Contributed by:
This article reports on a large-scale, exploratory factor analysis study that determined the underlying constructs that comprise student barriers to online learning.
The eight factors found were:-
(a) administrative issues,
(b) social interaction,
(c) academic skills,
(d) technical skills,
(e) learner motivation,
(f) time and support for studies,
(g) cost and access to the Internet, and
(h) technical problems.
Independent variables that significantly affected student ratings of these barrier factors included: gender, age, ethnicity, type of learning institution, self-rating of online learning skills, the effectiveness of learning online, online learning enjoyment, prejudicial treatment in
traditional classes, and the number of online courses completed.
1.
Distance Education
Vol. 26, No. 1, May 2005, pp. 29–48
Student Barriers to Online Learning: A
factor analytic study
Lin Y. Muilenburga* and Zane L. Bergeb
aUniversity of South Alabama, USA; bUniversity of Maryland, USA
University
00000May
Taylor
Distance of2005
SouthLtd
10.1080/01587910500081269
CDIE108109.sgm
0158-7919
Original
Open
12005
26 and
and
Article
Education
Distance Alabama2460
(print)/1475-0198
Francis Learning Wildflower
Association
(online) LaneHuntingtonMD
of Australia, Inc. 20639USALin@muilenburgs.com
This article reports on a large-scale (n = 1,056), exploratory factor analysis study that determined
the underlying constructs that comprise student barriers to online learning. The eight factors
found were (a) administrative issues, (b) social interaction, (c) academic skills, (d) technical skills,
(e) learner motivation, (f) time and support for studies, (g) cost and access to the Internet, and (h)
technical problems. Independent variables that significantly affected student ratings of these
barrier factors included: gender, age, ethnicity, type of learning institution, self-rating of online
learning skills, effectiveness of learning online, online learning enjoyment, prejudicial treatment in
traditional classes, and the number of online courses completed.
As the popularity of the Internet grows, so does the potential for online learning. A
great deal of evidence exists showing that no significant differences should be
expected regarding the effectiveness of well-designed online learning compared with
well-designed in-person learning (Clark, 1983; Russell, 1999). Despite this, signifi-
cant differences still exist in the way students perceive their online experiences
during learning. To the extent that these students’ perceptions are negative regard-
ing their past, present, or future online learning experiences, the students’ percep-
tions may contribute to such outcomes as higher dropout rates (Carr, 2000), low
motivation of students to learn (Maltby & Whittle, 2000), and lower student satis-
faction with the learning experience (Kenny, 2003). Still, these outcomes are not
true for all students, in all situations, and at all times. What causes individual differ-
ences in outcomes for online learners?
Research on individual differences among students is conducted to increase our
ability to design instruction, to improve how we instruct, and to advise students. In
*Corresponding author. 2460 Wildflower Lane, Huntingtown, MD 20639, USA. Email:
ISSN 0158-7919 (print); 1475-0198 (online)/05/010029–20
© 2005 Open and Distance Learning Association of Australia, Inc.
DOI: 10.1080/01587910500081269
2.
30 L. Y. Muilenburg and Z. L. Berge
part, we want to better understand which students will face barriers when attempting
to learn online, what those barriers are, and ultimately how we can help individuals
in their learning by understanding and ameliorating their particular obstacles.
The survey research reported in this article sought to represent the perceptions of
students who differed on 11 independent variables: (a) gender; (b) age; (c) self-
reported ethnicity; (d) type of learning institution they attended (e.g., community
college, undergraduate, graduate, business/corporate/non-profit, and government/
military); (e) ability and confidence with online learning technology (from “not
currently using these technologies” to “being comfortable and confident with online
learning technologies”); (f) learning effectiveness in the online environment (from
“cannot learn as well online,” through “no difference between online and traditional
classroom,” to “learn better online”); (g) learning enjoyment in the online classroom
(from “enjoy online learning significantly less,” to “enjoy online learning signifi-
cantly more than the traditional classroom”); (h) the number of online courses
completed; (i) the number of online courses dropped; (j) the likelihood of taking a
future online course; and (k) whether or not students experienced prejudicial treat-
ment in the traditional classroom due to cultural background, disability, or other
personal characteristic. More than 1,000 survey respondents rated the severity of 47
separate student barriers to distance education on a 1–5 Likert scale (from “no
barrier” to “a very strong barrier,” respectively).
