08832323.2015.1007906
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Factors That Affect Students’ Capacity to Fulfill the
Role of Online Learner
Debra R. Comer, Janet A. Lenaghan & Kaushik Sengupta
To cite this article: Debra R. Comer, Janet A. Lenaghan & Kaushik Sengupta (2015) Factors That
Affect Students’ Capacity to Fulfill the Role of Online Learner, Journal of Education for Business,
90:3, 145-155, DOI: 10.1080/08832323.2015.1007906
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Date: 11 January 2016, At: 19:13
JOURNAL OF EDUCATION FOR BUSINESS, 90: 145–155, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2015.1007906
Factors That Affect Students’ Capacity to Fulfill
the Role of Online Learner
Debra R. Comer, Janet A. Lenaghan, and Kaushik Sengupta
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Hofstra University, Hempstead, New York, USA
Because most undergraduate students are digital natives, it is widely believed that they will
succeed in online courses. But factors other than technology also affect students’ ability to
fulfill the role of online learner. Self-reported data from a sample of more than 200
undergraduates across multiple online courses indicate that students generally view
themselves as having attributes that equip them for online learning. Additionally, courselevel factors affect students’ online learning experiences. Specifically, students in qualitative
(vs. quantitative) courses and in introductory (vs. advanced) classes reported more positive
perceptions of their online learning and various aspects of their coursework.
Keywords: distance education, distance learning, online learner role, online learning,
readiness for online learning
Enhancements in technology have led to an explosion in
online learning. Many college and university presidents
view online education as a critical component of their strategic plan (Allen & Seaman, 2013) and commit substantial
resources to meet the demand for online offerings (Young,
2011). According to a recent survey conducted by Babson
College (Allen & Seaman, 2013), whereas only 1.6 million
postsecondary students in the United States were enrolled
in an online course in 2002, just one decade later, the number had increased to 6.7 million, such that 32% of all students enrolled in higher education in the United States in
2012 had taken at least one course online. Results of the
2013 Association to Advance Collegiate Schools of Business (AACSB) Business School Questionnaire likewise
indicate that between 2007 and 2012, the number of accredited schools that offer fully online courses rose 43%, and
more than 25% of the 480 schools that had participated in
every year of the survey had a fully online degree program
(Nelson, 2013). The growth in online courses responds to
students’ requirements for flexible scheduling (Daymont,
Blau, & Campbell, 2011), as well as their need to develop
the skills to perform in the virtual teams that are becoming
Correspondence should be addressed to Debra R. Comer, Hofstra University, Zarb School of Business, Department of Management and Entrepreneurship, Hempstead, NY 11549-1340, USA. E-mail: debra.r.
comer@hofstra.edu
more prevalent in organizations (Arbaugh, 2014). This
expansion in online education led U.S. News & World
Report to add the “Top Online Education Programs” ranking to its “Best of” lists in 2012 (DeSantis, 2012). Likewise,
the Sloan Consortium, expecting growth in online programs
globally, has recently renamed itself the Online Learning
Consortium (Straumsheim, 2014).
Students’ learning in online courses has often been compared with their learning in more traditional, face-to-face
courses. Because many students today are digital natives,
who have used technology their entire lives (Prensky, 2001;
see also Palfrey & Gasser, 2008), it may be taken for
granted that they will embrace the technology associated
with online classes and flourish in such classes. However,
there is more to success in an online course than technological ability. Students favor online learning for its convenience; those whose work or family responsibilities would
make it onerous or impossible to attend classes on campus
especially appreciate the opportunity online programs
afford (Beqiri, Chase, & Bishka, 2010; Bocchi, Eastman, &
Swift, 2004). Yet, just because online learning is available
to an individual does not mean that the individual will excel
as an online learner. In this study, using a sample of undergraduate students enrolled in various online classes offered
by the management department of an AACSB-accredited
school, we investigated attributes that may predispose students to have positive online learning experiences.
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D. R. COMER ET AL.
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PERCEPTIONS ABOUT THE EFFECTIVENESS OF
ONLINE LEARNING1
Despite the rapid expansion of online programs, unfavorable perceptions persist. According to Allen and Seaman
(2013), chief academic officers in the United States report
that only 30.2% of “their faculty accept the value and
legitimacy of online education” (p. 27); even at institutions that have fully online courses, academic leaders
report that only 38.4% of the faculty members fully
accept online education. In 2010, the University of California offered a $30,000 bonus to create an online course,
but only 70 of thousands of faculty members designed
such a course, due to their skepticism about the efficacy
of online education (Asimov, 2012). Some faculty members express concern that lack of face-to-face interaction
in an online course diminishes student engagement
(Aggarwal, Adlakha, & Mersha, 2006; Barker, 2010;
Zemsky & Massy, 2004). To be sure, online instructors
must avoid a hit-and-run approach in which they merely
post canned lectures and notes on their institution’s course
management system (e.g., Blackboard). When a distance
learning course is poorly planned and executed, students’
satisfaction and learning suffer (Lawrence & Singhania,
2004; Vamosi, Pierce, & Slotkin, 2004). However, a
thoughtful educator can structure and present an online
course in a way that promotes students’ interaction with
their fellow students and instructor and their mastery of
course concepts (Arbaugh & Benbunan-Fich, 2007; Sonner, 1999).
EMPIRICAL RESEARCH ON THE EFFECTIVENESS
OF ONLINE LEARNING
A mounting body of research indicates that students in
online classes achieve the same learning outcomes as their
counterparts in traditional face-to-face classes (Arbaugh,
2000; Redpath, 2012). Online learning can foster interactivity (Han & Hill, 2006; Kuo, Walker, Schroder, & Belland, 2014; Swan, 2002) and collaboration (Arbaugh,
2008, 2010, 2014; Yoo, Kanawattanachai, & Citurs, 2002),
and even promote the participation of students who would
be less likely to contribute to face-to-face discussions (see,
e.g., Comer & Lenaghan, 2013). Hansen (2008) asserted
that participation and a sense of community are stronger in
online courses because students’ interactions are openended and unbounded by time. Because of variability in
the design and delivery of both face-to-face courses and
online courses, simple comparisons between the two types
are misguided and misleading. Instead, it seems that a
given online course will be pedagogically effective to the
extent that the instructor creates a community of learners
by designing the course appropriately; giving students
ample opportunities to connect to the course material, to
their instructor, and to one another; and ensuring that students can understand, apply, and synthesize course material (see Shea et al., 2010; see also Garrison & Arbaugh,
2007).
EMBRACING THE ROLE OF ONLINE LEARNER
Pitting online learning against face-to-face learning has distracted scholars from ascertaining whether online learning
is appropriate for all students. Yet, it has been asserted that
“distance learning is not for everyone” (Crow, Cheek, &
Hartman, 2003, p. 338), and our collective experience
teaching online for numerous semesters suggests that students do vary in their readiness for online learning.
Although some of our students excel in our online courses,
others have trouble completing course requirements. Part of
the problem may be that some members of the latter group
misjudge what online learning entails. Previous studies of
factors that affect students’ performance in online classes
examine familiarity and comfort with e-learning (McVay,
2001; Smith, Murphy, & Mahoney, 2003), computer technology (Bernard, Brauer, Abrami, & Surkes, 2004; Crow
et al., 2003), and dispositional self-management (Hollenbeck, Zinkhan, & French, 2005; Mariola & Manley, 2002;
Salas, Kosarzycki, Burke, Fiore, & Stone, 2002; Smith
et al., 2003). However, they do not consider these attributes
in terms of students’ fulfillment of the responsibilities
inherent in the role of online learner.
A role represents shared normative expectations that
drive and explain behavior (Kahn, Wolfe, Quinn, Snoek, &
Rosenthal, 1964). A given role is defined vis-a-vis its relationship with another role in a role set, such that one person
assumes the principal role while the other assumes a
counter-position (Stryker & Burke, 2000). Counter-role
partners (e.g., supervisor and subordinate or teacher and
student) shape each other’s behavioral expectations. The
performance of any student—whether in a traditional faceto-face class or a wholly asynchronous online class—relies
in large part on the student’s acceptance of key role
responsibilities.
The role responsibilities students must assume when they
take an online class are substantively—and, apparently, for
some students, surprisingly—different from those required of
students in a traditional face-to-face class. Students need to
adjust their behavior, anchored in a traditional model of delivery, to fit the role expectations of the online modality. They
may erroneously assume that online courses require less work
than traditional courses (Crow et al., 2003).2 In reality, online
students have at least as much coursework as students in traditional classes, but they have more leeway as to when and
where they will complete it (Fisher, King, & Tague, 2001).
