Developing and Testing a Model to Understand Relationships
between e-Learning Outcomes and Human Factors
Sean B. Eom
1
and Nicholas J. Ashill
2
1
Department of Accounting, Southeast Missouri State University, 1 University Plaza, Cape Girardeau, U.S.A.
2
Department of Marketing, American University of Sharjah, Sharjah, U.A.E.
Keywords: e-Learning Outcomes, Human Factors, Student Satisfaction, Interaction.
Abstract: This study applies partial least squares (PLS) to examine the effects of interactions and instructor feedback
and facilitation on the students' satisfaction and their perceived learning outcomes in the context of
university online courses. Independent variables included in the study are course structure, self-motivation,
learning style, and interaction. A total of 397 valid unduplicated responses from students who have
completed at least one online course at a university in the Midwest U.S. are used to examine the structural
model. Four of the five antecedent constructs hypothesized to directly affect student/instructor interaction
are significant. This research makes a critical contribution in e-learning empirical research by identifying
two critical human factors that make e-learning a superior mode of instruction.
1 INTRODUCTION
According to a comprehensive online and blended
learning literature review (Arbaugh et al., 2009), e-
learning empirical researchers have accumulated
important findings in regard to potential predictors
of e-learning outcomes, control variables, and
criterion variables. This review identified two most
common research streams: first, a comparison of
learning outcomes between face-to-face and e-
learning course delivery modes; second, research
which examined potential predictors of e-learning
outcomes. Previous empirical studies in this area of
prediction of e-learning outcomes can be broadly
classified into (1) conceptual frameworks that
identify factors that affect e-learning outcomes and
learner satisfaction, (2) empirical studies that
examine a subset of factors on learning outcomes
(e.g., effects of e-learner characteristics such as
gender, age and e-learning experience), and (3)
empirical studies examining factors and their effects
on e-learning outcomes and (4) empirical studies
examining factors that make e-learning a superior
mode of instruction. Many research models that
identify the predictors of e-learning outcomes are
built on the conceptual frameworks of Piccoli et al.,
(2001) and Peltier et al., (2003). The former
identifies human and design factors as antecedents
of learning effectiveness. Human factors are
concerned with students and instructors, while
design factors characterize such variables as
technology, learner control, course content, and
interaction. The conceptual framework of online
education proposed by Peltier et al., (2003) consists
of instructor support and mentoring, instructor-to-
student interaction, student-to-student interaction,
course structure, course content, and information
delivery technology.
Empirical studies however, report conflicting
findings. Eom et al., (2006) for example, examined
the determinants of students' satisfaction and their
perceived learning outcomes in the context of
university online courses. Independent variables
included in this study were course structure,
instructor feedback, self-motivation, learning style,
interaction, and instructor facilitation as potential
determinants of online learning. The results
indicated that all of the antecedent variables
significantly affect students' satisfaction. Of the six
antecedent variables hypothesized to affect the
perceived learning outcomes, only instructor
feedback and learning style were significant.
Although this study represents an important
milestone in e-learning empirical research by
fundamentally shifting the focus of e-learning
empirical studies from simply identifying the
predictors of e-learning outcomes to identifying a
361
B. Eom S. and J. Ashill N..
Developing and Testing a Model to Understand Relationships between e-Learning Outcomes and Human Factors.
DOI: 10.5220/0004437803610370
In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 361-370
ISBN: 978-989-8565-60-0
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
subset of critical success factors that makes learning
outcomes surpass those provided in classroom-based
settings, some of the study’s findings run contrary to
other studies. For example, Eom et al. (2006) found
no support for a positive relationship between
interaction and perceived learning outcomes, a
finding that runs contrary to LaPointe and
Gunawardena (2004).
In the current study, we advance current research
by examining relationships between e-learning
outcomes and human factors, especially human
interactions, in university online education using e-
learning systems. Specifically, our conceptual model
examines the human interaction construct and we
place this construct as a mediating variable between
learning outcomes and other three constructs (course
structure, motivation, and learning styles). Using the
extant literature, we begin by introducing and
discussing the research model illustrating factors
affecting e-learning systems outcomes and e-learner
satisfaction. We follow this with a description of the
cross-sectional survey that was used to collect data
and the results from structural equation modeling
(SEM) analysis using PLS-Graph. PLS-based SEM
yields robust results despite that it does not have
measurement, distributional, or sample size
assumptions. In the final section, we summarize
and conclude with the implications of the results for
e-learning.
