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A Structural Equation Model of Predictors for Effective Online Learning

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In studying online learning, researchers should examine three critical interactions: instructor-student, student-student, and student-content. Student-content interaction may include a wide variety of pedagogical tools (e.g., streaming media, PowerPoint, and hyperlinking). Other factors that can affect the perceived quality of online learning include distance education advantages (e.g., work and family flexibility) and antecedent personal characteristics (e.g., experience and gender). The study indicated that instructor-student interaction is most important, twice that of student-student interaction; that some student-content interaction is significantly related to perceived learning; that antecedent variables are not significant; and that distance education advantages/flexibility, although significant, are less important than other interactions.
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10.1177/1052562904271199
JOURNAL OF MANAGEMENT EDUCATION / August 2005
Marks et al. / PREDICTORS FOR ONLINE LEARNING
A STRUCTURAL EQUATION MODEL
OF PREDICTORS FOR EFFECTIVE
ONLINE LEARNING
Ronald B. Marks
Stanley D. Sibley
J. B. Arbaugh
University of Wisconsin–Oshkosh
In studying online learning, researchers should examine three critical inter-
actions: instructor-student, student-student, and student-content. Student-
content interaction may include a wide variety of pedagogical tools (e.g.,
streaming media, PowerPoint, and hyperlinking). Other factors that can affect
the perceived quality of online learning include distance education advantages
(e.g., work and family flexibility) and antecedent personal characteristics
(e.g., experience and gender). The study indicated that instructor-student
interaction is most important, twice that of student-student interaction; that
some student-content interaction is significantly related to perceived learning;
that antecedent variables are not significant; and that distance education
advantages/flexibility, although significant, are less important than other
interactions.
Keywords: online learning; structural equation modeling; MBA education
Web-based courses have become popular, with thousands of courses now
offered by educational institutions (“Educators Divided Over Rush,” 1999).
The major driving force for this popularity is the development of new mar
-
kets of nontraditional students, especially working adults (Confessore,
1999), who are geographically distant from the source but seek time and
location flexibility for their education. The expectation, however, for the
future is that the students taking Web-based courses will resemble more
closely current traditional students (Guernsey, 1998). Yet, the rush to offer
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JOURNAL OF MANAGEMENT EDUCATION, Vol. 29 No. 4, August 2005 531-563
DOI: 10.1177/1052562904271199
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Web courses has left many questions about what makes them effective and
satisfactory. Important issues are the perceived advantages of Web-based
courses; the proper level of interactivity (both instructor to student and stu
-
dent to student); appropriate pedagogical tools (e.g., streaming media,
PowerPoint, etc.) to facilitate student-content interaction; and the influence
of antecedent characteristics (e.g., gender, experience, etc.). These issues
provide the framework for this research project.
The objective of this research project is to apply structural equation mod
-
eling to evaluate the potential causative variables of instructor-student inter
-
actions, student-student interactions, student-content interactions (T. Ander
-
son, 2003; Moore, 1976, 1989; Sherry, Fulford, & Zhang, 1998), online
course advantages, personal characteristics, and online course experience of
the student as they may affect learning and satisfaction with Web-based
courses as perceived by students. Perceived student learning was examined
because of the inconsistencies associated with measuring and assigning
grades in this multiple-course, multiple-instructor study design of graduate
courses. Moreover, course grades tend to have a relatively restricted range at
the graduate level (Rovai, 2002). Given the relative newness of Web-based
courses, student satisfaction is likely to determine whether the student takes
subsequent courses in this format, in the same program, or even with the
same education provider. Also, it has been used as the dependent variable ina
number of online studies (Alavi, Wheeler, & Valacich, 1995; Alavi, Yoo, &
Vogel, 1997; Arbaugh, 2000a; Chidambaram, 1996; Warkentin, Sayeed, &
Hightower, 1997).