With their greater autonomy (to paraphrase Stan Lee) comes
greater responsibility. Online students have to work independently and exercise self-discipline to negotiate course
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STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
147
requirements and meet deadlines. They need to be more
active and self-directed learners than passive and teacherdirected followers (Fisher et al., 2001; Knowles, 1990;
McVay, 2001). Students with superior time management
skills and higher self-efficacy are more likely to continue in
an online program (Holder, 2007). Those who routinely procrastinate perform worse in online courses, in part because
they do not participate sufficiently in discussion forums
(Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011).
Indeed, interaction and participation are other key aspects
of the role of online learner (Arbaugh & Benbunan-Fich,
2007; Swan, 2002). Students will succeed to the extent that
they engage in online discussions and contribute to their community of learners (McIsaac & Gunawardena, 1996). Moreover, students’ level of participation in online threaded
discussions is related to their course performance (Hwang &
Francesco, 2010; Krentler & Willis-Flurry, 2005). Given that
coursework in asynchronous online classes relies on textbased communication, students need to write well. They also
need to be able to resolve inevitable technical glitches. Students who can meet the requirements of the online learner role
would be more likely to fare well in an online course. Thus:
H2: Students enrolled in qualitative online courses would
have more favorable perceptions of their learning and
more positive experiences with various aspects of the
course than would students enrolled in quantitative
online courses.
Hypothesis 1 (H1): Students whose personal attributes
enhance their capacity to perform the responsibilities
inherent in the role of online learner would have more
favorable perceptions of their learning in an online
course and more positive perceptions of various
aspects of the course.
H3: Students in an introductory-level course would have
more favorable perceptions of their learning in an
online course and more positive experiences with various aspects of the course than would students in more
advanced courses.
Other Factors That May Affect Fulfillment of the
Responsibilities of the Online Learner Role
In addition to students’ personal attributes, other factors that
may affect their ability to perform the responsibilities of the
online learner role. One factor is the type of course material.
The way course content is disseminated and learned varies by
course (Arbaugh, 2005), and there are “discipline-related differences in online learning outcomes” (Arbaugh & Rau,
2007, p. 67). Although our study sample included only management courses, management is a multidisciplinary field of
study (Arbaugh, 2007). Even in face-to-face classes, instructors of quantitative subjects may be inclined to rely on traditional pedagogical methods to impart facts and formulae to
their students. In face-to-face qualitative courses, on the other
hand, instructors are more apt to use collaborative learning
and to rely upon active learning. Students who enroll in an
online qualitative course likely expect to be participating in
group discussions and otherwise assuming an active part in
their learning. Students may be less likely to anticipate the
extent to which an online quantitative course will require
active learning; thus, they may take such a course even though
they are not ready to take on the role responsibilities of an
online learner. Accordingly, we formulated the following
hypothesis:
By the same token, students may respond differently to a
more advanced course than to an introductory course. An
emerging stream of research suggests that students’ perceptions of their learning and of the effectiveness of online delivery are related not only to course discipline and content, but
also to whether a course is at the introductory or advanced level
(Arbaugh, 2013; Arbaugh, Desai, Rau, & Sridhar, 2010;
Arbaugh & Rau, 2007; Chen, Jones, & Moreland, 2013).
According to these findings, the effectiveness of online learning may decrease at higher levels, as course material becomes
more difficult conceptually (Chen et al., 2013). Moreover, students enrolled in more advanced courses express a greater need
for face-to-face interaction than those enrolled in a principles
course (Chen et al., 2013). We therefore expected that students
in an introductory course would respond more positively to
online learning than would those in upper level courses:
Course duration is another factor expected to affect perceptions of online learning. Postsecondary students in faceto-face courses generally prefer nontaxing workloads (see
Sperber, 2005). It is safe to conclude that students in online
courses do, too. To the extent that an online course is
administered over a shorter period of time, the role
demands on any given day would be greater than those in a
course whose contents are distributed over a longer period.
We therefore expected online students to respond less positively to highly compressed courses:
H4: Students enrolled in online courses of longer duration
would have more favorable perceptions of their learning and more positive experiences with various aspects
of the course than would students enrolled in online
courses of shorter duration.
METHOD
Sample
The sample consisted of undergraduate students enrolled
in 13 completely asynchronous online courses in the
management department of the AACSB-accredited business school of a medium-sized private nonsectarian
148
D. R. COMER ET AL.
university in the Northeast United States. These courses
were offered over condensed semesters during summer
and winter sessions, between 2009 and 2011. We collected data from four sections of an introduction to management course and from nine sections of advanced
courses: four sections of operations management, three
sections of purchasing and supply management, and two
sections of recruitment and selection. The operations
management course is highly quantitative, and the other
courses are qualitative. Eight classes lasted three weeks
and five lasted five weeks (see Table 1).
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Measures
Students anonymously completed two surveys, one before
starting their course and the other after completing the
course. In order to encourage students to respond to the survey, and following Chalmers’s (2009) advice to award students course credit for every piece of work in an online
class, we gave students a small portion of course credit for
completing each survey. The response rate was virtually
100% across samples. We had to exclude the responses of a
tiny number of students, who either failed to answer several
questions or whose answers we flagged as nonusable. Additionally, because we were looking at students’ readiness for
and perceptions of online learning, we considered responses
only from those students who were taking their first online
course.
A 50-item precourse survey assessed students’ readiness
for online learning. Each item in the survey instrument was
coded on a 4-point Likert-type scale, ranging from 1
(strongly agree) to 4 (strongly disagree). Course materials
were not available to students until the survey deadline had
passed. Thus, responses on this precourse scale reflect
students’ self-perceptions before they had completed any
coursework. The postcourse survey, administered at the end
of each course, tapped students’ perceptions of their learning and of various aspects of the course. Students were
instructed that their final course grade would not be posted
until they completed the survey.
We derived several of the items for the presurvey and
postsurvey from four extant scales used in studies of
online or hybrid learning (Fisher et al., 2001; M. J. Jackson & Helms, 2008; Kizlik, 2005; Robinson & Hullinger, 2008). Additionally, we each developed our own
items, based on our collective experiences as to appropriate practices and principles of online course design.
Each of us independently came up with items, and then
had multiple discussions and iterations to agree on the
final list of items that constituted the scales for the two
surveys.
Because we collected self-reported data, we adopted
several well-accepted ex ante approaches during survey
design and data collection in order to minimize the possibility of common method variance (Podsakoff, MacKenzie,
Lee, & Podsakoff, 2003). We sampled multiple courses and
multiple sections within these courses, over different
semesters and across three years. In addition, we ordered
the questions in both surveys to separate like items, thereby
reducing the likelihood of consistency motive and theoryin-use biases in the students’ responses (Podsakoff et al.,
2003). We also used the ex post approach of analyzing the
data with Harman’s single-factor test. For each survey, the
single-factor solution extracted only 17% of the variance,
and the final multifactor solution explained significantly
more of the variance. We therefore believe that common
method variance does not threaten the analysis and interpretation of our data. The analyses that follow are based on 275
usable responses for the precourse survey and 248 for the
postcourse survey. We have fewer completed postcourse
surveys because some students withdrew from their respective courses before taking the postcourse survey (see
Table 1).
RESULTS
Factor Analysis of Scales
Items of both the precourse and postcourse surveys were
factor analyzed with principal component analysis
TABLE 1
Percentage of Sample by Course Dimension
Precourse sample (n D 275)
Course dimension
Type
Level
Duration
Quantitative
Qualitative
Introductory
Advanced
Three weeks
Five weeks
Postcourse sample (n D 248)
n
%
n
%
85
190
79
196
166
109
31
69
29
71
61
39
76
172
74
174
152
96
31
69
30
70
61
39
The postcourse sample is smaller because some students withdrew from their course before it was administered.
STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
149
TABLE 2
Factor Analysis of Precourse Scale
Factors
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Capability Self-discipline Active learning
I know how and where to search for information.
I am able to stay focused on a complex problem until I can solve it.
I feel at ease when working with computers, a variety of software applications, and the
Internet.
I can easily follow written instructions.
I possess sufficient computer and software knowledge (or have ready access to adequate
support resources) to complete an on-line course.
I recognize that technical problems are likely to occur on occasion and feel that I can work
through such problems successfully.
I take responsibility for my own learning.
I am the kind of person who is curious about many things.
Whenever I have a question, I tend to try to figure out the answer for myself before asking
someone else for help.
I often figure out new ways to solve problems.
I can determine what I need to do in a particular situation and then come up with a way of
doing it.
I am methodical whenever I have to solve a problem.
I am proud of my writing skills.
I am an organized person.
I am good at budgeting my time.
I manage my time well.