2 RESEARCH MODEL
Figure 1 summarizes research questions we attempt
to answer. There are three antecedents (course
structure, students’ motivation, and students’
learning styles) of interactions. The impact of three
constructs on learning outcomes is mediated by
interaction.
2.1 Antecedents of Interaction
e-Learning systems aim to maximize learning
outcomes using learning management systems. In
doing so, understanding widely accepted learning
theories is prerequisite to apply these learning
management systems and technologies, because it
defines different roles the instructor and students
have to play in the learning process and the roles of
dynamic interactions among human subsystems
(students, the instructor), technology, and contents in
the learning process. The most widespread learning
paradigms are the behaviorist paradigm of learning
(behaviorism), the constructive paradigm of learning
(constructivism) and the cognitive paradigm of
learning (an extension of constructivism). With
increasing adoption of e-learning, constructivism has
become the dominant learning theory.
Constructivism assumes that individuals learn better
when they control the pace of learning. Therefore,
the instructor supports learner-centered active
learning. Under the cooperative model of learning
(collaboratism), students learn as individual students
and verify, solidify, construct and improve shared
understanding of their mental models. It is necessary
for students to interact with other students and the
instructor in the form of active forum discussions,
private e-mails, teleconferencing and group project
completion. The interaction and active participation
enable students to construct and share the new
knowledge. In this learning process, student
involvement is critical to learning and the instructor
becomes a discussion leader. The socioculturism
model necessitates empowering students with
freedom and responsibilities since learning is
individualistic (Leidner and Jarvenpaa, 1995).
Moreover, e-learning places greater emphasis on the
constructive paradigm of learning (constructivism)
and the cognitive paradigm of learning (an extension
of constructivism). Consequently, the role of
interaction among students and between students
and the instructor in the e-learning process has
become critical.
The design dimension in Piccoli et al., (2001)
includes a wide range of constructs that affect
effectiveness of e-learning systems such as
technology, learner control, learning model, course
contents and structure, and interaction. Among the
many frameworks/taxonomies of interaction
(Northrup, 2002), this research adopts Moore’s
(1989) communication framework which classified
engagement in learning through (a) interaction
between participants and learning materials, (b)
interaction between participants and tutors/experts,
and (c) interactions among participants. These three
forms of interaction in online courses are recognized
as important and critical constructs determining the
performance of web-based course quality.
The community of inquiry model is another
useful framework that explains the roles of
interaction to achieve higher learning outcomes and
learner satisfaction. The community of inquiry
framework emphasizes the creation of an effective
online learning community that enhances and
supports learning and learner satisfaction (Akyol and
Garrison, 2011). The essence of the effective online
learning community is the creation of cognitive
presence, which is a condition/learning environment
ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems
362
Figure 1: Research model.
that facilitates higher-order thinking and deep and
meaningful learning (Garrison and Anderson, 2003).
Cognitive presence in the e-learning process
facilitates e-learners to freely exchange ideas and
information and connect ideas to construct new
knowledge. In doing so, interactions with students
and the instructor becomes a necessary ingredient in
the learning process. The next element of the
community of inquiry model is social presence.
According to Garrison (Garrison, 2009, p.352),
social presence is defined as “the ability of
participants to identify with community (e.g., course
of study), communicate purposefully in a trusting
environment, and develop interpersonal relationships
by way of projecting their individual personalities.”
2.1.1 Course Structure
Course structure is seen as a crucial variable that
affects the success of distance education along
interaction. According to Moore (1991, p.3), the
course structure “expresses the rigidity or flexibility
of the program's educational objectives, teaching
strategies, and evaluation methods” and the course
structure describes “the extent to which an education
program can accommodate or be responsive to each
learner's individual needs.”