A major shortcoming of the majority of prior studies is that the bulk of
research in the behavioral and social sciences has addressed relationships
between and among theoretical constructs that are not directly observable
(e.g., motivation, traits, satisfaction, ambiguity, interactivity, learning, atti
-
tude). As a consequence, if a test of theory is desired, variables that can be
observed must be found to use as proxies for the unobservable constructs, or
variables need to be identified to form an index to represent the construct
(Hughes, Price, & Mares, 1986). A major problem inherent in both
approaches is that the measured variables, even with an index, usually con
-
tain at least some moderate amounts of error. As Goldberger (1971) pointed
out, when such measures are used in linear models (e.g., ANOVA, regres
-
sion, and path analysis), the coefficients obtained will be biased, most often
in unknown degree and direction. The same is true of research in distance
education, a shortcoming described by Merisotis (1999):
The validity and reliability of the instruments used to measure student out
-
comes and attitudes are questionable. An important component of good
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educational research relates to proper measurement of learning outcomes and/
or student attitudes. In short, do the measurement instruments—the final
examinations, quizzes, questionnaires, or attitude scales developed by the
teacher—measure what they are supposed to measure? A well-conducted
study would include evidence of the validity and reliability of the measurement
instruments so that the reader could have confidence in the results. But in
almost all of the studies reviewed, this information was lacking. (p. 13)
The purpose of this study was to eliminate many of the problems of
correlational and ordinary least squares analysis by using confirmatory fac
-
tor analysis (CFA) and structural equation modeling (SEM) to systematically
identify plausible evaluation factors and further test the major relationships
between variables (e.g., online instructor activities, student to student activi
-
ties, and learning). SEM improves reliability, or the degree to which a mea
-
sure is “error-free. It is difficult to measure a concept perfectly; thereis
always some degree of measurement error. For example, when asking about
something as straightforward as household income, some people will answer
incorrectly, either overstating or understating the amount, or not knowing it
precisely. The answers provided have some measurement error and thus
affect the estimation of the “true” structural coefficient. Measurement error is
not caused just by inaccurate responses but occurs with more abstract or theo-
retical concepts, such as motives, personality attributes, or other psychologi-
cal constructs. With concepts such as these, the researcher tries to design the
best questions to measure the concept. The participants also may be some-
what unsure about how to respond or may interpret the questions in a differ-
ent way from how the researcher intended. Both situations can give rise to
measurement error. In SEM, interest usually focuses on latent constructs—
abstract psychological variables like “intelligence” or “role ambiguity”—
rather than on the manifest variables used to measure these constructs. SEM
allows the researcher to use one or more variables for a single independent or
dependent latent construct and then estimate the reliability. The researcher
can assess the contribution of each manifest indicator variable, as well as
incorporate how well the indicator variables measure the concept (reliabil
-
ity), and then estimate the relationships between independent and dependent
latent constructs (Hair, Anderson, Tatham, & Black, 1998).
Moreover, this study has additional methodological advantages, as its
sample includes students from multiple courses. Phipps and Merisotis (1999)
criticized distance education research for relying too much on single-course
studies. Multicourse studies like this one provide methodological benefits
such as external validity, increased statistical power, and the ability to control
for instructor-specific characteristics.
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Literature Review and Hypothesis Development
The literature review is organized by the following topics: (a) perfor
-
mance and satisfaction with the Web-based learning experience, (b) advan
-
tages of Web-based courses for students, (c) instructor-student interaction in
Web-based courses, (d) student-student interaction in Web-based courses,
(e) studet-content interaction, (f) personal characteristics and demographics
of Web-based students, and (g) experience as a predictor of student learning
and satisfaction.
PERFORMANCE AND SATISFACTION WITH
THE WEB-BASED LEARNING EXPERIENCE
The learning experience, a performance variable, should be directly
related to satisfaction (Churchill & Surprenant, 1982; Oliver & DeSarbo,
1988; Tse & Wilton, 1988). Of course, whether satisfaction and learning are
synonymous can be debated. One typical contingency outcome assumed
from a successful learning experience, it can be argued, is that the student
should be satisfied with the experience. Satisfaction with course activities
often has been included as a dependent variable in studies of distance edu-
cation, computer-mediated communication, and Web-based courses (Alavi
et al., 1995, 1997; Arbaugh, 2000a; Chidambaram, 1996; Frey, Faul, &
Yankelov, 2003; Warkentin et al., 1997).