I often make myself a schedule or a list of the things I need to do.
I schedule times during a typical day/week to do my schoolwork.
I prioritize my work.
People who know me consider me responsible.
I have a lot of self-discipline.
I often challenge myself by going beyond course requirements.
Once I have a set of goals or objectives, I can figure out what I need to do to reach them.
My peers regard me as a self-starter.
It is interesting to me to find out what my classmates think about an issue.
I like classes in which students contribute to one another’s learning.
I enjoy helping others learn.
Applying course concepts to my own experiences helps me learn.
I use feedback about one course requirement to improve my performance on a future course
requirement.
I enjoy expressing my opinions to others.
I am able to translate course requirements into objectives that matter to me.
I learn course material well when I can apply it to real situations.
I expect this on-line course to be less demanding than a traditional course held in a classroom
I tend to depend on others to remind me about what I need to do.
I would rather ask my instructor a question about course requirements than have to look up the
information myself on the course syllabus.
There is not much I can learn from my classmates.
I become frustrated easily.
I fulfill only the minimum requirements of a course.
I become overwhelmed when I have many deadlines coming up at the same time.
I am an underachiever.
extraction and varimax rotation. We used the following criteria to determine the factors:
1. The total variance explained with the multiple factors
was significantly higher than the variance explained
with Harman’s single-factor solution.
2. A minimum of three items loaded on each factor.
Overall learning
orientation
.657
.610
.588
.570
.535
.528
.469
.467
.465
.463
.440
.438
.435
.739
.713
.676
.602
.598
.564
.552
.545
.484
.469
.405
.735
.724
.639
.602
.520
.496
.483
.469
.599
.568
.526
.468
.466
.451
.410
.401
3. Factor loadings for each of the items for a factor were
at least .4 (which is higher than the normally accepted
level of .3; Hair, Tatham, Anderson, & Black, 1998).
In addition, the factor loadings for a specific item
were higher on that factor than on any other factor.
4. The Cronbach’s Alpha reliability coefficient for each
factor was at least .5 (Hair et al., 1998).
150
D. R. COMER ET AL.
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5. The discriminant validity of the factors was less than
.85, indicating that the factors were distinct from one
another (Campbell, 1960; Campbell & Fiske, 1959).
Precourse survey. Following these criteria, 40 of the
50 items in the precourse survey were retained (see Table 2).
More than 35% of the variance was explained by these four
factors: capability, students’ self-sufficiency and ability to
do what is necessary in an online course (e.g., write and
solve problems for themselves); self-discipline, students’
initiative and responsibility and their ability to manage their
time and their activities; active learning, students’ tendency
to engage fully with classmates and coursework; and overall
learning orientation, students’ propensity to embrace the
role of learners. Respondents’ perceptions of their greater
readiness for learning online are associated with lower
scores on the first three of the four factors and higher scores
on the fourth factor. As Table 3 illustrates, the students in
the sample generally perceived themselves as being suited
for online learning. Although there are significant Pearson
correlations between some of the factors, none of the discriminant validity scores approaches the threshold value of
.85, with the highest score of .687 between capability and
active learning. We analyzed the effect size parameters for
the precourse survey factors using the three indices based on
standardized mean differences (Cohen’s d, Hedges’s λ, and
Glass’s D). All three indices show a small effects size for
the factors. For instance, Cohen’s d ranges from less than
.1–.26 (Cohen, 1988). The factor score is the mean of all the
items loaded on that factor; all subsequent analyses are
based on the factor scores.
Postcourse survey. Table 4 displays the items in the
postcourse survey and the results of the factor analysis of
responses to this survey. The analysis extracted four distinct
factors, which together explain 43% of the variance:
Students’ perceptions of their overall learning from the
course, the value of course discussions and course materials,
and the course workload. As expected, the reverse-coded
items have negative factor loads; the scores on these items
are reversed for the reliability, correlation, and discriminant
analyses. Low scores on the first three factors—learning,
course discussions, and course materials—indicate a
respondent’s favorable perceptions of learning. Low scores
on course workload reflect a respondent’s perception that
the course is demanding. The mean score for all four factors
is around 2 (see Table 5), implying that students generally
agree with the statements. In addition, the standard deviation
values lie between .499 and .595, revealing a reasonably
consistent set of responses within the sample. Similar to the
precourse survey factors, the factors in the postcourse survey
show some significant inter-factor correlations. However,
the discriminant validity scores are low, with the highest
score of .778 below the .85 threshold value. The standardized mean differences indices show a small to medium
effects size for the postcourse survey factors. For instance,
Cohen’s d has a range of .03 to .46 (Cohen, 1988). Although
some of the effects sizes were small, the testing of the
hypotheses as discussed below shows that results are mixed,
with support for some of the hypotheses.
Testing of Hypotheses
None of the correlations between the four precourse scale
factors and the four postcourse scale factors are significant
(see Table 6). In short, we did not find support for H1. We
tested the remaining hypotheses using a one-way analysis
of variance (ANOVA) because our data involve categorical
(two-level) variables. Table 7 shows the relationship
between students’ perceptions of various aspects of online
learning and course type, course level, and course duration.
The scores of students in the qualitative courses are significantly lower than those of students in the quantitative
course for both learning and course discussions. The scores
in the qualitative courses are lower for course workload and
higher for course materials, at a nonsignificant level. Based
on these results, we found partial support for H2. Students
in the introductory classes have significantly more favorable views of learning, course discussions, and course
materials. Those students in the advanced classes are more
TABLE 3
Descriptive Statistics, Correlations, and Discriminant Validity Coefficients of Precourse Factors
M
SD
Coefficient of variation
Cronbach’s a
1. Capability
2. Self-discipline
1.6943
1.8059
0.3465
0.4318
.2045
.2391
.821
.840
3. Active learning
1.7783
0.4002
.2250
.774
4. Overall learning orientation
3.2255
0.3724
.1155
.612
a
Discriminant validity coefficient should be less than .85.
p < .05. **p < .01.
*
1
2
3
.450**
.652a
.437**
.687a
–.146*
.290a
.354**
.544a
–.107
.208a
–.097
.204a
STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
151
TABLE 4
Factor Loadings of Postcourse Scale
Outcome factors
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Learning
I probably learned less in this on-line class than I would have learned in a face-toface class.R
I felt more isolated from my instructor than I do in face-to-face courses.R
I learned a lot from this course.
After taking this course, my interest in this subject matter has increased.
After taking this on-line course, I would like to take other on-line courses.
Completing this course has increased my confidence in my writing abilities.
Completing this course has enhanced my ability to organize my work.
Taking this course has made me recognize that I can learn on my own.
I found it more difficult to relate to the other students in this course than in the
face-to-face courses I’ve taken.R
Reading my classmates’ comments on the Discussion Boards helped me
understand and apply the course concepts.
Responding to my classmates’ comments on the Discussion Boards helped me
understand and apply the course concepts.
Posting my original comments on the Discussion Boards helped me understand
and apply the course concepts.
I was more comfortable participating in discussions in this course than I usually
am in face-to-face courses.
Taking this course has made me recognize that I can learn from other students.
Taking this course has taught me that I prefer expressing my opinions in writing
rather than orally.
It was appropriate to assess students’ performance in this class on the basis of their
comments on the Discussion Boards.
The audio slideshows helped me learn the course materials.
I relied heavily on the audio slideshows to learn the materials in this course.
The videos helped me understand and apply the course concepts.
I relied heavily on the videos to learn the materials in this course.
This course has been one of the more demanding courses I have taken as a college
student.
This course required more time than I had expected.
I worked hard to keep up with the course schedule and deadlines.
At the start of the course, it took me a lot of time to understand the course delivery,
structure, and requirements.
Course discussions
Course materials
Course workload
–.736
–.604
.590
.578
.571
.571
.551
.521
–.479
.802
.767
.681
.596
.553
.536
.472
.778
.771
.634
.583
.705
.693
.477
.476
R D reverse coded item.
likely, at a marginally significant level, to perceive the
course workload as more demanding. We thus found support for H3. In contrast, the results indicate that course
duration does not affect the scores significantly on any of
the four factors. We found no support for H4.
DISCUSSION
Many have compared online learning to face-to-face learning. In contrast, we conducted this study with the premise
that online learning can be just as effective and rewarding
TABLE 5
Descriptive Statistics, Correlations, and Discriminant Coefficients of Postcourse Factors
M
SD
Coefficient of variation
Cronbach’s a
1. Learning
2. Course discussions
1.982
1.947
0.501
0.529
.252
.272
.836
.823
3. Course materials
1.947
0.595
.305
.780
4. Course workload
2.179
0.499
.229
.532
a
Discriminant validity coefficient should be less than 0.85.
p < .01.