Course structure has two structural elements -
course objectives/expectation and course
infrastructure. Course objectives/expectation are
specified in the course syllabus including expected
class participation in the form of online conferencing
systems and group project assignments. These
structural elements affect the interaction level. The
instructor’s efforts to generate interaction include
the online forum activities as part of grading
systems. Student’s attitude and behavior changes
significantly when the instructor assigns forum
activities as a grading component. We theorize that
course material that is organized into logical and
understandable components will lead to the high
levels of interaction between the instructor and
students and between students and students. Thus,
we hypothesize:
H
1
: There will be a positive relationship between
perceptions of course structure and
student/instructor interaction.
2.1.2 Student Self-Motivation
One of the stark contrasts between successful
students is their apparent ability to motivate
themselves, even when they do not have the burning
desire to complete a certain task. On the other hand,
less successful students tend to have difficulty in
calling up self-motivation skills, like goal setting,
verbal reinforcement, self-rewards, and punishment
control techniques (Dembo and Eaton, 2000). The
extant literature suggests that students with strong
motivation will be more successful and tend to learn
the most in web-based courses than those with less
motivation (Frankola, 2001); (LaRose and Whitten,
2000). Students' motivation is a major factor that
affects the attrition and completion rates in the web-
based course and a lack of motivation is also linked
to high dropout rates (Frankola, 2001); (Galusha,
DevelopingandTestingaModeltoUnderstandRelationshipsbetweene-LearningOutcomesandHumanFactors
363
1997). It is conceivable that a high level of student
motivation be positively related to a high level of
interaction with the instructor and students. Thus, we
hypothesize:
H
2
: There will be a positive relationship between
student motivation and student/instructor
interaction.
2.1.3 Students’ Learning Style
We assume that online learning systems may include
less sound or oral components than traditional face-
to-face course delivery systems and that online
learning systems have more proportion of read/write
assignment components, Students with visual
learning styles and read/write learning styles may do
better in online courses than their counterparts in
face-to-face courses. There are some empirical
studies that investigated the direct relationships
between interaction and students’ perceived learning
outcomes and satisfactions in university e-learning
(Eom et al., 2006). But there are few studies that
explore the interaction construct as a mediating
variable that connects three other attributes (learning
styles, motivation, and course structures).
It is conceivable that there is some association
between styles of learning and the level of
interaction. For example, Gardner’s theory of
multiple intelligence categorizes eight different
learning styles (Gardner, 1983). Of these,
interpersonal learners learn better when they work
together, while intrapersonal learners do better in
individual and self-paced projects by working alone.
Research indicates that learning styles can be
incorporated as a key feature for group formation,
which in turn may affect the final results of the tasks
accomplished by them collaboratively (Alfonseca et
al., 2006). This implicitly assumes a positive
association between learning styles and the level of
interaction. Therefore, we hypothesize:
H
3
: There will be a positive relationship between
visual and read/write learning styles and
student/instructor interaction.
2.1.4 Instructor
Some of widely accepted learning models are
objectivism, constructivism, collaborativism,
cognitive information processing, and
socioculturalism (Leidner and Jarvenpaa, 1995).
Distance learning can easily break a major
assumption of objectivism – instructor houses all
necessary knowledge. For this reason, distance
learning systems can utilize many other learning
models such as constructivist, collaboratism, and
socioculturism. Constructivism assumes that
individuals learn better when they control the pace
of learning. Therefore, the instructor supports
learner-centered active learning. Under the model of
collaboratism, student involvement is critical to
learning and the instructor becomes discussion
leader. Socioculturism models necessitate
empowering students with freedom and
responsibilities since learning is individualistic.
e-Learning environments demand a transition of
the roles of students and the instructor. The
instructor’s role is to become a facilitator who
stimulates, guides and challenges their students via
empowering them with freedom and responsibility,
rather than a lecturer who focuses on the delivery of
instruction. (Huynh, 2005). The importance of the
level of encouragement can be found in the model
proposed by Lam (2005). We added the two
questions to assess the roles of the instructor as the
facilitator and stimulator: The instructor was
actively involved in facilitating this course; The
instructor stimulated students to intellectual effort
beyond that required by face-to-face courses.
Therefore, we hypothesize:
H
4
: There will be a positive relationship between
instructor knowledge and facilitation and
student/instructor interaction.
H
7
: There will be a positive relationship between
instructor knowledge and facilitation and
learning outcomes.