Literature Review
Studies have documented both favorable and unfavorable students’ perceptions in
distance education. The authors reviewed the literature specifically on students’
perceived barriers to online learning and more generally on students’ perceived
barriers to learning. The goal was to seek out barriers, issues, and success factors
from the students’ perspectives that may affect the learning outcomes (e.g., learning
effectiveness, learner attitudes, and motivation). We also searched for indications of
what background characteristics and demographics of the learner might affect the
outcomes of their online learning. Previous studies have found significant differences
in learning, attitudes, motivation, or experiences based on:
(1) gender (e.g., Chen, 1986; Teo & Lim, 2000; Young, 2000);
(2) age (e.g., Rekkedal, 1983);
(3) ethnicity (e.g., Owens, 1998; Branden & Lambert, 1999; Chen, 1999);
(4) ability and confidence with online learning technology (from “not currently
using these technologies” to being “comfortable and confident with online
learning technologies”). In other words, students’ experiences with learning tech-
nologies (e.g., Koohang, 1989; Hara, 1998; Hara & Kling, 1999);
(5) the type of learning institution they attend (community college, undergraduate,
graduate, business/corporate/non-profit, and government/military) which may
be compared outright, or which may also speak to their prior educational level
(e.g., Rekkedal, 1983; Sheets, 1992; Mungania, 2003); and
3.
Student Barriers to Online Learning 31
(6) learning effectiveness in the online environment (from “cannot learn as well
online,” through “no difference between online and traditional classroom,” to
“learn better online”), or self-efficacy—their perceptions that one can be a
successful student online (Mungania, 2003).
To these we added several variables we wanted to explore:
(7) learning enjoyment in the online classroom (“enjoy online learning significantly
less,” to “enjoy online learning significantly more than the traditional class-
room”);
(8) number of online courses completed;
(9) number of online courses dropped;
(10) likelihood of taking a future online course; and
(11) whether students experienced prejudicial treatment in the traditional classroom due
to cultural background, disability or other personal characteristics.
Two rounds of this survey were conducted. The first is identified here as the “pilot
study,” and the second round as the “main study.”
Pilot Study
The initial survey items were drawn from a review of literature, from previous stud-
ies on barriers conducted by Muilenburg and Berge (2001), and from content analy-
ses of selected case studies (Berge & Mrozowski, 2001).
The literature was reviewed for an initial organizing framework. Garland (1993)
studied student perceptions of the situational, institutional, dispositional, and episte-
mological barriers to persistence. Later, Schilke (2001), updating Garland’s model
of barriers to persistence in distance education, added a technical factor. In his
review of the literature, Berge (1995) summarized the responsibilities of the online
instructor using four categories: pedagogical, technical, social, and managerial. For
the pilot study, and as a matter of convenience, we started with Berge’s framework
as a grouping mechanism for the barriers identified from the literature. The pilot
version of the survey, which contained 61 individual barrier items, was released on
the World Wide Web in May, 2003. 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 into an output file
and imported into SPSS™ (Statistical Package for the Social Sciences). Respon-
dents were asked to rate each of the 61 barriers on a 1–5 Likert scale.
To announce this pilot survey, we sent individual e-mail messages to personal
acquaintances; to the e-mail addresses of 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
4.
32 L. Y. Muilenburg and Z. L. Berge
the topic of discussion was believed to be related to education, distance education,
and technology-enhanced learning. The announcement included background
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 estimate the rate of return accurately.
Data were collected between May, 2003 and July, 2003. We conducted a factor
analysis on the first round of data collected (n = 423), then evaluated the match
between the survey constructs and the factors yielded through the statistical analysis.
The questions on the pilot survey that did not exceed a 0.4 cutoff for factor loadings
were deleted, and a second version of the survey was drafted.
Results of the Pilot Study
A factor analysis of the responses to the pilot resulted in six factors:
(1) Time/interruptions is a grouping that has to do with the perceived barriers to
students’ spending time in learning online and the interruptions that may
disrupt a student’s learning.
(2) Infrastructure/support services. This grouping, from the students’ perspective, has
to do with issues that the instructor or organization could control.
(3) Motivation. This grouping has to do with the psychological processes that cause
students to persist in meeting their learning goals.
(4) Prerequisite skills. This grouping consists of areas that most students believe they
need to have mastered to a certain degree before entering the online classroom.
(5) Technical. This grouping refers to students being comfortable with the online
system and the software/hardware that is being used in online learning.
(6) Social. This grouping refers to the learning environment that is created for learn-
ing online which should be friendly and social, and one in which learning is
promoted. This suggests promoting human relationships, developing group
cohesiveness, maintaining the group as a unit, and in other ways helping partici-
pants to work together for a mutual cause.
Conclusions of the Pilot Study
From our analysis of the pilot data, we determined that the Berge model developed
in 1995 that listed the responsibilities of faculty was not an illuminating way to cate-
gorize students’ perceptions regarding perceived barriers to online learning.