**
1.
2.
3.
.646**
.778a
.409**
.506a
–.012
–.018a
.406**
.507a
.062
.094a
.045
.069a
152
D. R. COMER ET AL.
TABLE 6
Correlations Between Precourse Scale Factors and Postcourse Scale Factors
Precourse scale factors
Postscale factors
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Learning
Course discussions
Course materials
Course workload
Capability
Self-discipline
Active learning
Overall learning orientation
.048
.104
–.052
–.109
.092
.107
.019
–.067
.112
.096
.015
–.063
–.023
.085
.013
.043
as traditional face-to-face pedagogy, but that its effectiveness may vary with students’ capacity to fulfill the responsibilities of the online learner role. We expected that
attributes of students would affect their ability to fulfill that
role, as would aspects of courses.
The students in our sample seemed to have a fairly uniform and positive disposition to online courses. According
to their responses to our precourse scale, they generally
viewed themselves as ready to assume the role of the online
learner. Specifically, they perceived themselves as self-sufficient, responsible, eager to engage with classmates and
coursework, and oriented to learning. Given the increasing
prevalence of online learning, these results bode well for
colleges and universities. However, we cannot ignore the
possibility of a response bias, due in part to students’
attempts to avoid postdecision dissonance (Vroom & Deci,
1971). Because the students we sampled were taking their
first online course, they might have been experiencing a
combination of excitement and apprehension. If so, in order
to quell their misgivings, they might have tried to “psych
themselves up” by responding very positively to the precourse questions. Furthermore, the precourse data we present come from students who stayed in the course until at
least the first week of the course. Our findings do not
include the responses of students who withdrew between
the time they completed the precourse survey and the
beginning of the course. Indeed, we encouraged students to
contemplate, as they completed the survey, the extent to
which online learning would be appropriate for them. Also,
insofar as students who believe that they will fare well as
online learners may be more likely to register for online
courses (see Perreault, Altman, & Zhao, 2002), it is plausible that readiness for online learning may be higher in our
sample than in the general population of undergraduate students. Indeed, upon administering the 40-item precourse
survey to a group of 72 students in a face-to-face introductory management class, we found that their scores on all
four factors were less indicative of readiness for online
TABLE 7
Differences in Perceptions of Online Learning by Course Type, Level, and Duration
Course type
Factor
Learning
Course discussions
Course materials
Course workload
Qualitative (n D 172)
Quantitative (n D 76)
F
Significance level
1.9167
1.8887
1.9720
2.1642
2.1287
2.0789
1.8953
2.2138
9.759
6.959
0.827
0.518
.002
.009
.364
.472
Course level
Factor
Learning
Course discussions
Course materials
Course workload
Introductory (n D 74)
Advanced (n D 174)
F
Significance level
1.7432
1.7664
1.7846
2.2635
2.0830
2.0238
2.0124
2.1437
26.291
12.84
6.96
3.012
.000
.000
.009
.084
Course duration
Factor
Learning
Course discussions
Course materials
Course workload
Three weeks (n D 152)
Five weeks (n D 96)
F
Significance level
2.0015
1.9539
1.9696
2.1891
1.9502
1.9360
1.9099
2.1641
0.613
0.067
0.536
0.148
.434
.796
.465
.701
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STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
learning than the scores of the 79 students in our sample
who took the same course online. Their self-reported capability, initiative and responsibility, and propensity to
engage actively with classmates and coursework were significantly higher, t D 2.20, p D .0292; t D 3.12, p D .0022;
and t D 3.98, p D .0001, respectively, and their selfreported overall learning orientation was significantly lower
t D 9.70, p D .0001.
We had expected to find a relationship between
students’ readiness to fulfill the responsibilities of the
online learner role and their perceptions of online learning.
That we did not can be attributed, at least in part, to the
fact that students who were not performing well in their
respective courses (or not as well as they would have
liked) withdrew before taking the posttest. It is reasonable
to assume that the slightly more than 10% of students who
withdrew from their online courses would have responded
less positively about their online learning experiences.
Their leaving the course before taking the posttest
restricted the range of responses we obtained on this measure. Future researchers should explore the relationship
between readiness for online learning and perceptions of
online coursework by assessing the latter before the very
end of the semester.
Our other results provide some information about factors
that affect students’ perception of their learning in online
courses. The results show that students in qualitative
courses have more favorable perceptions of learning and
course discussions than their counterparts in quantitative
courses. However, students’ enrollment in quantitative versus qualitative courses does not seem to affect their perceptions of course materials or course workload.
We also found that students in introductory online classes have more favorable perceptions of their learning,
course discussions, and course materials; and students in
advanced online classes perceive the course workload as
more demanding. Upper level courses are objectively more
challenging, covering topics with greater complexity and
intensity. It could also be the case that students’ perceptions
change as they take more advanced coursework. They may
expect to derive more benefits from higher-level courses.
Students may hold the delivery and quality of their
advanced courses to a higher standard than do students in
introductory-level courses; higher expectations are more
difficult to satisfy. Additional studies could address whether
the online learning platform is more suitable for introductory-level courses, and explore ways to meet the needs of
students in upper level online courses.
We expected students in five-week courses to have more
favorable perceptions of their online courses than students
in three-week courses. Both are compressed-format
courses, covering the same material as courses taught over
15 weeks in a regular (fall or spring) semester. However,
based on our extensive experience teaching both five-week
and three-week courses, online and in a classroom, we note
153
a marked difference between them. Whereas the pace of a
five-week course is brisk but manageable, that of a threeweek course borders on exhausting and uncomfortable. Our
findings suggest, nonetheless, that these differences are
more salient to us than to our students, who perceive threeweek courses no less positively than five-week courses. It is
left for additional research to assess whether students’ perceptions of their learning in compressed online courses differ from their perceptions of learning in full-semester
online courses.3
Limitations
Our results are based on a sample of undergraduate business
students at one AACSB-accredited business school in
Northeast United States. Further studies are required to
examine whether the same results hold with other student
populations in different geographic locations. Although the
students in our sample perceived themselves as ready for
online learning, their counterparts at other postsecondary
institutions may be less equipped. Many undergraduate students lack a sense of responsibility for their own learning
and are also deficient in other behaviors and strategies that
contribute to success in meeting the demands of collegelevel coursework—in the traditional or online classroom
(Conley & French, 2014; J. Jackson & Kurlaender, 2014;
Tierney & Sablan, 2014). Additionally, because of our
focus, all of the students in this sample were taking their
first online courses. It is up to future research to examine
whether the perceptions we assessed change when students
take successive online courses.
It bears repeating that we depended on self-reported
measures. As discussed, we took steps to reduce the possibility of common method variance in the two surveys.
However, it may make sense to gauge students’ readiness
for online learning by asking them to have their close
friends or family members complete the survey about them
rather than using the survey as a self-report instrument.
Conclusion
This study contributes to the research regarding online
learning in business education. Our findings indicate that
course content and level (but not course duration) affect
students’ perceptions of their overall learning, course discussions, course materials, and course workload. We look
to future research to explore other curricular components
that may contribute to students’ responses to online learning. Additionally, preliminary results suggest that our 40item precourse survey has potential as a tool to guide students in gauging their readiness for online learning. As
mentioned, the scale scores of 72 students enrolled in a traditional (i.e., nononline) introductory management class
indicate that they were less ready for online learning than
the students in the online classes of the same course.4 We
154
D. R. COMER ET AL.
plan to collect more data to ascertain the suitability of our
readiness-for-online-learning scale as a tool for guidance.
As an increasing number of business schools expand their
online offerings, understanding the factors that enhance
students’ capacity to fulfill the responsibilities of the online
learner role can help academicians and administrators
design and deliver online programs. Meanwhile, giving students information as to how well they may fare as online
learners may reduce disappointment and increase retention
in online courses.
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NOTES
1. In this study, we focused on asynchronous online
courses characterized by a relatively small number of
students, thus facilitating extensive interaction
among students and between students and their
instructor. Therefore, we do not consider massive
open online courses (MOOCs), which have a very
different aim and focus. Nor do we consider synchronous online courses nor hybrid/blended courses.
2. Students’ unrequited expectations likely contribute to
the lower retention rates identified in online versus
traditional classes (Allen & Seaman, 2013; Heyman,
2010; Kember, 1996).
3. We did not have an opportunity to analyze fullsemester online courses in this study because our
institution introduced such courses only recently.
4. We compared the scores of the class of traditional
students to the subset of our online sample in the
same course to minimize the effects of other factors.
It should be noted that their scores were also significantly different on all four factors when compared to
those of the whole online sample.