One approach to understanding student satisfaction is to study students’
evaluations of the course and their attitudes toward the course (Ellram &
Easton, 1999). Yet, only anecdotal evidence was provided on how the satis-
faction of the student was related to learning by the student. One study com
-
pared satisfaction levels between computer-supported groups and
noncomputer-supported groups with both evaluating the outcomes of the
sessions (Chidambaram, 1996). The perceptions of the process and the cohe
-
siveness of the group contributed to the differences in satisfaction with the
outcomes. The students completing out-of-class exercises with computer
support gave high satisfaction scores for the learning experience and for the
participation in the activities. In contrast, other research has indicated no dif
-
ferences in satisfaction levels of students for the process or outcome mea
-
sures for three different types of offerings: distant videoconferencing, local
telelearning, and face-to-face (Alavi et al., 1995). Nevertheless, time, place,
and pace flexibility should be different in an online environment as opposed
to the traditional setting and in the three settings above whereby the
flexibility was reduced and, hence, should affect satisfaction levels.
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ADVANTAGES OF WEB-BASED COURSES FOR STUDENTS
Convenience and flexibility often are acclaimed as the distinctive and
most valuable features of Web courses (Arbaugh & Duray, 2001; Hiltz &
Wellman, 1997; Hislop, 1999; Shapley, 2000; Sullivan, 2001). These bene
-
fits included the ability to enroll in a class that otherwise would be missed,
finish a degree sooner, save commuting time, arrange a better work schedule,
and spend more time on nonwork activities. Therefore, the following is
hypothesized:
Hypothesis 1: Web-based course advantages/flexibility will be positively related
to student perceived learning and satisfaction.
INSTRUCTOR-STUDENT INTERACTION IN WEB-BASED COURSES
The subject of interactivity has generated controversy among distance
learning professionals who raise questions about the quality of online
courses. The computer-mediated debate has occurred because many educa-
tors believe that interactivity is a vital element in the educational process.
Critics usually stress that interactivity is the missing element or ingredient in
distance education because classes lack traditional face-to-face interactions.
However, proponents state that interactivity in distance education is just as
good as, or even better than, the traditional classroom, and recent research
suggests that it is a highly significant predictor of online course outcomes
(Arbaugh, in press; Swan, 2003; Wagner, 1997).
This debate over interactivity, especially that traditional classroom educa-
tion possesses it and distance education does not, is somewhat misleading.
Even in traditional group-based classroom environments, the majority of a
student’s learning time is spent independently, outside of class; the standard
expectation is 2 to 3 hours of study outside of class for every 1 spent in class.
As Tony Bates of the University of British Columbia noted,
There is an even greater myth that students in conventional institutions are
engaged for the greater part of their time in meaningful, face-to-face interac
-
tion. The fact is that for both conventional and distance education students, by
far the largest part of their studying is done alone, interacting with textbooks or
other learning materials. (Twigg, 2000, p. 1)
Immediacy behaviors and their relationship to student attitudes and learn
-
ing in traditional classrooms have been studied thoroughly by educational
communication scholars (Christophel, 1990; Gorham, 1988; Menzel &
Carrell, 1999). Originally conceptualized by Mehrabian (1971), immediacy
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refers to communication behaviors that reduce social and psychological dis
-
tance between people (Myers, Zhong, & Guan, 1998). At the present time,
the routine demonstration of nonverbal immediacy behaviors, including
emotions, over the Internet ranges from fairly difficult to almost impossible,
depending on what is to be conveyed and the depth of the desired message.
Therefore, these behaviors often are demonstrated via text-based messaging
or emoticons (Gains, 1999; Poole, 2000; Rovai, 2001; Wang, Sierra, &
Folger, 2003). However, behaviors associated with verbal immediacy
(Gorham, 1988; Mehrabian, 1971) still are possible in the virtual environ
-
ment. In this study, immediacy is verbal behavior the instructor makes toward
students that does not necessarily require direct interaction with them. In
aggregate, instructor-student interactions can include using a story or case
history about the topic, encouraging discussion and feedback, or addressing
students by name through discussion or e-mail (Arbaugh, 2001, 2002;
Hutchins, 2003; Swan, 2002). Therefore, the following is hypothesized:
Hypothesis 2: Instructor-student interaction activities will be positively related to
student perceived learning and satisfaction.