2.1.5 Instructor Feedback
Instructor feedback to the learner is defined as
information a learner receives about his or her
learning process and achievement outcomes (Butler
and Winne, 1995) and it is “one of the most
powerful component in the learning process” (Dick
and Carey, 1990, p.165). It intends to improve
student performance via informing students how
well they are doing and via directing students
learning efforts. Instructor feedback in the Web-
based system includes the simplest cognitive
feedback (e.g., exam/assignment with his or her
answer marked wrong), diagnostic feedback (e.g.,
exam/assignment with instructor comments why the
answers are correct or incorrect), prescriptive
feedback (instructor feedback suggesting how the
correct responses can be constructed) via replies to
student e-mails, graded work with comments, online
grade books, and synchronous and asynchronous
commentary.
Instructor feedback to students can improve
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learner affective responses, increase cognitive skills
and knowledge, and activate metacognition.
Metacognition refers to the awareness and control of
cognition through planning, monitoring, and
regulating cognitive activities (Pintrich et al., 1991).
Metacognitive feedback concerning learner progress
directs the learner’s attention to learning outcomes
(Ley, 1999). When metacognition is activated,
students may become self-regulated learners. They
can set specific learning outcomes and monitor the
effectiveness of their learning methods or strategies
(Chen, 2002); (Zimmerman, 1989). Therefore, we
hypothesize:
H
5
: There will be a positive relationship between
instructor feedback and student/instructor
interaction.
H
8
: There will be a positive relationship between
instructor feedback and learning outcomes.
2.2 Consequences of Interaction
Interaction between participants in online courses
has been recognized as the most important construct
of the dimensions determining Web-based course
quality. Hence, many studies have shown that
interaction is highly correlated to the learning
effectiveness of Web-based courses and most
students who reported higher levels of interaction
with content, instructor, and peers reported higher
levels of satisfaction and higher levels of learning.
(Moore, 1989); (Swan, 2001); (Vaverek and
Saunders, 1993).
Interaction with the Instructor: The learner-
instructor interaction involves direct interaction
between instructor and learner, and may be initiated
by either. Interactions include answering questions
about both course content and organization,
providing personal examples of class material,
demonstrating a sense of humor about the course
material, and, importantly, inviting students to seek
feedback (Arbaugh, 2001); (Saltzberg and Polyson,
1995). High levels of learner-instructor interaction
are positively related with levels of satisfaction with
the course and levels of learning (Arbaugh, 2000);
(Swan, 2001). Furthermore, Picciano (1998)
discovered that students perceive learning from
online courses to be related to the amount of
discussion actually taking place in them. When
students actively participate in an intellectual
exchange with fellow students and the instructor,
students verbalize what they are learning in a course
and articulate their current understanding(Chi and
VanLehn, 1991). Therefore, we hypothesize:
H
6
: There will be a positive relationship between
interaction and learning outcomes.
The last hypothesis tests a positive association
between learning outcomes and students’
satisfaction. Depending on how each indicator of
user satisfaction and learning outcomes are
measured, these construct can be reciprocal. In this
study, learning outcomes are measured by perceived
level of learning, perceived quality of the learning
experience in online courses, whereas user
satisfaction is measured by the degree of willingness
by students to take an online course and to
recommend the course taken to other students in the
future. Consequently, learning outcomes precede
user satisfaction. Therefore we hypothesize:
H
9
: There will be a positive relationship between
learning outcomes and user satisfaction.
3 SURVEY INSTRUMENT
The survey instrument was designed after
conducting an extensive literature review and
adapting items from the commonly administered
IDEA (Individual Development & Educational
Assessment) student rating systems developed by
Kansas State University (see Appendix A). In an
effort to survey students using technology-enhanced
e-learning systems, we focused on students enrolled
in Web-based courses with no on campus meetings.
A survey URL and instructions were sent to 1,854
student email addresses that were collected from
student data files associated with every online course
delivered through the online program of a university
in the Midwest of the United States. Three hundred
and ninety seven valid unduplicated responses were
collected from the survey.
4 RESEARCH METHODOLOGY
The hypotheses were tested using a quantitative
survey of satisfaction and learning outcome
perceptions of students who had taken at least one
online course at a large Midwestern university in the
United States. Relationships between variables were
tested using the structural equation modeling (SEM)
tool PLS graph version 3.0, build 1126.