Main Survey
Instead of the Berge framework, the questions on the main survey were grouped into
six parts corresponding to the results of the factor analysis of the pilot study: techni-
cal, infrastructure/support services, social, prerequisite skills, motivation, and time/
interruptions (see survey at http://www.umbc.edu/oit/phonetree/student_barrier/
5.
Student Barriers to Online Learning 33
Modifications to the Pilot Survey
Changes were made to the pilot survey before data collection began for the main
study based on the factor analysis and the comments from the pilot study. Fifteen of
the original barriers were dropped and one barrier was added that was not in the
pilot study, making a total of 47 barriers for the main study. The instructions that
accompanied the survey were edited to clarify and explain more fully that the
purpose of the survey is to gather data on students’ perceptions. Also, it was explained
that answering “no barrier” could mean any of the following: that the respondents
believe the barrier “does not apply to them,” or that the respondents “have the skills
to deal with this barrier,” or that the respondents have “never experienced this
barrier,” or, if the respondents have never taken an online course, that they “would
not experience this barrier” should they take an online class in the future.
Results of the Main Study
Data were collected using the second draft of the survey from July, 2003 to Novem-
ber, 2003. Survey responses with large blocks of missing data were deleted. Addi-
tionally, survey responses that had the same rating for every barrier item (e.g., all
“no barrier”) were judged to have not been mindfully completed and were therefore
deleted. After data cleaning, 1,056 valid surveys remained and were analyzed using
SPSS. Of the 1,056 survey respondents, 24.0% (n = 253) were 18–24 years of age,
14.6% (n = 154) were 25–31, 15.4% (n = 163) were 32–38, 15.4% (n = 163) were
39–45, 15.1% (n = 159) were 45–51, 11.4% (n = 120) were 52–57, and 4.1% (n =
44) were 58 or above. There were 31% (n = 329) men and 69% (n = 727) women.
The ethnicity of respondents included 79.6% (n = 841) white/non-Hispanics, 5.8%
(n = 61) black/non-Hispanics, 5.7% (n = 60) Asian/Pacific Islanders, 4.0% (n = 42)
Hispanics, 0.4% (n = 5) American Indian/Alaskan Natives, and 4.5% (n = 47)
“other.” The institution types in which respondents took their most recent online
courses were 52.3% (n = 552) from graduate schools, 27.6% (n = 291) undergradu-
ate schools, 13.1% (n = 138) business/corporate/non-profit, 4.2% (n = 45) govern-
ment/military, and 2.8% (n = 30) community colleges. A little over 6.0% (n = 67) of
respondents felt that they had experienced prejudice that significantly affected their
learning in the traditional classroom environment due to their ethnicity, or because
of personal characteristics such as a disability or their appearance.
When rating their comfort level with online learning technologies, the majority of
respondents, 67.7% (n = 715), are comfortable and confident learning online;
23.3% (n = 246) are using e-mail and Internet for personal productivity but are not
taking online courses; 7.2% (n = 76) are learning online but feel unsure of their
skills; and 1.8% (n = 19) do not use these technologies very often. Of those respon-
dents who have studied online (n = 735), when considering how effectively they feel
about their learning online, 33.2% (n = 244) said they cannot learn as well online,
44.0% (n = 323) do not see a difference between their ability to learn online or in a
traditional classroom, and 22.8% (n = 168) think they learn better online. For those
6.
34 L. Y. Muilenburg and Z. L. Berge
who have not taken an online class yet (n = 321), 60.1% (n = 193) predict they
would learn poorly online, 32.4% (n = 104) think there would be no difference
between learning online or in a traditional classroom, and 7.5% (n = 24) predict
they would learn better online. The results were quite similar when asked how well
respondents enjoyed learning online. For those who have studied online: 30.9% (n =
227) enjoyed online learning less, 35.2% (n = 258) enjoyed online learning about
the same as a traditional classroom; and 33.9% (n = 249) enjoyed online learning
more. For those who have never taken an online class, 53.7% (n = 173) predicted
they would enjoy online learning less, 34.8% (n = 112) predicted they would like
online learning about the same, and 11.5% (n = 37) predicted they would like online
learning more.
Respondents ranged from highly experienced online learners (14.4%, n = 152
have completed eight or more online courses) to those who have never taken an
online course (33%, n = 347). Most respondents have never dropped an online
course (83%, n = 875), 11% (11.1%, n = 117) have dropped one course, 3.2% (n =
34) have dropped two courses, and less than 3% (n = 30) have dropped three
courses or more. When asked if they were likely to take a future course online, 5.3%
(n = 56) said definitely not, 24.1% (n = 254) said probably not, 35.8 (n = 378) said
probably yes, and 34.8% (n = 368) said definitely yes.