STUDENT-STUDENT INTERACTION IN WEB-BASED COURSES
Whereas instructor interaction behavior appears to be an important influ-
ence on Web-based courses, it also is becoming apparent that student interac-
tion, such as in small discussion groups and with peer teaching opportunities,
can be significant. In some studies, adequate opportunity to participate in
online discussions has been associated with enhanced social presence
(Gunawardena, Lowe, & Anderson, 1997) and increased satisfaction with
online courses and discussion forums, particularly when courses use smaller
groups within the course to facilitate discussion (Jonassen, Davidson, Col
-
lins, Campbell, & Haag, 1995; Sherry et al., 1998).
Participation in student-to-student interactions in class discussion has
been shown to be greater (Arbaugh, 2000b; Arbaugh & Rau, 2002;
Benbunan-Fich, Hiltz, & Turoff, 2001), more evenly distributed (Card, 2000;
Strauss, 1996), a source of stronger bonding (Whipp & Schweizer, 2000),
and more liberating (Wolfe, 2000) in Web-based course activities than in tra
-
ditional classrooms. However, this interaction can be significantly more dif
-
ficult to accomplish (Arbaugh, 2000b; Hightower & Sayeed, 1996; Yoo,
Kanawattanachai, & Citurs, 2002) and less satisfying (Ocker & Yaverbaum,
1999; Piccoli, Ahmad, & Ives, 2001; Warkentin et al., 1997).
A significant question meriting research attention is whether instructor-
student or student-student interaction, or a combination, best predicts student
learning and/or satisfaction in Web-based courses. Leidner and Jarvenpaa
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(1995) argued that Web-based courses would fit best with collaborative
learning models because the software used to deliver them facilitates com
-
munication between a number of participants, either synchronously or
asynchronously. Initial research on learning in computer-mediated commu
-
nication environments suggested that Web-enhanced or Web-based courses
support collaborative approaches better than traditional classroom courses
(Alavi, 1994; Alavi et al., 1995; Chidambaram, 1996). Hiltz, Coppola, Rot
-
ter, Turoff, and Benbunan-Fich (2000) studied 26 Information Systems
courses over a 3-year period and found strong evidence for the effectiveness
of collaborative learning. This was supported further by a qualitative study
suggesting that instructors had changed their teaching persona “to a more
Socratic pedagogy, emphasizing multilogues among students” (Coppola,
Hiltz, & Rotter, 2002). In another qualitative study, Brower (2003) suggested
that quality classroom discussion not only was emulated using electronic
bulletin board technology but also went beyond the advantages of regular
classroom discussion. However, Swan (2002), in a recent study of 73 courses
offered by the State University of New York Learning Network, found that
the use of collaborative learning techniques was negatively associated with
student learning. Swan did point out, though, that it was difficult to determine
whether this relationship could be attributed to the use of collaborative learn-
ing in Web-based courses as inappropriate; or that collaborative learningis
appropriate but poorly practiced; or that there are combinations of collabora-
tive and noncollaborative activities that result in a more optimal learning
experience than exclusive reliance on either one. The inconclusive natureof
this research to date suggests that this topic requires extensive research
before any definite conclusions can be made on the appropriateness of
learning approaches.
Mason (1991) studied interactivity in a distance education class at the
Open University in Great Britain and found that teachers played a major role
in directing the online discussions. Instructors influenced the discussion pro
-
cess by encouraging new topics, sharing new material, and redirecting the
conversation patterns. The project did find that student interactions were fos
-
tering learning by integrating personal experience into class discussions and
by gaining insights from other students. Yet only one third of the students
actively engaged in providing and receiving online feedback. The study
raised additional concerns that student interactions did not promote critical
thinking opportunities to seriously examine course themes. Experienced
online teachers have complained that the threaded-topic design, a common
technique for enabling student-student dialog, does not effectively support
group learning and problem solving (Turoff & Hiltz, 1995, 2001). Threaded-
topic design typically requires the cumbersome process of opening and clos
-
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ing many messages. There is no way for students to create in-context links
from within a given message or to insert text or multimedia into any jointly
prepared document. In short, the hypertext power of the Web often is con
-
spicuously absent in threaded-topic discussions. For this reason, Docushare,
a Web-based document management system that facilitates easily storing,
accessing, and sharing information in a password protected environment, is
being used by some universities (“What’s New at LTDE?” 2003).