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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Factors That Affect Students’ Capacity to Fulfill the
Role of Online Learner
Debra R. Comer, Janet A. Lenaghan & Kaushik Sengupta
To cite this article: Debra R. Comer, Janet A. Lenaghan & Kaushik Sengupta (2015) Factors That
Affect Students’ Capacity to Fulfill the Role of Online Learner, Journal of Education for Business,
90:3, 145-155, DOI: 10.1080/08832323.2015.1007906
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JOURNAL OF EDUCATION FOR BUSINESS, 90: 145–155, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2015.1007906
Factors That Affect Students’ Capacity to Fulfill
the Role of Online Learner
Debra R. Comer, Janet A. Lenaghan, and Kaushik Sengupta
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Hofstra University, Hempstead, New York, USA
Because most undergraduate students are digital natives, it is widely believed that they will
succeed in online courses. But factors other than technology also affect students’ ability to
fulfill the role of online learner. Self-reported data from a sample of more than 200
undergraduates across multiple online courses indicate that students generally view
themselves as having attributes that equip them for online learning. Additionally, courselevel factors affect students’ online learning experiences. Specifically, students in qualitative
(vs. quantitative) courses and in introductory (vs. advanced) classes reported more positive
perceptions of their online learning and various aspects of their coursework.
Keywords: distance education, distance learning, online learner role, online learning,
readiness for online learning
Enhancements in technology have led to an explosion in
online learning. Many college and university presidents
view online education as a critical component of their strategic plan (Allen & Seaman, 2013) and commit substantial
resources to meet the demand for online offerings (Young,
2011). According to a recent survey conducted by Babson
College (Allen & Seaman, 2013), whereas only 1.6 million
postsecondary students in the United States were enrolled
in an online course in 2002, just one decade later, the number had increased to 6.7 million, such that 32% of all students enrolled in higher education in the United States in
2012 had taken at least one course online. Results of the
2013 Association to Advance Collegiate Schools of Business (AACSB) Business School Questionnaire likewise
indicate that between 2007 and 2012, the number of accredited schools that offer fully online courses rose 43%, and
more than 25% of the 480 schools that had participated in
every year of the survey had a fully online degree program
(Nelson, 2013). The growth in online courses responds to
students’ requirements for flexible scheduling (Daymont,
Blau, & Campbell, 2011), as well as their need to develop
the skills to perform in the virtual teams that are becoming
Correspondence should be addressed to Debra R. Comer, Hofstra University, Zarb School of Business, Department of Management and Entrepreneurship, Hempstead, NY 11549-1340, USA. E-mail: debra.r.
comer@hofstra.edu
more prevalent in organizations (Arbaugh, 2014). This
expansion in online education led U.S. News & World
Report to add the “Top Online Education Programs” ranking to its “Best of” lists in 2012 (DeSantis, 2012). Likewise,
the Sloan Consortium, expecting growth in online programs
globally, has recently renamed itself the Online Learning
Consortium (Straumsheim, 2014).
Students’ learning in online courses has often been compared with their learning in more traditional, face-to-face
courses. Because many students today are digital natives,
who have used technology their entire lives (Prensky, 2001;
see also Palfrey & Gasser, 2008), it may be taken for
granted that they will embrace the technology associated
with online classes and flourish in such classes. However,
there is more to success in an online course than technological ability. Students favor online learning for its convenience; those whose work or family responsibilities would
make it onerous or impossible to attend classes on campus
especially appreciate the opportunity online programs
afford (Beqiri, Chase, & Bishka, 2010; Bocchi, Eastman, &
Swift, 2004). Yet, just because online learning is available
to an individual does not mean that the individual will excel
as an online learner. In this study, using a sample of undergraduate students enrolled in various online classes offered
by the management department of an AACSB-accredited
school, we investigated attributes that may predispose students to have positive online learning experiences.
146
D. R. COMER ET AL.
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PERCEPTIONS ABOUT THE EFFECTIVENESS OF
ONLINE LEARNING1
Despite the rapid expansion of online programs, unfavorable perceptions persist. According to Allen and Seaman
(2013), chief academic officers in the United States report
that only 30.2% of “their faculty accept the value and
legitimacy of online education” (p. 27); even at institutions that have fully online courses, academic leaders
report that only 38.4% of the faculty members fully
accept online education. In 2010, the University of California offered a $30,000 bonus to create an online course,
but only 70 of thousands of faculty members designed
such a course, due to their skepticism about the efficacy
of online education (Asimov, 2012). Some faculty members express concern that lack of face-to-face interaction
in an online course diminishes student engagement
(Aggarwal, Adlakha, & Mersha, 2006; Barker, 2010;
Zemsky & Massy, 2004). To be sure, online instructors
must avoid a hit-and-run approach in which they merely
post canned lectures and notes on their institution’s course
management system (e.g., Blackboard). When a distance
learning course is poorly planned and executed, students’
satisfaction and learning suffer (Lawrence & Singhania,
2004; Vamosi, Pierce, & Slotkin, 2004). However, a
thoughtful educator can structure and present an online
course in a way that promotes students’ interaction with
their fellow students and instructor and their mastery of
course concepts (Arbaugh & Benbunan-Fich, 2007; Sonner, 1999).
EMPIRICAL RESEARCH ON THE EFFECTIVENESS
OF ONLINE LEARNING
A mounting body of research indicates that students in
online classes achieve the same learning outcomes as their
counterparts in traditional face-to-face classes (Arbaugh,
2000; Redpath, 2012). Online learning can foster interactivity (Han & Hill, 2006; Kuo, Walker, Schroder, & Belland, 2014; Swan, 2002) and collaboration (Arbaugh,
2008, 2010, 2014; Yoo, Kanawattanachai, & Citurs, 2002),
and even promote the participation of students who would
be less likely to contribute to face-to-face discussions (see,
e.g., Comer & Lenaghan, 2013). Hansen (2008) asserted
that participation and a sense of community are stronger in
online courses because students’ interactions are openended and unbounded by time. Because of variability in
the design and delivery of both face-to-face courses and
online courses, simple comparisons between the two types
are misguided and misleading. Instead, it seems that a
given online course will be pedagogically effective to the
extent that the instructor creates a community of learners
by designing the course appropriately; giving students
ample opportunities to connect to the course material, to
their instructor, and to one another; and ensuring that students can understand, apply, and synthesize course material (see Shea et al., 2010; see also Garrison & Arbaugh,
2007).
EMBRACING THE ROLE OF ONLINE LEARNER
Pitting online learning against face-to-face learning has distracted scholars from ascertaining whether online learning
is appropriate for all students. Yet, it has been asserted that
“distance learning is not for everyone” (Crow, Cheek, &
Hartman, 2003, p. 338), and our collective experience
teaching online for numerous semesters suggests that students do vary in their readiness for online learning.
Although some of our students excel in our online courses,
others have trouble completing course requirements. Part of
the problem may be that some members of the latter group
misjudge what online learning entails. Previous studies of
factors that affect students’ performance in online classes
examine familiarity and comfort with e-learning (McVay,
2001; Smith, Murphy, & Mahoney, 2003), computer technology (Bernard, Brauer, Abrami, & Surkes, 2004; Crow
et al., 2003), and dispositional self-management (Hollenbeck, Zinkhan, & French, 2005; Mariola & Manley, 2002;
Salas, Kosarzycki, Burke, Fiore, & Stone, 2002; Smith
et al., 2003). However, they do not consider these attributes
in terms of students’ fulfillment of the responsibilities
inherent in the role of online learner.
A role represents shared normative expectations that
drive and explain behavior (Kahn, Wolfe, Quinn, Snoek, &
Rosenthal, 1964). A given role is defined vis-a-vis its relationship with another role in a role set, such that one person
assumes the principal role while the other assumes a
counter-position (Stryker & Burke, 2000). Counter-role
partners (e.g., supervisor and subordinate or teacher and
student) shape each other’s behavioral expectations. The
performance of any student—whether in a traditional faceto-face class or a wholly asynchronous online class—relies
in large part on the student’s acceptance of key role
responsibilities.
The role responsibilities students must assume when they
take an online class are substantively—and, apparently, for
some students, surprisingly—different from those required of
students in a traditional face-to-face class. Students need to
adjust their behavior, anchored in a traditional model of delivery, to fit the role expectations of the online modality. They
may erroneously assume that online courses require less work
than traditional courses (Crow et al., 2003).2 In reality, online
students have at least as much coursework as students in traditional classes, but they have more leeway as to when and
where they will complete it (Fisher, King, & Tague, 2001).