4.1 Measurement Model Estimation
The test of the measurement model included an
estimation of the internal consistency and the
DevelopingandTestingaModeltoUnderstandRelationshipsbetweene-LearningOutcomesandHumanFactors
365
convergent and discriminant validity of the
instrument items.
Table 1: Convergent and discriminant validity of the
model constructs.
loading t-statistic#
Course Structure
(ic=0.88 ave =0.73)
Struc1 0.8346 14.8330
Struc2 0.8850 18.2017
Struc3 0.8324 11.7576
Instructor Feedback
(ic = 0.93 ave = 0.77)
Feed1 0.8722 24.2528
Feed2 0.8286 18.8518
Feed3 0.9045 30.9556
Feed4 0.9035 26.8712
Self-Motivation
(ic = 0.79 ave = 0.66)
Moti1 0.7156 7.1801
Moti2 0.8989 14.2685
Learning Style
(ic = 0.81 ave = 0.67)
Styl1 0.8681 7.8532
Styl2 0.7707 5.4517
Interaction
(ic = 0.76 ave = 0.62)
Intr1 0.9258 17.1161
Intr2 0.6063 6.9865
Instructor Knowledge
& Facilitation
(ic = 0.89 ave = 0.73)
Inst1 0.8396 17.0887
Inst2 0.9026 26.9581
Inst3 0.8125 17.9503
User Satisfaction
(ic = 0.90 ave = 0.76)
Sati1 0.8676 28.3807
Sati2 0.8984 35.8619
Sati3 0.8398 30.6980
Learning Outcomes
(ic = 0.91 ave = 0.78
Outc1 0.8678 22.5078
Outc2 0.8887 33.3322
Outc3 0.8930 35.1271
Note: ‘ic’ is internal consistency measure; ‘ave’ is average
variance extracted.
# All significant p < .05.
The composite reliability of a block of indicators
measuring a construct was assessed with two
measures - the composite reliability measure of
internal consistency and average variance extracted
(AVE). All reliability measures were above the
recommended level of 0.70 (Table 1), thus
indicating adequate internal consistency (Fornell and
Bookstein, 1982); (Nunnally, 1978). The average
variance extracted scores (AVE) were also above the
minimum threshold of 0.5 (Chin, 1998b); (Fornell
and Larcker, 1981) and ranged from 0.62 to 0.78
(see Table 1). When AVE is greater than 0.50, the
variance shared with a construct and its measures is
greater than error. This level was achieved for all of
the model constructs.
Convergent validity is demonstrated when items
load highly (loading >0.50) on their associated
factors. Loadings of 0.5 are considered acceptable if
there are additional indicators in the block for
comparative purposes (Chin, 1998b). Ideally
however, they should be 0.7 or higher. Table 1
shows that with the exception of one item, all
loadings were above 0.7 for the items measuring
each of the eight constructs.
Discriminant validity was firstly assessed by
examining the cross-loadings of the constructs and
the measures. This analysis revealed that the
correlations of each construct with its measures were
higher than the correlations with any other measures.
Second, the square root of the average variance
extracted (AVE) for each construct was compared
with the correlation between the construct and other
constructs in the model (Chin, 1998b); (Fornell and
Larcker, 1981). Table 2 shows that the square root of
each AVE is larger than any correlation among any
pair of constructs thus indicating discriminant
validity.
Table 2: Correlation among construct scores (square root
of AVE in the diagonal).
CS IF SM LS
INT
IKF US LO
CS
.85
IF .72
.87
SM .26 .23
.81
LS .29 .21 .25
.82
INT .44 .59 .38 .26
.79
IKF .68 .80 .25 .26 .55
.85
US .74 .69 .36 .41 .53 .71
.87
LO .56 .49 .35 .44 .45 .55 .78
.88
4.2 Structural Model Results
Consistent with the distribution free, predictive
approach of PLS (Wold, 1985), the structural model
was evaluated using the R
2
for the dependent
ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems
366
constructs, the Stone-Geisser Q-square test (Geisser,
1975); (Stone, 1974) for predictive relevance, and
the size, t-statistics and significance level of the
structural path coefficients. The t-statistics were
estimated using the bootstrap resampling procedure
(100 resamples). The results of the structural model
are summarized in Table 3.