Item analyses were conducted on the 47 items hypothesized to assess perceptions
of barriers to online learning. Each of the 47 items was correlated with the total score
(r > 0.94) for Student Barriers (with the item removed); therefore, all 47 items were
retained in the scale. The reliability of the Student Barriers to Online Learning Scale
was 0.94 as measured by Cronbach’s alpha.
Analyses of the Main Study
A principal component factor analysis (PCFA) with Varimax rotation was used to
determine the underlying structure of the data. The factorability of the matrix was
determined using the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (MSA).
In our study, the MSAs for individual variables ranged from 0.89 to 0.97. The
MSA for the entire matrix was 0.937. Each of these MSA values is well above the
0.80 meritorious level (Kaiser & Rice, 1974).
Eight factors were identified using the latent root criterion, which is the most
common technique for determining the number of factors to extract (Hair, Ander-
son, Tatham, & Black, 1998). The initial eigenvalues were greater than 1, which are
considered significant. Table 1 shows the percentage variance accounted for by each
of the variables.
The PCFA of the 47 barriers to distance education listed in the survey resulted in
eight factors that accounted for 62.4% of the overall variance.1 A cutoff for statistical
significance of the factor loadings of 0.5 was used, because loadings of 0.5 or greater
are also considered practically significant (Hair et al., 1998). Each item loaded
distinctively on one factor; the highest factor loading was separated from its next
nearest loading by at least 0.2. Two of the forty-seven barrier items were deleted
7.
Student Barriers to Online Learning 35
Table 1. Total variance explained
Initial eigenvalues
Component Total % of variance Cumulative %
Administrative/instructor issues 13.04 13.19 13.19
Social interactions 4.81 9.54 22.73
Academic skills 2.97 8.68 31.41
Technical skills 2.43 8.16 39.56
Learner motivation 2.07 7.46 47.02
Time and support for studies 1.66 6.10 53.12
Cost and access to the Internet 1.29 5.15 58.28
Technical problems 1.06 4.13 62.41
because their factor loadings were below the 0.5 cutoff. These items were: (a) lack
accreditation or sanction by professional organization, and (b) fear the loss of
privacy, confidentiality, or issues with property rights. Table 2 shows the variables
loading on each of the components, which produced the following factors: (a)
administrative issues, (b) social interactions, (c) academic skills, (d) technical skills,
(e) learner motivation, (f) time and support for studies, (g) cost and access to the
Internet, and (h) technical problems.
The Appendix defines these terms. It is interesting to note that it appears that
the “technical issues” factor in the pilot study broke out into two factors in the
main study: access/cost and technical problems. Similarly, the “prerequisite skills”
factor from the pilot study split into academic skills and technical skills in the main
Overall Priority of Student Barriers
Factor scores were calculated for each of the eight factors identified above. The
means for the eight factors were used to rank order the barrier factors from the most
severe to least severe (see Table 3). The single most important barrier to students
learning online was a lack of social interaction (M = 2.36). Administrative/instructor
issues, time and support for studies, and learner motivation clustered very closely as
the next most severe barriers (M = 2.05, 1.91, and 1.91). Less important barriers
were technical problems and cost/access to the Internet (M = 1.70 and 1.60).
Respondents rated a lack of technical skills and academic skills as very low obstacles
to learning online (M = 1.30 and 1.22).
Differences Among Subgroups
To determine whether particular subgroups of respondents viewed barriers differ-
ently, a series of ANOVAs was conducted using factor scores for the barriers as
dependent variables. Ten of the eleven independent variables tested affected student
8.
36 L. Y. Muilenburg and Z. L. Berge
Table 2. Rotated component matrix of factors
Factors
Components (45) 1 2 3 4 5 6 7 8
Lack of sufficient academic advisors 0.750
Course materials not always delivered 0.747
on time
Instructors do not know how to teach 0.743
Lack of clear expectations/instructions 0.729
Difficulty contacting academic or 0.726
administrative staff
Lack of timely feedback from 0.726
Lack of access to instructor/expert 0.690
Lack of support services such as tutors 0.657
Lower quality materials/instruction 0.609
Insufficient training to use the delivery 0.543
Class size is not right for online 0.510
Lack of interaction/communication 0.828
among students
Online learning seems impersonal 0.809
Afraid of feeling isolated 0.803
Lack of social context cues 0.770
Lack of student collaboration 0.757
Prefer to learn in person 0.717
Lack language skills for online learning 0.816
Lack writing skills for online learning 0.807
Lack reading skills for online learning 0.787
Lack communication skills for online 0.770
Lack typing skills for online learning 0.702
Shy or lack confidence for online 0.660
Fear new tools for online learning 0.778
Fear computers and technology 0.725
Lack online learning software skills 0.706
Lack skills for using the delivery 0.689
Unfamiliar with online learning 0.648
technical tools
Fear different learning methods used 0.598
for online learning
9.