With their greater autonomy (to paraphrase Stan Lee) comes
greater responsibility. Online students have to work independently and exercise self-discipline to negotiate course
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STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
147
requirements and meet deadlines. They need to be more
active and self-directed learners than passive and teacherdirected followers (Fisher et al., 2001; Knowles, 1990;
McVay, 2001). Students with superior time management
skills and higher self-efficacy are more likely to continue in
an online program (Holder, 2007). Those who routinely procrastinate perform worse in online courses, in part because
they do not participate sufficiently in discussion forums
(Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011).
Indeed, interaction and participation are other key aspects
of the role of online learner (Arbaugh & Benbunan-Fich,
2007; Swan, 2002). Students will succeed to the extent that
they engage in online discussions and contribute to their community of learners (McIsaac & Gunawardena, 1996). Moreover, students’ level of participation in online threaded
discussions is related to their course performance (Hwang &
Francesco, 2010; Krentler & Willis-Flurry, 2005). Given that
coursework in asynchronous online classes relies on textbased communication, students need to write well. They also
need to be able to resolve inevitable technical glitches. Students who can meet the requirements of the online learner role
would be more likely to fare well in an online course. Thus:
H2: Students enrolled in qualitative online courses would
have more favorable perceptions of their learning and
more positive experiences with various aspects of the
course than would students enrolled in quantitative
online courses.
Hypothesis 1 (H1): Students whose personal attributes
enhance their capacity to perform the responsibilities
inherent in the role of online learner would have more
favorable perceptions of their learning in an online
course and more positive perceptions of various
aspects of the course.
H3: Students in an introductory-level course would have
more favorable perceptions of their learning in an
online course and more positive experiences with various aspects of the course than would students in more
advanced courses.
Other Factors That May Affect Fulfillment of the
Responsibilities of the Online Learner Role
In addition to students’ personal attributes, other factors that
may affect their ability to perform the responsibilities of the
online learner role. One factor is the type of course material.
The way course content is disseminated and learned varies by
course (Arbaugh, 2005), and there are “discipline-related differences in online learning outcomes” (Arbaugh & Rau,
2007, p. 67). Although our study sample included only management courses, management is a multidisciplinary field of
study (Arbaugh, 2007). Even in face-to-face classes, instructors of quantitative subjects may be inclined to rely on traditional pedagogical methods to impart facts and formulae to
their students. In face-to-face qualitative courses, on the other
hand, instructors are more apt to use collaborative learning
and to rely upon active learning. Students who enroll in an
online qualitative course likely expect to be participating in
group discussions and otherwise assuming an active part in
their learning. Students may be less likely to anticipate the
extent to which an online quantitative course will require
active learning; thus, they may take such a course even though
they are not ready to take on the role responsibilities of an
online learner. Accordingly, we formulated the following
hypothesis:
By the same token, students may respond differently to a
more advanced course than to an introductory course. An
emerging stream of research suggests that students’ perceptions of their learning and of the effectiveness of online delivery are related not only to course discipline and content, but
also to whether a course is at the introductory or advanced level
(Arbaugh, 2013; Arbaugh, Desai, Rau, & Sridhar, 2010;
Arbaugh & Rau, 2007; Chen, Jones, & Moreland, 2013).
According to these findings, the effectiveness of online learning may decrease at higher levels, as course material becomes
more difficult conceptually (Chen et al., 2013). Moreover, students enrolled in more advanced courses express a greater need
for face-to-face interaction than those enrolled in a principles
course (Chen et al., 2013). We therefore expected that students
in an introductory course would respond more positively to
online learning than would those in upper level courses:
Course duration is another factor expected to affect perceptions of online learning. Postsecondary students in faceto-face courses generally prefer nontaxing workloads (see
Sperber, 2005). It is safe to conclude that students in online
courses do, too. To the extent that an online course is
administered over a shorter period of time, the role
demands on any given day would be greater than those in a
course whose contents are distributed over a longer period.
We therefore expected online students to respond less positively to highly compressed courses:
H4: Students enrolled in online courses of longer duration
would have more favorable perceptions of their learning and more positive experiences with various aspects
of the course than would students enrolled in online
courses of shorter duration.
METHOD
Sample
The sample consisted of undergraduate students enrolled
in 13 completely asynchronous online courses in the
management department of the AACSB-accredited business school of a medium-sized private nonsectarian
148
D. R. COMER ET AL.
university in the Northeast United States. These courses
were offered over condensed semesters during summer
and winter sessions, between 2009 and 2011. We collected data from four sections of an introduction to management course and from nine sections of advanced
courses: four sections of operations management, three
sections of purchasing and supply management, and two
sections of recruitment and selection. The operations
management course is highly quantitative, and the other
courses are qualitative. Eight classes lasted three weeks
and five lasted five weeks (see Table 1).
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Measures
Students anonymously completed two surveys, one before
starting their course and the other after completing the
course. In order to encourage students to respond to the survey, and following Chalmers’s (2009) advice to award students course credit for every piece of work in an online
class, we gave students a small portion of course credit for
completing each survey. The response rate was virtually
100% across samples. We had to exclude the responses of a
tiny number of students, who either failed to answer several
questions or whose answers we flagged as nonusable. Additionally, because we were looking at students’ readiness for
and perceptions of online learning, we considered responses
only from those students who were taking their first online
course.
A 50-item precourse survey assessed students’ readiness
for online learning. Each item in the survey instrument was
coded on a 4-point Likert-type scale, ranging from 1
(strongly agree) to 4 (strongly disagree). Course materials
were not available to students until the survey deadline had
passed. Thus, responses on this precourse scale reflect
students’ self-perceptions before they had completed any
coursework. The postcourse survey, administered at the end
of each course, tapped students’ perceptions of their learning and of various aspects of the course. Students were
instructed that their final course grade would not be posted
until they completed the survey.
We derived several of the items for the presurvey and
postsurvey from four extant scales used in studies of
online or hybrid learning (Fisher et al., 2001; M. J. Jackson & Helms, 2008; Kizlik, 2005; Robinson & Hullinger, 2008). Additionally, we each developed our own
items, based on our collective experiences as to appropriate practices and principles of online course design.
Each of us independently came up with items, and then
had multiple discussions and iterations to agree on the
final list of items that constituted the scales for the two
surveys.
Because we collected self-reported data, we adopted
several well-accepted ex ante approaches during survey
design and data collection in order to minimize the possibility of common method variance (Podsakoff, MacKenzie,
Lee, & Podsakoff, 2003). We sampled multiple courses and
multiple sections within these courses, over different
semesters and across three years. In addition, we ordered
the questions in both surveys to separate like items, thereby
reducing the likelihood of consistency motive and theoryin-use biases in the students’ responses (Podsakoff et al.,
2003). We also used the ex post approach of analyzing the
data with Harman’s single-factor test. For each survey, the
single-factor solution extracted only 17% of the variance,
and the final multifactor solution explained significantly
more of the variance. We therefore believe that common
method variance does not threaten the analysis and interpretation of our data. The analyses that follow are based on 275
usable responses for the precourse survey and 248 for the
postcourse survey. We have fewer completed postcourse
surveys because some students withdrew from their respective courses before taking the postcourse survey (see
Table 1).
RESULTS
Factor Analysis of Scales
Items of both the precourse and postcourse surveys were
factor analyzed with principal component analysis
TABLE 1
Percentage of Sample by Course Dimension
Precourse sample (n D 275)
Course dimension
Type
Level
Duration
Quantitative
Qualitative
Introductory
Advanced
Three weeks
Five weeks
Postcourse sample (n D 248)
n
%
n
%
85
190
79
196
166
109
31
69
29
71
61
39
76
172
74
174
152
96
31
69
30
70
61
39
The postcourse sample is smaller because some students withdrew from their course before it was administered.
STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
149
TABLE 2
Factor Analysis of Precourse Scale
Factors
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Capability Self-discipline Active learning
I know how and where to search for information.
I am able to stay focused on a complex problem until I can solve it.
I feel at ease when working with computers, a variety of software applications, and the
Internet.
I can easily follow written instructions.
I possess sufficient computer and software knowledge (or have ready access to adequate
support resources) to complete an on-line course.
I recognize that technical problems are likely to occur on occasion and feel that I can work
through such problems successfully.
I take responsibility for my own learning.
I am the kind of person who is curious about many things.
Whenever I have a question, I tend to try to figure out the answer for myself before asking
someone else for help.
I often figure out new ways to solve problems.
I can determine what I need to do in a particular situation and then come up with a way of
doing it.
I am methodical whenever I have to solve a problem.
I am proud of my writing skills.
I am an organized person.
I am good at budgeting my time.
I manage my time well.
I often make myself a schedule or a list of the things I need to do.
I schedule times during a typical day/week to do my schoolwork.