Notwithstanding mixed research results, as the two learning management
systems, Blackboard and Lotus LearningSpace, used by the student sample
in this study included threaded discussion, chat rooms, and e-mail as primary
means of student-student interaction, the following was hypothesized:
Hypothesis 3: Student-student interaction will be positively related to student per
-
ceived learning and satisfaction.
Hypothesis 3a: The effect of student-student interaction will be equivalent to that
of instructor-student interaction (i.e., the path coefficients will be approxi
-
mately equal).
STUDENT-CONTENT INTERACTION
If an instructor’s goal were simply to replicate the classroom learning
environment on the Internet, it would be difficult according to media richness
(Daft & Lengel, 1984) and social presence theories (Rice, 1984; Sproull &
Kiesler, 1991). Two identified problems were unembellished text-based
media and the elimination of nonverbal communications. To offer a higher
value, the text-based online course experience could be supplemented with
both useful and inexpensive traditional print media (Moore, 1987) and new
media, such as videoconferencing (albeit expensive for the institution and the
student) (Alavi et al., 1997), voice messaging, video clips, and/or multimedia
(Bailey & Coltar, 1994; Greco, 1999). Multimedia technology can provide
multisensory experiences to enhance learning. These online interactions can
be as good as or better than traditional classroom lectures (Davis, 2003;
Weigel, 2002). If these interactions failed to provide a better learning experi
-
ence, then instructors may not need to invest the necessary and substantial
time to prepare and arrange for multimedia presentations (Dumont, 1996;
Neumann, 1998).
Student-content interaction refers to pedagogical tools and assignments,
including PowerPoint presentations, streaming audio and video presenta
-
tions (feasible but difficult for students with dial-up connections), group pro
-
jects, individual projects, and embedded links in Web courses. These tools
and activities represent pedagogies from a social constructivist view that stu
-
dents themselves are creators of knowledge with others (Benbunan-Fich,
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2002; Jonassen et al., 1995). Of course, instructors must be careful not to
overuse these pedagogies, as the possibilities of sensory overload and cogni
-
tive dissonance are real. Nevertheless, the following hypothesis is proposed:
Hypothesis 4: Student-content interactions will be positively related to perceived
student learning and satisfaction.
PERSONAL CHARACTERISTICS AND
DEMOGRAPHICS OF WEB-BASED STUDENTS
It is intuitive that personal characteristics and demographics should be
related to learning and satisfaction with Web courses. Yet, research on age,
gender, and GPA have provided little predictive power in determining
whether the student would choose a Web-based course or an in-class course
on the same topic (Parnell & Carraher, 2003; Roblyer, 1996). Whether age,
GPA, and gender of the student are related to Web-based course perceptions
on learning and satisfaction is still open. Age differences, with students
younger than 20 years old liking Web-based instruction more than students
older than 23 years old, have been found (Sanders & Morrison-Shetlar,
2001), but questions remain from the very small sample size of the older stu-
dents. Gender differences in computing have been a topic of concern and
comment for 20 years (Arbaugh & Duray, 2001). Do females take to the com-
puter to the same degree as males? Are they more anxious about computing?
Do they use the computer differently? Do they value different aspects of
computing? Do they communicate differently when using the computer? The
answers to these questions are particularly important to pursue when so many
distant learners have been adult females (Hardy & Boaz, 1997; Moore &
Kearsley, 1996). One study suggested that females had a more positive atti
-
tude toward a Web-based course than did males (Sanders & Morrison-
Shetlar, 2001). In spite of these mixed results from previous research, using
SEM with its improved reliability, the following is posited:
Hypothesis 5: Age, grade point average, and gender (male vs. female), respec
-
tively, are significantly related to student perceived learning and satisfaction.