Table 3: Structural (inner) model results.
Dependent variables
Interaction
Learning
Outcomes
User
Satisfaction
Constructs
Instructor
Knowledge &
Facilitation
.151** .384**** -
Instructor
Feedback
.466**** .077 ns -
Course
Structure
-.086 ns - -
Self-
Motivation
.238**** - -
Learning Style .088** - -
Student/Instruc
tor Interaction
- .189*** -
User
Satisfaction
- - -
Learning
Outcomes
- - .526****
P-values: **** 0.001, *** 0.01, **0.05, ns - not significant
Effect on Interaction R
2
=0.44, Learning Outcomes R
2
=0.33,
Effect on User Satisfaction R
2
=0.61.
The results show that the structural model
explains 43.7 percent of the variance in the
student/instructor interaction construct, 33.4 percent
of the variance in the learning outcomes construct
and 61.2 percent of the variance in the user
satisfaction construct. The percentage of variance
explained for these primary dependent variables is
greater than 10 percent implying satisfactory and
substantive value and predictive power of the PLS
model (Falk and Miller, 1992).
As can be seen from the results, four of the five
antecedent constructs hypothesized to directly affect
student/instructor interaction are significant. The
magnitude of the path coefficients however, indicate
that instructor feedback (β = 0.47, t = 5.83) and self-
motivation (β = 0.24, t = 4.90) are stronger
predictors of interaction relative to instructor
knowledge and facilitation (β = 0.15, t = 2.09) and
learning style (β = 0.09, t = 1.93). Course structure
has no significant relationship with
student/instructor interaction (β = -0.09, t = 1.46).
H1 is therefore rejected while support exists for H2-
H5.
Two of the three antecedent constructs
hypothesized to directly affect learning outcomes are
significant - instructor knowledge/facilitation (β =
0.38, t = 5.37) and student/instructor interaction (β =
0.19, t = 3.02). H
6
and H
7
are therefore supported.
Instructor feedback has no significant impact on
learning outcomes (β = 0.08, t = 0.94). H
8
is
therefore rejected. Finally, the hypothesized direct
relationship between learning outcomes and user
satisfaction is significant (β = 0.78, t = 41.74). H
9
is
therefore supported.
Although PLS estimation does not utilize formal
indices to assess overall goodness-of-fit (GoF) such
as GFI, CFI, chi-square values, NNFI and RMSEA,
it can be demonstrated by strong factor loadings,
high R
2
values and substantial and statistically
significant structural paths (Chin, 1998a; 1998b);
(Tenenhaus et al., 2005). Tenenhaus et al., (2005)
have also developed an additional GoF measure for
PLS based on taking the square root of the product
of the variance extracted with all constructs with
multiple indicators and the average R
2
value of the
endogenous constructs. In the currents study the
GoF measure is .577 which indicates very good fit
(Cohen, 1988).
In addition to examining R
2
, the PLS model was
also evaluated by looking at the Q-square for
predictive relevance for the model constructs. Q-
square is a measure of how well the observed values
are reproduced by the model and its parameter
estimates. Q-squares greater than 0 indicate that the
model has predictive relevance, whereas Q-squares
less than 0 suggest that the model lacks predictive
relevance. In the current study, Q-square values are
0.15 for student/instructor interaction, 0.09 for
learning outcomes and 0.42 for user satisfaction.
5 CONCLUSIONS
This study, applying structural equation modeling,
examines the effects of interactions and instructor
feedback and facilitation on students' satisfaction
and their perceived learning outcomes in the context
of university online courses. Independent variables
included in the study are course structure, self-
motivation, learning style, and interaction. A total of
397 valid unduplicated responses from students who
have completed at least one online course at a
university in the Midwest were used to examine the
structural model.
DevelopingandTestingaModeltoUnderstandRelationshipsbetweene-LearningOutcomesandHumanFactors
367
Figure 2: Structural model results.