I prioritize my work.
People who know me consider me responsible.
I have a lot of self-discipline.
I often challenge myself by going beyond course requirements.
Once I have a set of goals or objectives, I can figure out what I need to do to reach them.
My peers regard me as a self-starter.
It is interesting to me to find out what my classmates think about an issue.
I like classes in which students contribute to one another’s learning.
I enjoy helping others learn.
Applying course concepts to my own experiences helps me learn.
I use feedback about one course requirement to improve my performance on a future course
requirement.
I enjoy expressing my opinions to others.
I am able to translate course requirements into objectives that matter to me.
I learn course material well when I can apply it to real situations.
I expect this on-line course to be less demanding than a traditional course held in a classroom
I tend to depend on others to remind me about what I need to do.
I would rather ask my instructor a question about course requirements than have to look up the
information myself on the course syllabus.
There is not much I can learn from my classmates.
I become frustrated easily.
I fulfill only the minimum requirements of a course.
I become overwhelmed when I have many deadlines coming up at the same time.
I am an underachiever.
extraction and varimax rotation. We used the following criteria to determine the factors:
1. The total variance explained with the multiple factors
was significantly higher than the variance explained
with Harman’s single-factor solution.
2. A minimum of three items loaded on each factor.
Overall learning
orientation
.657
.610
.588
.570
.535
.528
.469
.467
.465
.463
.440
.438
.435
.739
.713
.676
.602
.598
.564
.552
.545
.484
.469
.405
.735
.724
.639
.602
.520
.496
.483
.469
.599
.568
.526
.468
.466
.451
.410
.401
3. Factor loadings for each of the items for a factor were
at least .4 (which is higher than the normally accepted
level of .3; Hair, Tatham, Anderson, & Black, 1998).
In addition, the factor loadings for a specific item
were higher on that factor than on any other factor.
4. The Cronbach’s Alpha reliability coefficient for each
factor was at least .5 (Hair et al., 1998).
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D. R. COMER ET AL.
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5. The discriminant validity of the factors was less than
.85, indicating that the factors were distinct from one
another (Campbell, 1960; Campbell & Fiske, 1959).
Precourse survey. Following these criteria, 40 of the
50 items in the precourse survey were retained (see Table 2).
More than 35% of the variance was explained by these four
factors: capability, students’ self-sufficiency and ability to
do what is necessary in an online course (e.g., write and
solve problems for themselves); self-discipline, students’
initiative and responsibility and their ability to manage their
time and their activities; active learning, students’ tendency
to engage fully with classmates and coursework; and overall
learning orientation, students’ propensity to embrace the
role of learners. Respondents’ perceptions of their greater
readiness for learning online are associated with lower
scores on the first three of the four factors and higher scores
on the fourth factor. As Table 3 illustrates, the students in
the sample generally perceived themselves as being suited
for online learning. Although there are significant Pearson
correlations between some of the factors, none of the discriminant validity scores approaches the threshold value of
.85, with the highest score of .687 between capability and
active learning. We analyzed the effect size parameters for
the precourse survey factors using the three indices based on
standardized mean differences (Cohen’s d, Hedges’s λ, and
Glass’s D). All three indices show a small effects size for
the factors. For instance, Cohen’s d ranges from less than
.1–.26 (Cohen, 1988). The factor score is the mean of all the
items loaded on that factor; all subsequent analyses are
based on the factor scores.
Postcourse survey. Table 4 displays the items in the
postcourse survey and the results of the factor analysis of
responses to this survey. The analysis extracted four distinct
factors, which together explain 43% of the variance:
Students’ perceptions of their overall learning from the
course, the value of course discussions and course materials,
and the course workload. As expected, the reverse-coded
items have negative factor loads; the scores on these items
are reversed for the reliability, correlation, and discriminant
analyses. Low scores on the first three factors—learning,
course discussions, and course materials—indicate a
respondent’s favorable perceptions of learning. Low scores
on course workload reflect a respondent’s perception that
the course is demanding. The mean score for all four factors
is around 2 (see Table 5), implying that students generally
agree with the statements. In addition, the standard deviation
values lie between .499 and .595, revealing a reasonably
consistent set of responses within the sample. Similar to the
precourse survey factors, the factors in the postcourse survey
show some significant inter-factor correlations. However,
the discriminant validity scores are low, with the highest
score of .778 below the .85 threshold value. The standardized mean differences indices show a small to medium
effects size for the postcourse survey factors. For instance,
Cohen’s d has a range of .03 to .46 (Cohen, 1988). Although
some of the effects sizes were small, the testing of the
hypotheses as discussed below shows that results are mixed,
with support for some of the hypotheses.
Testing of Hypotheses
None of the correlations between the four precourse scale
factors and the four postcourse scale factors are significant
(see Table 6). In short, we did not find support for H1. We
tested the remaining hypotheses using a one-way analysis
of variance (ANOVA) because our data involve categorical
(two-level) variables. Table 7 shows the relationship
between students’ perceptions of various aspects of online
learning and course type, course level, and course duration.
The scores of students in the qualitative courses are significantly lower than those of students in the quantitative
course for both learning and course discussions. The scores
in the qualitative courses are lower for course workload and
higher for course materials, at a nonsignificant level. Based
on these results, we found partial support for H2. Students
in the introductory classes have significantly more favorable views of learning, course discussions, and course
materials. Those students in the advanced classes are more
TABLE 3
Descriptive Statistics, Correlations, and Discriminant Validity Coefficients of Precourse Factors
M
SD
Coefficient of variation
Cronbach’s a
1. Capability
2. Self-discipline
1.6943
1.8059
0.3465
0.4318
.2045
.2391
.821
.840
3. Active learning
1.7783
0.4002
.2250
.774
4. Overall learning orientation
3.2255
0.3724
.1155
.612
a
Discriminant validity coefficient should be less than .85.
p < .05. **p < .01.
*
1
2
3
.450**
.652a
.437**
.687a
–.146*
.290a
.354**
.544a
–.107
.208a
–.097
.204a
STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
151
TABLE 4
Factor Loadings of Postcourse Scale
Outcome factors
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Learning
I probably learned less in this on-line class than I would have learned in a face-toface class.R
I felt more isolated from my instructor than I do in face-to-face courses.R
I learned a lot from this course.
After taking this course, my interest in this subject matter has increased.
After taking this on-line course, I would like to take other on-line courses.
Completing this course has increased my confidence in my writing abilities.
Completing this course has enhanced my ability to organize my work.
Taking this course has made me recognize that I can learn on my own.
I found it more difficult to relate to the other students in this course than in the
face-to-face courses I’ve taken.R
Reading my classmates’ comments on the Discussion Boards helped me
understand and apply the course concepts.
Responding to my classmates’ comments on the Discussion Boards helped me
understand and apply the course concepts.
Posting my original comments on the Discussion Boards helped me understand
and apply the course concepts.
I was more comfortable participating in discussions in this course than I usually
am in face-to-face courses.
Taking this course has made me recognize that I can learn from other students.
Taking this course has taught me that I prefer expressing my opinions in writing
rather than orally.
It was appropriate to assess students’ performance in this class on the basis of their
comments on the Discussion Boards.
The audio slideshows helped me learn the course materials.
I relied heavily on the audio slideshows to learn the materials in this course.
The videos helped me understand and apply the course concepts.
I relied heavily on the videos to learn the materials in this course.
This course has been one of the more demanding courses I have taken as a college
student.
This course required more time than I had expected.
I worked hard to keep up with the course schedule and deadlines.
At the start of the course, it took me a lot of time to understand the course delivery,
structure, and requirements.
Course discussions
Course materials
Course workload
–.736
–.604
.590
.578
.571
.571
.551
.521
–.479
.802
.767
.681
.596
.553
.536
.472
.778
.771
.634
.583
.705
.693
.477
.476
R D reverse coded item.
likely, at a marginally significant level, to perceive the
course workload as more demanding. We thus found support for H3. In contrast, the results indicate that course
duration does not affect the scores significantly on any of
the four factors. We found no support for H4.
DISCUSSION
Many have compared online learning to face-to-face learning. In contrast, we conducted this study with the premise
that online learning can be just as effective and rewarding
TABLE 5
Descriptive Statistics, Correlations, and Discriminant Coefficients of Postcourse Factors
M
SD
Coefficient of variation
Cronbach’s a
1. Learning
2. Course discussions
1.982
1.947
0.501
0.529
.252
.272
.836
.823
3. Course materials
1.947
0.595
.305
.780
4. Course workload
2.179
0.499
.229
.532
a
Discriminant validity coefficient should be less than 0.85.
p < .01.
**
1.
2.