Student Barriers to Online Learning 37
Table 2. Continued
Factors
Components (45) 1 2 3 4 5 6 7 8
Procrastinate, cannot get started 0.812
Lack personal motivation for online 0.796
Must take on more responsibility for 0.762
Choose easier, less demanding aspects 0.678
of assignments
Online learning environment is not 0.625
inherently motivating
Fear family life will be disrupted 0.768
Online learning cuts into my personal 0.759
Lack support from family, friends, 0.671
Significant interruptions during study 0.638
at home/work
Insufficient time to learn during online 0.531
Lack adequate Internet access 0.732
Online learning technology costs too 0.727
Needed technology is not available 0.656
Lack of consistent platforms, 0.688
browsers, software
Incompatibility creates technical 0.673
Lack technical assistance 0.623
Note. Extraction method: principal component analysis. Rotation method: Varimax with Kaiser
normalization. Cutoff = 0.50. Factors: (1) Administrative/instructor issues, (2) social interactions,
(3) academic skills, (4) technical skills, (5) learner motivation, (6) time and support for studies, (7)
cost and access to the Internet, and (8) technical problems.
ratings of barriers to online learning significantly (p < 0.05): gender, age, ethnicity,
type of learning institution, self-rating of online learning skills, effectiveness of learn-
ing online, online learning enjoyment, the number of online courses completed, the
likelihood of taking a future online course, and persons who reported experiencing
prejudicial treatment. The only variable that did not show significant differences
among the means was the number of courses dropped, and this variable contained
too few people in most categories to run an ANOVA.
To determine the strength of association of the independent variables to each of
the eight barrier factors, eta squared was calculated for each ANOVA. Eta squared
10.
38 L. Y. Muilenburg and Z. L. Berge
Table 3. Priority of student barriers to online learning
Barrier factors (n = 1,056) Means SD
Social interactions 2.36 1.07
Administrative/instructor issues 2.05 0.80
Time and support for studies 1.91 0.79
Learner motivation 1.91 0.93
Technical problems 1.70 0.73
Cost and access to the Internet 1.60 0.73
Technical skills 1.30 0.50
Academic skills 1.22 0.50
indicates the proportion of variance in the dependent variable that is explained by
the independent variable, and values “of .01, .06, and .14 are by convention inter-
preted as small, medium, and large effect sizes, respectively” (Green & Salkind,
2003, p. 162). A summary of the eta squared values for the significant ANOVA tests
is found in Table 4.
Discussion and Implications
There are many significant relationships in this data set that need to be explored.
This article will focus on the four most critical barriers previously identified: (a)
social interaction, (b) administrative/instructor issues, (c) learner motivation, and
(d) time/support for studies. The focus is also limited in this paper to the five inde-
pendent variables that have the most effect on the above four barriers. These are
variables with medium to large effect sizes (see bold print in Table 4): (a) ability and
confidence with online learning technology, (b) effectiveness of online learning, (c)
online learning enjoyment, (d) online courses completed, and (e) the likelihood of
taking a future online course. For ANOVAs that were significant, post hoc pair-wise
comparisons were conducted using t tests with Bonferroni correction.
Ability and Confidence with Online Learning Technology
Respondents with the highest level of comfort and confidence using online learning
technologies perceived significantly fewer barriers for social interaction, administra-
tive/instructor issues, learner motivation, and time and support for studies than the
other three groups who were unsure of their skills or were not using online learning
technologies. Differences among the less confident groups and those not using
online learning technologies were not significant. Means for the various groups are
presented in Table 5.
The association was moderate between ability and confidence with online learning
technologies and the dependent variables social interaction, administrative/instruc-
tor issues, and learner motivation (η2 = 0.116, 0.064, and 0.124, respectively).
11.
