet- or Web-based computer conferencing
(1,462); print-based systems (286); videoconferencing or desktop
videoconferencing (269); CD-ROM or multimedia (177); audiotape
or videotape (123); ITV (118); audioconferencing or audiographics
(35); EPSS (Electronic Performance Support System) (32); and radio
We conducted a common-factor analysis—using the generalized
least-squares extraction method and applying Kaiser’s criterion of
eigenvalues of 1.0 or greater—to determine the underlying structure
of the data. Because we found no previous studies suggesting the
existence of any specific uncorrelated factors, and because
nonorthogonal factors may better represent the reality of barriers to
distance education, Oblimin rotation was used. Therefore, some of
the factors obtained through this analysis are correlated. This could
pose some problems if the factors were used as independent variables
in a multivariate analysis. However, our plans for further analysis of
the data would utilize these factors as dependent variables only.
education resulted in ten factors that accounted for 52% of the overall
variance. Table 1 shows the variables loading on each of the ten
factors. A cutoff for statistical significance of the factor loadings of
0.30 was used, according to guidelines presented by Hair et al. (1995)
for larger-sample sizes (n > 350).1 Seven of the sixty-four variables
loaded significantly on two factors, with the secondary loadings
having a relatively low value ranging from .307 to .380. However, to
obtain a parsimonious solution, only the larger loading for each
variable would be used to determine which set of variables comprises
a particular factor.
The factor analysis of the sixty-four barriers to distance
Table 1. Factor Analysis of Barriers to Distance Education
Full-time equivalent issues
Lack of transferability of
Lack of ongoing credibility
Local, state, or federal
Revenue sharing with
departments or business units
Difficulty competing with
new distance learning (DL)
Lack of money to implement
calendar/schedule hinders DL
Organizational resistance to
Lack of shared vision for DL
Lack of champion for DL in
Lack of strategic planning for
Difficult to convince
stakeholders of DL benefits
Lack of knowledge/support
Slow pace of implementation
Lack of "right" people to
Lack of identified need
Lack of colleague
knowledge/support of DL
Difficulty keeping up with
Lack support staff to help
with course development
Lack of technical support
Lack of technology-enhanced
Lack of personal
Lack of DL training provided
Disrupts traditional social
organization of classroom
Quality of course/program,
students, or learning
Concerns with evaluation,
testing, assessment, outcomes
incentives, workload, tenure,
Increased time commitment
Lack of grants for DL
Perception that computers
may replace teachers
Faculty feel job security is
Fear of technology
Threat to instructors’ sense of
Copyright and fair use issues
Lack of policy concerning
intellectual property rights
Legal issues (computer
crime, hackers, piracy,
Lack of research supporting
Lack effective evaluation for
Lack adequate student access
or equal access concerns
Lack of adequate instructor
access to DL
Lack of advisement for DL
Lack of library access or
Lack student services
(admissions, financial aid,
Inability to monitor identity
of DL students
Difficulty recruiting faculty
The following fourteen variables fell below the 0.30 factor-loading
cutoff and were not included in any factor:
• existing union contracts;
• competition with on-campus courses, or for existing students;
• service area limitations or restrictions;
• lack of parental involvement;
• information overload;
• isolation felt by instructors;
• problems with vast distances or time zones;
• lack of acceptable-use policy;
• lack of professional prestige for distance learning;
• ethical issues;
• accreditation issues;
• language barriers across cultures;
• cultural issues (lack of bias-neutral technology);
• difficulty managing distance learning classrooms.
Identifying the Factors
The goal of this exploratory analysis was to identify constructs that
organize barriers to distance education. Although the Berge and
Mrozowski (1999) framework contained nine categories of barriers
and served as the theoretical foundation for the survey used and
reported here, this factor analysis produced ten factors. Exhibit 1
shows an analysis of the overlaps concerning this framework and the
factors found in the current study.
following factor names or proposed constructs:
Review of the barriers that loaded on each factor yielded the
1. Administrative structure: Managing distance learning
programs through the existing administrative structure can be
problematic. Partnerships among different units within an
organization or among different organizations require
agreements on fiscal issues such as costs, tuition and fees, and
distribution of revenue, as well as scheduling and issuance of
2. Organizational change: Organizations are resistant to change.
Without a shared vision for distance learning, a strategic plan,
and key players within the organization who are
knowledgeable and supportive of distance learning,
implementing a distance learning program is a slow and
3. Technical expertise, support, and infrastructure: It is difficult
to keep pace with technological change. Many instructors lack
the knowledge and skills to design and teach distance learning
courses, yet their organizations lack support staff to assist
with technical problems, to develop distance learning course
materials, or to provide distance learning training. The
technology-enhanced classrooms or laboratories and the
infrastructure required to use them may not be available.
4. Social interaction and program quality: Participants in
distance learning courses can feel isolated due to lack of
person-to-person contact. But some people are uncomfortable
with the use of student-centered and collaborative learning
activities because they change the traditional social structure
of the classroom. There are concerns about the quality
distance learning courses, programs, and student learning.
Testing and assessment of student outcomes is also a concern.
5. Faculty compensation and time: Distance learning courses
require a greater time commitment, so faculty compensation,
incentives, and release time are important issues. Lack of
grants to fund distance learning projects is also a problem.