3.
.646**
.778a
.409**
.506a
–.012
–.018a
.406**
.507a
.062
.094a
.045
.069a
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D. R. COMER ET AL.
TABLE 6
Correlations Between Precourse Scale Factors and Postcourse Scale Factors
Precourse scale factors
Postscale factors
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Learning
Course discussions
Course materials
Course workload
Capability
Self-discipline
Active learning
Overall learning orientation
.048
.104
–.052
–.109
.092
.107
.019
–.067
.112
.096
.015
–.063
–.023
.085
.013
.043
as traditional face-to-face pedagogy, but that its effectiveness may vary with students’ capacity to fulfill the responsibilities of the online learner role. We expected that
attributes of students would affect their ability to fulfill that
role, as would aspects of courses.
The students in our sample seemed to have a fairly uniform and positive disposition to online courses. According
to their responses to our precourse scale, they generally
viewed themselves as ready to assume the role of the online
learner. Specifically, they perceived themselves as self-sufficient, responsible, eager to engage with classmates and
coursework, and oriented to learning. Given the increasing
prevalence of online learning, these results bode well for
colleges and universities. However, we cannot ignore the
possibility of a response bias, due in part to students’
attempts to avoid postdecision dissonance (Vroom & Deci,
1971). Because the students we sampled were taking their
first online course, they might have been experiencing a
combination of excitement and apprehension. If so, in order
to quell their misgivings, they might have tried to “psych
themselves up” by responding very positively to the precourse questions. Furthermore, the precourse data we present come from students who stayed in the course until at
least the first week of the course. Our findings do not
include the responses of students who withdrew between
the time they completed the precourse survey and the
beginning of the course. Indeed, we encouraged students to
contemplate, as they completed the survey, the extent to
which online learning would be appropriate for them. Also,
insofar as students who believe that they will fare well as
online learners may be more likely to register for online
courses (see Perreault, Altman, & Zhao, 2002), it is plausible that readiness for online learning may be higher in our
sample than in the general population of undergraduate students. Indeed, upon administering the 40-item precourse
survey to a group of 72 students in a face-to-face introductory management class, we found that their scores on all
four factors were less indicative of readiness for online
TABLE 7
Differences in Perceptions of Online Learning by Course Type, Level, and Duration
Course type
Factor
Learning
Course discussions
Course materials
Course workload
Qualitative (n D 172)
Quantitative (n D 76)
F
Significance level
1.9167
1.8887
1.9720
2.1642
2.1287
2.0789
1.8953
2.2138
9.759
6.959
0.827
0.518
.002
.009
.364
.472
Course level
Factor
Learning
Course discussions
Course materials
Course workload
Introductory (n D 74)
Advanced (n D 174)
F
Significance level
1.7432
1.7664
1.7846
2.2635
2.0830
2.0238
2.0124
2.1437
26.291
12.84
6.96
3.012
.000
.000
.009
.084
Course duration
Factor
Learning
Course discussions
Course materials
Course workload
Three weeks (n D 152)
Five weeks (n D 96)
F
Significance level
2.0015
1.9539
1.9696
2.1891
1.9502
1.9360
1.9099
2.1641
0.613
0.067
0.536
0.148
.434
.796
.465
.701
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STUDENTS’ CAPACITY TO FULFILL THE ROLE OF ONLINE LEARNER
learning than the scores of the 79 students in our sample
who took the same course online. Their self-reported capability, initiative and responsibility, and propensity to
engage actively with classmates and coursework were significantly higher, t D 2.20, p D .0292; t D 3.12, p D .0022;
and t D 3.98, p D .0001, respectively, and their selfreported overall learning orientation was significantly lower
t D 9.70, p D .0001.
We had expected to find a relationship between
students’ readiness to fulfill the responsibilities of the
online learner role and their perceptions of online learning.
That we did not can be attributed, at least in part, to the
fact that students who were not performing well in their
respective courses (or not as well as they would have
liked) withdrew before taking the posttest. It is reasonable
to assume that the slightly more than 10% of students who
withdrew from their online courses would have responded
less positively about their online learning experiences.
Their leaving the course before taking the posttest
restricted the range of responses we obtained on this measure. Future researchers should explore the relationship
between readiness for online learning and perceptions of
online coursework by assessing the latter before the very
end of the semester.
Our other results provide some information about factors
that affect students’ perception of their learning in online
courses. The results show that students in qualitative
courses have more favorable perceptions of learning and
course discussions than their counterparts in quantitative
courses. However, students’ enrollment in quantitative versus qualitative courses does not seem to affect their perceptions of course materials or course workload.
We also found that students in introductory online classes have more favorable perceptions of their learning,
course discussions, and course materials; and students in
advanced online classes perceive the course workload as
more demanding. Upper level courses are objectively more
challenging, covering topics with greater complexity and
intensity. It could also be the case that students’ perceptions
change as they take more advanced coursework. They may
expect to derive more benefits from higher-level courses.
Students may hold the delivery and quality of their
advanced courses to a higher standard than do students in
introductory-level courses; higher expectations are more
difficult to satisfy. Additional studies could address whether
the online learning platform is more suitable for introductory-level courses, and explore ways to meet the needs of
students in upper level online courses.
We expected students in five-week courses to have more
favorable perceptions of their online courses than students
in three-week courses. Both are compressed-format
courses, covering the same material as courses taught over
15 weeks in a regular (fall or spring) semester. However,
based on our extensive experience teaching both five-week
and three-week courses, online and in a classroom, we note
153
a marked difference between them. Whereas the pace of a
five-week course is brisk but manageable, that of a threeweek course borders on exhausting and uncomfortable. Our
findings suggest, nonetheless, that these differences are
more salient to us than to our students, who perceive threeweek courses no less positively than five-week courses. It is
left for additional research to assess whether students’ perceptions of their learning in compressed online courses differ from their perceptions of learning in full-semester
online courses.3
Limitations
Our results are based on a sample of undergraduate business
students at one AACSB-accredited business school in
Northeast United States. Further studies are required to
examine whether the same results hold with other student
populations in different geographic locations. Although the
students in our sample perceived themselves as ready for
online learning, their counterparts at other postsecondary
institutions may be less equipped. Many undergraduate students lack a sense of responsibility for their own learning
and are also deficient in other behaviors and strategies that
contribute to success in meeting the demands of collegelevel coursework—in the traditional or online classroom
(Conley & French, 2014; J. Jackson & Kurlaender, 2014;
Tierney & Sablan, 2014). Additionally, because of our
focus, all of the students in this sample were taking their
first online courses. It is up to future research to examine
whether the perceptions we assessed change when students
take successive online courses.
It bears repeating that we depended on self-reported
measures. As discussed, we took steps to reduce the possibility of common method variance in the two surveys.
However, it may make sense to gauge students’ readiness
for online learning by asking them to have their close
friends or family members complete the survey about them
rather than using the survey as a self-report instrument.
Conclusion
This study contributes to the research regarding online
learning in business education. Our findings indicate that
course content and level (but not course duration) affect
students’ perceptions of their overall learning, course discussions, course materials, and course workload. We look
to future research to explore other curricular components
that may contribute to students’ responses to online learning. Additionally, preliminary results suggest that our 40item precourse survey has potential as a tool to guide students in gauging their readiness for online learning. As
mentioned, the scale scores of 72 students enrolled in a traditional (i.e., nononline) introductory management class
indicate that they were less ready for online learning than
the students in the online classes of the same course.4 We
154
D. R. COMER ET AL.
plan to collect more data to ascertain the suitability of our
readiness-for-online-learning scale as a tool for guidance.
As an increasing number of business schools expand their
online offerings, understanding the factors that enhance
students’ capacity to fulfill the responsibilities of the online
learner role can help academicians and administrators
design and deliver online programs. Meanwhile, giving students information as to how well they may fare as online
learners may reduce disappointment and increase retention
in online courses.
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NOTES
1. In this study, we focused on asynchronous online
courses characterized by a relatively small number of
students, thus facilitating extensive interaction
among students and between students and their
instructor. Therefore, we do not consider massive
open online courses (MOOCs), which have a very
different aim and focus. Nor do we consider synchronous online courses nor hybrid/blended courses.
2. Students’ unrequited expectations likely contribute to
the lower retention rates identified in online versus
traditional classes (Allen & Seaman, 2013; Heyman,
2010; Kember, 1996).
3. We did not have an opportunity to analyze fullsemester online courses in this study because our
institution introduced such courses only recently.
4. We compared the scores of the class of traditional
students to the subset of our online sample in the
same course to minimize the effects of other factors.
It should be noted that their scores were also significantly different on all four factors when compared to
those of the whole online sample.
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