Table 4. Eta squared values for significant ANOVAs (factors by independent variables)
Barrier factors (from most to least important)
Social Admin/ Learner Time and Technical Cost and Technical Academic
Independent variables interaction instr issues motivation support problems access skills skills
Gender 0.007 0.004
Men rate these factors higher than women
Age 0.049 0.034 0.112 0.025 0.022
Barriers perceived decrease as age increases
0.014 0.033 0.025 0.014 0.030 0.014 0.046
Asians and Hispanics rate barriers higher. Whites
and blacks rate barriers lower
Learning institution 0.014 0.046
Undergrads rate barriers higher. Corporate and
graduate students rate barriers lower
Ability and confidence with online learning technology 0.116 0.064 0.124 0.053 0.030 0.027 0.096 0.040
Those learning online but unsure of skills rate
barriers highest … even higher than those not yet
learning online
Effectiveness at online learning 0.378 0.169 0.213 0.079 0.038 0.045 0.074 0.016
Those who say they don’t, or predict they
wouldn’t, learn well online rate barriers highest
Online learning enjoyment 0.397 0.153 0.161 0.046 0.028 0.041 0.053
Those who don’t enjoy online learning rate
barriers high. Those who predict they wouldn’t
enjoy online learning rate barriers highest
Student Barriers to Online Learning 39
12.
Table 4. Continued
Barrier factors (from most to least important)
Social Admin/ Learner Time and Technical Cost and Technical Academic
Independent variables interaction instr issues motivation support problems access skills skills
Number of online course completed 0.133 0.068 0.112 0.030 0.042 0.047 0.020
Those who have completed NO online courses
rate barriers higher. Ratings decrease as more
courses are completed
40 L. Y. Muilenburg and Z. L. Berge
Likelihood of taking a future online course 0.261 0.088 0.146 0.028 0.008 0.022 0.029
Likelihood of taking a future course increases as
barriers decrease
Experienced prejudice in face-to-face classes 0.005 0.005 0.009 0.009 0.017
People who experienced prejudice rated these
barriers higher
13.
Student Barriers to Online Learning 41
Table 5. Barrier means by ability and confidence with online learning technology
Number Social Admin/instr Learner Time and
Response category of cases interaction issues motivation support
I do not use online learning 19 3.32 2.58 2.15 2.12
technology (such as e-mail and the
Internet) very much
I use online learning technologies 246 2.81 2.32 2.41 2.12
such as e-mail and the Internet for
my own personal productivity but
not so much for education or
training purposes
I am learning online, but I am 76 2.95 2.31 2.35 2.33
unsure of my skills when doing so
I have learned, or I am learning, 715 2.11 1.91 1.68 1.79
online and feel comfortable and
confident when I do so
Ability and confidence with online learning technologies had a modest association
with time and support for online learning (η2 = 0.053).
Effectiveness of Online Learning
For the question related to a respondent’s effectiveness at online learning, students
who had taken an online class were asked to compare how well they learned online
to their learning in a traditional classroom. If students had never taken an online
course, they were asked to predict how well their learning online would compare to a
traditional class. A significant relationship exists for this independent variable across
each of the four dependent variables: students who indicated they cannot learn well
online (or predicted a lack of success) had the highest barrier ratings. Those who
indicated their learning online was equal to the traditional classroom (or predicted it
would be the same) had a moderate level of barriers. Students who felt they learned
better online had the lowest mean for the barrier factors. Interestingly, for each level
of comparison (experienced poor learning vs. predicted poor learning, experienced
equal learning vs. predicted equal learning, and experienced better learning vs.
predicted better learning) students who had not taken online classes predicted
higher barriers than students who had taken classes. This comparison was statisti-
cally significant in all cases, except for comparisons with level 6 (students predicting
better learning online), possibly due to the relatively small number of respondents in
this group (n = 22). Means for the various groups are presented in Table 6.
There was a strong association between effectiveness of online learning and social
interaction (η2 = 0.378), administrative/instructor issues (η2 = 0.169), and learner
motivation (η2 = 0.213). Effectiveness of online learning had a moderate effect with
time and support for online learning (η2 = 0.079).
14.
42 L. Y. Muilenburg and Z. L. Berge
Table 6. Barrier means by effectiveness of online learning
Number Social Admin/instr Learner Time and
Response category of cases interaction issues motivation support
I cannot learn as well online as I can 244 3.03 2.29 2.20 2.12
in the classroom with other learners
and the instructor
I really don’t see much difference in 323 1.83 1.74 1.53 1.73
my learning in an online learning
environment compared to being in
the classroom with other learners
and the instructors
I learn better through online 168 1.59 1.70 1.43 1.69
learning compared to being in the
same room as other learners and the
While I have never completed an 193 3.22 2.55 2.58 2.24
online class, I predict I would not
learn as well online as I would in the
classroom with other learners and
the instructor
While I have never completed an 104 2.19 2.01 1.87 1.79
online class, I predict I would not
see much difference in my learning
in an online learning environment
compared to being in the classroom
with other learners and the
While I have never completed an 24 1.84 2.11 2.11 1.74
online class, I predict I would learn
better online compared to being in
the classroom with other learners
and the instructor
Online Learning Enjoyment
The rating scale for online learning enjoyment was similar to the effectiveness of
online learning scale described above. Students were asked to compare how
much they enjoyed learning online with learning in a traditional classroom. If
students had never taken an online course, they were asked to predict how well
they would like learning online. The same trend exists for online learning enjoy-
ment, as was described above for effectiveness of online learning. Students who
did not enjoy learning online as much as they enjoy learning in a traditional
class (or predicted they would not) had significantly higher barrier ratings. Those
who enjoyed learning online equally with a traditional classroom (or predicted it
would be the same) had a moderate level of barriers. The lowest barrier ratings
were from students who enjoyed online learning more. Again, barriers were
15.