6. Threat of technology: Some educators fear that an increase in
the use of distance learning technologies may decrease the
need for teachers. Feeling intimidated by technology may also
threaten an instructor’s sense of competence or authority.
Either or both of these psychological factors may lead a
teacher to feel that his/her job security is threatened.
7. Legal issues: The increasing use of (in particular) the Internet
to deliver distance learning raises concerns about copyright,
fair-use policies, piracy, intellectual-property rights, and
problems with hackers and viruses.
8. Evaluation/effectiveness: There is concern over a lack of
research supporting the effectiveness of distance learning as
well as a lack of effective evaluation methods for distance
learning courses and programs. (The issue of accreditation for
distance learning programs also correlated with this factor,
although it did not meet the cutoff of 0.30.)
9. Access: Many students lack access to necessary hardware,
software, or the Internet, or there are concerns over equal
access to courses offered via newer technologies such as
Web-based instruction. Instructors also lack access to the
necessary equipment and courses.
10. Student-support services: Provision of student services such
as advisement, library services, admissions, and financial aid
is a critical facet of any distance learning program. There are
also concerns about how to monitor the identity of distance
learning students (such as determining that the person who
registered for the class is the person taking the online
examination or doing the work).
It can be seen from the literature review that there are many ways to
categorize barriers. In addition, some specific barriers can be placed
in more than one category within a particular theoretical framework
(see Table 1). For example, the barrier intellectual-property rights
may be considered to fall within both the legal and the labor-
management categories of the Berge and Mrozowski (1999)
have no quantitative basis. The number of categories used, and the
degree to which specific barriers are combined or grouped, has been
through qualitative analyses. The ten constructs revealed through this
factor analysis provide a solid starting point for developing a
quantitatively based framework of barriers to distance education.
However, given that the framework derived here is relatively general,
specific sites or populations may wish to conduct their own factor
analyses, possibly using the instrument here or a modification of it.
To date, the frameworks that have been used in the literature
exploratory factor-analytic study were also identified in some of the
theoretical frameworks reviewed in this article. These commonalities
lend strength to the validity of the ten constructs found in this study.
Most of the barrier constructs identified through this
Exhibit 1. Overlap of Factors with Literature Review
• Administrative structure is similar to Cegles’ (1998)
collaborative partnerships, and Berge and Mrozowski’s
• Organizational change is similar to Cegles’ (1998) leadership
and management issues, and Berge and Mrozowski’s (1999)
• Technical expertise, support, and infrastructure is discussed in
Leggett and Persichitte’s (1998) resources, expertise, and
support, Yap’s (1996) lack of equipment and support, and
Berge and Mrozowski’s (1999) support.
• Social interaction and quality is addressed in Cegles’ (1998)
quality assurance issues.
• Legal issues match Berge and Mrozowski’s (1999) legal
issues, Merrill’s et al. (1992) legal issues, and Cegles’(1998)
administrative, policy-related, and legislative issues.
• Evaluation/effectiveness is addressed in Cegles’ (1998)
evaluation methods and outcomes effectiveness.
• Access matches Leggett and Persichitte’s (1998) access, and
Cegles’ (1998) access to distributed learning and teaching.
• Student-support services is discussed in Rezabek’s (1999)
institutional barriers, Cegles’ (1998) learner-support and
student-related issues, and Berge and Mrozowski’s (1999)
technology were items mentioned in the discussion of some of the
frameworks, but were grouped under larger headings. Interestingly,
these two items were important enough to stand alone as factors in
The factors faculty compensation and time and threat of
Future Analysis and Research
This factor analysis is a first step in the analysis of the data gathered
in this survey. The data will be combined to create factor scores that
will be used as dependent variables. The six demographic variables
(workplace, job function, type of delivery system used, expertise
regarding distance education, the stage of the respondent’s
organization with regard to capabilities in delivering distance
education, and the area in which the respondent primarily works) will
be used as independent variables to determine how they affect the
barriers perceived. Due to the very large data set obtained (more than
2,500 participants), it will be possible to analyze the barriers
perceived by many different subgroups of the survey.
possible to compare the newer data to the original data set to confirm
the findings of this study or to look for differences. A follow-up
survey could be sent to the entire pool of participants to look at
changes over time. Because survey studies raise so many new and
interesting questions that the data cannot answer, it may be fruitful to
conduct a quantitative or qualitative follow-up with a select sample of
the original respondents.
Data is still being collected using the online survey. It may be
variance in the data. The ten categories identified here probably do
not represent completely the constructs that comprise barriers to
distance education. Further research is needed both to validate the
The model developed in this study accounts for 52% of the
findings of this study and to identify additional constructs that would
account for a greater proportion of the variance.
it became clear that there was an inherent bias in the original survey
toward a faculty perspective. There is need for future research
concerning barriers to distance education from the student
perspective. For instance, the literature involving student dropout
rates (Cookson 1989; Cookson 1990) should be considered.
Finally, as we worked with the responses to this large dataset,
1. The authors realize that 0.30 is not the most conservative cutoff
that could be used. Reasons for deciding on this cutoff are mainly
threefold. First, this is an exploratory factor analysis, and the factors
found using the methodology presented here are reasonable based on
the literature and our experience. Secondly, given an n > 2,500
responses, it seems unlikely that the weaker factors we included are
random noise. Finally, it is common practice in educational,
exploratory research using factor analysis to use the cutoff value of
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