Decision Sciences Journal of Innovative Education
Volume 4 Number 2
July 2006
Printed in the U.S.A.
The Determinants of Students’ Perceived
Learning Outcomes and Satisfaction in
University Online Education: An Empirical
Investigation
∗
Sean B. Eom
†
and H. Joseph Wen
Department of Accounting and MIS, Harrison College of Business, Southeast Missouri State
University, Cape Girardeau, MO 63701, e-mail: sbeom@semo.edu, hjwen@semo.edu
Nicholas Ashill
School of Marketing and International Business, P.O. Box 600, Wellington, New Zealand,
e-mail: Nicholas.Ashill@vuw.ac.nz
ABSTRACT
In this study, structural equation modeling is applied to examine the determinants of
students’ satisfaction and their perceived learning outcomes in the context of university
online courses. Independent variables included in the study are course structure, instruc-
tor feedback, self-motivation, learning style, interaction, and instructor facilitation as
potential determinants of online learning. 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. 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 feed-
back and learning style are significant. The structural model results also reveal that user
satisfaction is a significant predictor of learning outcomes. The findings suggest online
education can be a superior mode of instruction if it is targeted to learners with spe-
cific learning styles (visual and read/write learning styles) and with timely, meaningful
instructor feedback of various types.
Subject Areas: Asynchronous Learning, Correlation Analysis, Distance
Education/Distance Learning, Learning Effectiveness, Perceived Learning
Outcomes, Structural Equation Modeling, Student Satisfaction, and User-
Satisfaction.
INTRODUCTION
The landscape of distance education is changing. This change is being driven by
the growing acceptance and popularity of online course offerings and complete
∗
We thank the editor and two anonymous referees for their suggestions which improved the quality of
this article significantly.
†
Corresponding author.
215
216 Students’ Learning Outcomes in University Online Education
online degree programs at colleges and universities worldwide. The distance learn-
ing system can be viewed as having several human/nonhuman entities interacting
together via computer-based instructional systems to achieve the goals of educa-
tion, including perceived learning outcomes and student satisfaction. These two
outcomes are widely cited as measures of the effectiveness of online education
systems (e.g., Alavi, Wheeler, & Valacich, 1995; Graham & Scarborough, 2001).
The primary objective of this study is to investigate the determinants of stu-
dents’ perceived learning outcomes and satisfaction in university online education
using e-learning systems. Using the extant literature, we begin by introducing and
discussing a research model illustrating factors affecting e-learning systems out-
comes. We follow this with a description of the cross-sectional survey that was
used to collect data and the results from a Partial Least Squares (PLS) analysis of
the research model. In the final section, we outline the implications of the results
for higher educational institutions and acknowledge the limitations of the study
and a future research agenda.
THE IMPORTANT FACTORS THAT CONTRIBUTE TO THE
SUCCESS OF E-LEARNING SYSTEMS
Our conceptual model illustrating factors potentially affecting e-learning systems
outcomes is built on the conceptual frameworks of Piccoli, Ahmad, and Ives
(2001). Piccoli et al. (2001) refer to human and design factors as antecedents
of learning effectiveness. Human factors are concerned with students and instruc-
tors, while design factors characterize such variables as technology, learner control,
course content, and interaction. The conceptual framework of online education pro-
posed by Peltier, Drago, and Schibrowsky (2003) consists of instructor support and
mentoring, instructor-to-student interaction, student-to-student interaction, course
structure, course content, and information delivery technology. Our research model
is illustrated in Figure 1.
Student Self-Motivation
Students are the primary participants of e-learning systems. Web-based e-learning
systems placed more responsibilities on learners than traditional face-to-face learn-
ing systems. A different learning strategy, self-regulated learning, is necessary for
e-learning systems to be effective. Self-regulated learning requires changing roles
of students from passive learners to active learners. Learners must self-manage
the learning process. The core of self-regulated learning is self-motivation (Smith,
2001). Self-motivation is defined as the self-generated energy that gives behavior
direction toward a particular goal (Zimmerman, 1985, 1994).
The strength of the learner’s self-motivation is influenced by self-regulatory
attributes and self-regulatory processes. The self-regulatory attributes are the
learner’s personal learning characteristics including self-efficacy, which is
situation-specific self-confidence in one’s abilities (Bandura, 1977). Because self-
efficacy influences choice, efforts, and volition (Schunk, 1991), a survey question
(Moti1 in Appendix A) representing self-efficacy is used to measure the strength
of self-motivation. The self-regulatory processes refer to the learner’s personal
Eom, Ashill, and Wen 217
Figure 1: Research model.