Student Barriers to Online Learning 43
higher at each level of enjoyment when comparing students who predicted versus
students who rated their actual experiences with online learning. This relation-
ship was statistically significant for comparisons within the social interaction,
administrative/instructor issues and learner motivation factors, except when using
level 6 (students predicting better learning online). Although the same pattern
is evident in the time and support factor, many of the pair-wise comparisons
were not statistically significant. Means for the various groups are presented in
Table 7.
The strongest association found in this study was between online learning enjoy-
ment and social interaction (η2 = 0.397). Administrative/instructor issues (η2 =
0.153) and learner motivation (η2 = 0.161) were also strong relationships. Online
learning enjoyment had a small effect with time and support for online learning
(η2 = 0.046).
Table 7. Barrier means by online learning enjoyment
Number Social Admin/instr Learner Time and
Response category of cases interaction issues motivation support
I enjoy the online learning 227 3.06 2.18 2.11 2.02
experience significantly less
I really don’t see much difference 258 1.89 1.81 1.58 1.85
in my enjoyment between
learning online and in the
classroom with other learners and
the instructor
I enjoy the online learning 249 1.61 1.74 1.52 1.68
experience significantly more
While I have never completed an 173 3.32 2.64 2.52 2.16
online class, I predict I would
enjoy the learning experience
significantly less online compared
to being in the classroom with
other learners and the instructor
While I have never completed an 112 2.37 2.07 2.17 2.03
online class, I predict I would not
see much difference in my
enjoyment of the online learning
environment compared to being
in the classroom with other
learners and the instructor
While I have never completed an 37 1.74 2.07 1.91 1.75
online class, I predict I would
enjoy the learning experience
significantly more online than
being in the classroom with other
learners and the instructor
16.
44 L. Y. Muilenburg and Z. L. Berge
Number of Online Courses Completed
Respondents to this survey who had never taken an online course (n = 347) rated
each of the factors as significantly more severe barriers than any of the other groups.
Although the differences from level to level are not usually statistically significant,
there is also a visible trend in the data that as people complete more online courses,
ratings of the barriers decrease. Means for the various groups are presented in Table
8. It makes sense that perceived barriers decrease as experience with online learning
increases. What is most interesting is the huge drop in barriers perceived after
completing just one course, where fear of the unknown appears to be important.
The number of online courses completed had a moderate effect on barriers
perceived in social interaction (η2 = 0.133), administrative/instructor issues (η2 =
0.068), and learner motivation (η2 = 0.112). There was a small association between
the number of online courses completed and time and support for online learning
(η2 = 0.030).
Likelihood of Taking a Future Online Course
Table 9 presents the means for each dependent variable by subgroups. For the
factors social interaction, administrative/instructor issues, and learner motivation
there is a statistically significant pattern: as the barriers perceived decrease, the likeli-
hood of taking a future online course increases. Although not statistically significant
at each level, the same pattern holds true for time and support for studies.
The highest mean barrier rating (M = 3.66) found in the study was for the social
interaction barrier when considering the likelihood of voluntarily taking a future
online course. Clearly, overcoming the lack of social interaction in online courses is a
major contributor to the decision to continue with online learning. The likelihood of
voluntarily taking a future online course was related strongly to social interaction
barriers (η2 = 0.261) and problems with learner motivation (η2 = 0.146). Adminis-
trative and instructor issues (η2 = 0.088) had a moderate effect size. There was a
Table 8. Barrier means by number of online courses completed
Number Social Admin/instr Learner Time and
Response category of cases interaction issues motivation support
No online courses completed 347 2.82 2.32 2.29 2.07
1 online course completed 158 2.46 1.89 1.99 1.91
2 online courses completed 119 2.24 1.98 1.80 1.86
3 online courses completed 95 2.20 1.99 1.77 1.89
4 online courses completed 83 2.23 2.08 1.73 1.95
5–7 online courses completed 102 1.97 1.78 1.54 1.73
8–10 online courses completed 47 1.82 1.84 1.55 1.73
11–13 online courses completed 38 1.79 1.67 1.44 1.89
14 or more online courses completed 67 1.60 1.95 1.41 1.62
17.