The results indicate that four of the five
antecedent constructs hypothesized to directly affect
student/instructor interaction are significant. The
magnitude of the path coefficients however, indicate
that instructor feedback and self-motivation are
stronger predictors of interaction relative to
instructor knowledge and facilitation and learning
style. Course structure has no significant relationship
with student/instructor interaction. Two of the three
antecedent constructs hypothesized to directly affect
learning outcomes are significant – instructor
knowledge/facilitation and student/instructor
interaction. Instructor feedback has no significant
impact on learning outcomes. Finally, the
hypothesized direct relationship between learning
outcomes and user satisfaction is significant.
One of the crucial research questions we
attempted to answer was the relationship between
interaction and perceived learning outcomes.
Contrary to previous research (LaPointe and
Gunawardena, 2004), the study of Eom et al., (2006)
found no support for a positive relationship between
interaction and perceived learning outcomes.
However, the new research model (figure 1) in the
current study presents interaction as the key
mediating variable along with instructor feedback
and facilitation. Consequently, interactions with the
instructor and among students are a strong predictor
of student learning outcomes due to the combination
of direct effects of interaction on students learning
outcomes and indirect effects of course structure,
motivation, and learning styles on students learning
outcomes.
Our research attempted to include a mediating
variable (interaction) to connect course structure and
learning outcomes. A possible explanation for the
statistically insignificant relationship between online
course structure and perceived learning outcomes is
that all indicators of course structure may have not
included expected class participation in the form of
online conferencing systems.
Our results indicated that instructor feedback and
self-motivation are stronger predictors of interaction
relative to instructor knowledge and facilitation and
learning style. Therefore, self-motivation is
indirectly affecting students learning outcomes via
the interaction construct.
This research, along with the study of Eom, et al.
(Eom et al., 2006) has made a critical contribution in
e-learning empirical research by identifying two
critical human factors that make e-learning a
superior mode of instruction. In our view, this is a
significant shift of direction in e-learning empirical
research. Our research provides strong empirical
evidence that online education is not a universal
innovation applicable to all types of instructional
situations. Online education can be a superior mode
of instruction if it is targeted to learners with specific
learning styles (visual and read/write learning styles)
(Eom et al., 2006) and students personality
characteristics (Schniederjans and Kim, 2005) and
with timely, helpful instructor feedback of various
types. Proper management of human factors can
change the dynamics of e-learning process to
produce e-learning outcomes surpass those provided
in classroom-based settings. Technology in e-
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learning is just an instructional tool. We conclude
that instructor’s facilitating roles and feedback is the
most critical factor in e-learning that changes the e-
learning process positively and that changes learner-
instructor relationship positively to make e-learning
a superior mode of instruction.
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APPENDIX
Instructor
Inst1 = The instructor was very knowledgeable
about the course
Inst2 = The instructor was actively involved in
facilitating this course
Inst3 = The instructor stimulated students to
intellectual effort beyond that required by face-to-
face courses.
Course Structure
Struc1 =The overall usability of the course Web site
was good.
Struc2 =The course objectives and procedures were
clearly communicated.
Struc3 =The course material was organized into
logical and understandable components.
Feedback
Feed1 = The instructor was responsive to student
concerns.
Feed2 = The instructor provided timely feedback
on assignments, exams, or projects.
Feed3 = The instructor provided helpful timely
feedback on assignments, exams, or projects.
Feed 4 = I felt as if the instructor cared about my
individual learning in this course.
Self-Motivation
Moti1 = I am goal directed, if I set my sights on a
result, I usually can achieve it.
Moti2 = I put forth the same effort in on-line courses
as I would in a face-to-face course.
Learning Style
Styl1 = I prefer to express my ideas and thoughts in
writing, as opposed to oral expression.
Styl2 = I understand directions better when I see a
map than when I receive oral directions.
Interaction
Intr1 = I frequently interacted with the instructor in
this on-line course.
Intr2 = I frequently interacted with other students in
this on-line course.
OUTPUTS
User Satisfaction
Sati1 = The academic quality was on par with face-
to-face courses I’ve taken.
Sati2 = I would recommend this course to other
students.
Sati3 = I would take an on-line course at Southeast
again in the future.
Learning Outcomes
Outc1 = I feel that I learned as much from this
course as I might have from a face-to-face version of
the course.
Outc2 = I feel that I learn more in on-line courses
than in face-to-face courses.
Outc3 = The quality of the learning experience in
on-line courses is better than in face-to-face courses.
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