Student
Self-motivation
Student
Learning Style
Instructor Know-
ledge and
Facilitation
Instructor
Feedback
Interaction
Course
Structure
Perceived
Student
Satisfaction
Learning
Outcomes
H1a
H1b
H2a
H2b
H3a
H3b
H4a
H4b
H5a
H5b
H6a
H6b
learning processes such as attributions, goals, and monitoring. Attributions are
views in regard to the causes of an outcome (Heider, 1958). A survey question
(Moti2 in Appendix A) representing a controllable attribution is used to measure
the strength of 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 & 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 (e.g.,
Frankola, 2001; LaRose & Whitten, 2000). Students’ motivation is a major factor
that affects the attrition and completion rates in the Web-based course and a lack
218 Students’ Learning Outcomes in University Online Education
of motivation is also linked to high dropout rates (Frankola, 2001; Galusha, 1997).
Thus, we hypothesized:
H1a: Students with a higher level of motivation will experience a higher level
of user satisfaction.
H1b: Students with a higher level of motivation in online courses will report
higher levels of agreement that the learning outcomes equal to or better
than in face-to-face courses.
Students’ Learning Styles
Learning is a complex process of acquiring knowledge or skills involving a learner’s
biological characteristics/senses (physiological dimension); personality character-
istics such as attention, emotion, motivation, and curiosity (affective dimension);
information processing styles such as logical analysis or gut feelings (cognitive
dimension); and psychological/individual differences (psychological dimension)
(Dunn, Beaudry, & Klavas, 1989). Due to the multiples dimensions of differences
in each learner, there have been continuing research interests in learning styles.
Some 21 models of learning styles are cited in the literature (Curry, 1983) includ-
ing the Kolb learning preference model (Kolb, 1984), Gardner’s theory of multiple
intelligence (Gardner, 1983), and the Myers-Briggs Personality Type Indicators
(Myers & Briggs, 1995). The basic premise of learning style research is that differ-
ent students learn differently and students experience higher level of satisfaction
and learning outcomes when there is a fit between a learner’s learning style and a
teaching style.
This study uses the physiological dimension of the study of learning styles,
which focus on what senses are used for learning. A popular typology for the
physiological dimension of the learning styles is VARK (Visual, Aural, Read/write,
and Kinesthetic) (Drago & Wagner, 2004, p. 2).
1) Visual: visual learners like to be provided demonstrations and can learn
through descriptions. They like to use lists to maintain pace and organize
their thoughts. They remember faces but often forget names. They are
distracted by movement or action but noise usually does not bother them.
2) Aural: aural learners learn by listening. They like to be provided with aural
instructions. They enjoy aural discussions and dialogues and prefer to work
out problems by talking. They are easily distracted by noise.
3) Read/write: read/write learners are note takers. They do best by taking
notes during a lecture or reading difficult material. They often draw things
to remember them. They do well with hands-on projects or tasks.
4) Kinesthetic: kinesthetic learners learn best by doing. Their preference is
for hands-on experiences. They are often high energy and like to make use
of touching, moving, and interacting with their environment. They prefer
not to watch or listen and generally do not do well in the classroom.
One can speculate that a different set of learning styles is served in an online
course than in a face-to-face course. We assume that online learning systems may
Eom, Ashill, and Wen 219
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 as-
signment 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.
Hence, we hypothesized:
H2a: Students with visual and read/write learning styles will experience a
higher level of user satisfaction.
H2b: Students with visual and read/write learning styles will report higher
levels of agreement that the learning outcomes of online courses are
equal to or better than in face-to-face courses.
Instructor Knowledge and Facilitation
Some widely accepted learning models are objectivism, constructivism, collab-
orativism, cognitive information processing, and socioculturalism (Leidner &
Jarvenpaa, 1995). Traditional face-to-face classes using primarily the lecture
method, use the objectivist model of learning whose goal is transfer of knowl-
edge from instructor to students. Even in distance learning, it is still a critical
role of the instructor to transfer his/her knowledge to students, because the knowl-
edge of the instructor is transmitted to students at different locations (Leidner &
Jarvenpaa, 1995). Thus, we included a question to ask the perception of students in
regard to the knowledge of the instructor: The instructor was very knowledgeable
about the course.
Distance learning can easily break a major assumption of objectivism that
the instructor houses all necessary knowledge. For this reason, distance learning
systems can utilize many other learning models such as constructivist, collabo-
ratism, 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. The basic premise of this model of collaboratism is that students
learn through shared understanding of a group of learners. Therefore, instruction
becomes communication-oriented and the instructor becomes a discussion leader.
Distance learning facilities promote collaborative learning across distances with
facilities to enable students to communicate with each other. The socioculturism
model necessitates empowering students with freedom and responsibilities because
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 his/her students via empowering students with freedom and respon-
sibility, 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 two questions to assess the roles of the in-
structor as the facilitator and stimulator: “The instructor was actively involved in
facilitating this course” and “The instructor stimulated students to intellectual effort
beyond that required by face-to-face courses.” Therefore, we hypothesized:
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