Student Barriers to Online Learning 45
Table 9. Barrier means by likelihood that I will voluntarily take a future online course
Number Social Admin/instr Learner Time and
Response category of cases interaction issues motivation support
Definitely not 56 3.66 2.54 2.50 2.12
Probably not 254 2.98 2.35 2.38 2.09
Probably yes 378 2.30 2.00 1.89 1.91
Definitely yes 368 1.79 1.81 1.51 1.76
small association between the lack of time and support for online learning and the
likelihood of taking a future online course (η2 = 0.028).
A Cautionary Thought for Further Research
What does this research say to distance educators? This research design used barri-
ers as the dependent variables, which has certain implications for interpretation of
the findings. For instance, a lack of social interaction was the most severe barrier as
perceived by students overall. The findings here are that social interaction is strongly
related to online learning enjoyment, effectiveness of learning online, and the likeli-
hood of taking another online class. Therefore, it seems logical that improving social
interaction in online learning would lead to a more effective and enjoyable educa-
tional experience—one that students would want to repeat.
However, this research design does not speak to causation. It may be that increas-
ing social interaction would lead to educational benefits. Conversely, it may be that
because students enjoy online learning, or believe that online learning is as effective
as in-person education, their social interaction is improved. Perhaps certain types of
students simply don’t need much social interaction to find learning enjoyable and
effective. Several barrier factors and independent variables in this study are open to
this type of speculation regarding the direction of causation. For those distance
educators and researchers interested in reducing student barriers to distance educa-
tion and improving online learning, further investigation of causation may be a
useful line of research.
Additionally, if one looks at the number of classes that a student has taken, there is
a marked drop-off of perceived barriers for students who have taken only one course
compared to those who have taken no online classes. This may be because people
who take online classes are those who perceive lower barriers before taking any
online classes. Or it may be that after experiencing just one online class most
students either overcome many barriers or find out that they had overestimated the
barriers before taking any online courses. The point is, this study did not show
causes. We can show relationships among the barriers and the independent variables
we have chosen. To show what causes these relationships, further research would
need to be done using time-series and probably some qualitative methods that would
allow the researcher to determine the causation within these relationships.
18.
46 L. Y. Muilenburg and Z. L. Berge
1. The 62.4% of overall variance accounted for was deemed to be satisfactory. Hair et al. (1998)
state “… in the social sciences, where information is often less precise, it is not uncommon to
consider a solution that accounts for 60% of the total variance (and in some cases even less) as
satisfactory” (p. 104).
Notes on Contributors
Lin Muilenburg is an independent consultant and a doctoral candidate in Instruc-
tional Design and Development at the University of South Alabama, USA.
Zane Berge is an Associate Professor of Education at the University of Maryland,
Baltimore County, USA.
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Definitions of the Eight Barrier Factors
(1) Administrative/instructor issues. Students perceive barriers that administrators and
instructors control, such as course materials not always being delivered on time,
lack of sufficient academic advisors online, and lack of timely feedback from the
instructor.
(2) Social interactions. These are obstacles to online learning that students perceive
as being caused by a lack of interaction with peers or the instructor, such as the
lack of student collaboration online, the lack of social context cues, or their
being afraid of feeling isolated in online courses.
(3) Academic skills. This factor concerns respondents’ perceived barriers to online
learning due to their lack of academic skills in such areas as writing, reading, or
communication.
(4) Technical skills. This factor concerns respondents’ perceived barriers to online
learning due to their lack of technical skills such as fearing new tools for online
learning, lack of software skills, or their unfamiliarity with online learning tech-
nical tools.
(5) Learner motivation. Respondents answered whether they had certain characteris-
tics that would affect their motivation in online courses such as whether they
20.
48 L. Y. Muilenburg and Z. L. Berge
procrastinate, choose easier aspects of an assignment to complete, or feel the
online learning environment is not inherently motivating.
(6) Time and support for studies. This factor concerns the respondents’ perspectives
on whether a lack of time or support from family, friends, or people in the work-
place causes barriers to their online learning.
(7) Cost and access to the Internet. This factor concerns whether the respondents find
access to the Internet too expensive, fear the loss of privacy, confidence, or
property rights, or otherwise find access to the Internet limited to the point of
raising barriers to them.
(8) Technical problems. This factor concerns such things as a lack of consistent plat-
forms, browsers, and software, or the lack of technical assistance that causes
obstacles to online learning.
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