EXPERIENCE AS A PREDICTOR OF STUDENT
LEARNING AND SATISFACTION
Prior studies have shown that computing experience is a strong predictor
of attitudes toward computers, computer usage (Dyck & Smither, 1994;
Thompson, Higgins, & Howell, 1994; Whitley, 1997), and Internet usage
(Atkinson & Kydd, 1997). In an online learning environment, this experience
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has been associated with spending more time in the course, logging on to the
course site more frequently, and being more likely to take additional courses
via the medium in the future (Hiltz, 1994; Lee, Hong, & Ling, 2001; Smith,
Ferguson, & Caris, 2001). This implies that students who spend more time on
the Web-based course and/or who have prior experience with Web-based
courses are more likely to be satisfied with the experience and take more
ownership of the learning process, thereby increasing their own learning.In
addition, as students gained experience in using the computer and the
Internet in a Web-based course, they became more at ease with the course and
the various components, such as bulletin boards and e-mails (Jones & Wolf,
2001). Therefore, the following hypothesis is posited:
Hypothesis 6: Prior student experience with online courses is positively related to
student perceived learning and satisfaction.
Sample and Data Collection
The sample for the study was taken from the 43 class sections that were
conducted using either Lotus LearningSpace or Blackboard course software
platforms in the MBA program of an upper-Midwest U.S. university from
summer semester 1998 through fall semester 2001. A listing of the courses
and number of sections offered for each course is provided in Table 1. Nearly
all students in these courses also were enrolled in the university’s classroom-
based MBA program. Thirteen different instructors taught the courses, with
one instructor teaching three and another teaching two of the sections. This
alleviates the external validity problems associated with generalizing from
just one course. As mentioned earlier, a sample with multiple classes has
methodological advantages (Phipps & Merisotis, 1999).
Data collection was completed in a two-step process. In the first step, stu
-
dents completed a survey, either in traditional classrooms or from the course
Web site for online classes. In the second step, the nonresponding students
were mailed a copy of the survey. The student response rate was 78.4% (a
total N of 659).
METHOD
An initial evaluation instrument was developed, incorporating items from
the factors identified in previous studies cited above as major componentsof
Internet teaching effectiveness. These items and factors were chosen to beas
exhaustive a representation as possible of the suggested aspects of distance
teaching effectiveness discussed above, and appear in the appendix.
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Dependent variables. Because of the multiinstructor, multicourse, and
multidisciplinary nature of the study, we were unable to develop a common
measure of actual student learning. Therefore, we measured students’ per-
ceived learning using Alavi’s (1994) six-item scale on issue identification,
communications on the subject, and integration and generalization of the
course material and additional items from Arbaugh and Duray (2001). Per-
ceived satisfaction with the course, time and effort required, and decision to
take an Internet course each was measured using Arbaugh’s (2000a) seven-
item scale, where 1 = strongly disagree and 7 = strongly agree (Alavi et al.,
1995, 1997; Arbaugh & Duray, 2001; Chidambaram, 1996; Warkentin et al.,
1997).
Independent variables. Variables related to perceived learning and satis
-
faction, significant in prior studies, were measured, including antecedent
characteristics, instructor-student and student-student interaction, and student-
content support tools. Instructor-student interaction was measured with
items from Gorhams (1988) “immediacy” scale and additional items from
Dillon, Hengst, and Zoller (1991); Sherry et al. (1998); and Thach and
Murphy (1995), from whom student-student items also were adopted. Ante
-
cedents measured were personal characteristics, including gender (0-1
dummy coding with women represented by “1”), GPA, and experience (in
terms of number of online courses completed).
Marks et al. / PREDICTORS FOR ONLINE LEARNING 541
TABLE 1
Courses in the Study by Discipline or Subject Area
Discipline/Subject Area Number of Courses Offered
OB/OT 6
Finance 6
General management electives
a
6
Professional skills 5
Project management 4
International business 3
Accounting 3
Management information systems 3
Human resources 2
Operations management 2
Marketing 2
Strategy 1
NOTE: OB/OT = organizational behavior/organizational training
a. Management electives included topics such as Environmental (Green) Management, Plan-
ning for Management in the Future, and Classic and Contemporary Literature in Business.
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Student-content tools and exercises in the online courses included the
number of PowerPoint presentations, streaming audio presentations, stream-
ing video presentations, group projects, peer teaching opportunities, individ-
ual projects, and embedded links to other Web sites. Data for these measures
were collected for each course either by reviewing the course Web site or ask-
ing the course instructor. We also used 0-1 dummy coding to measure
whether an instructor divided the students in their course(s) into smaller dis
-
cussion groups. Online advantages of convenience and flexibility included
the ability to enroll in a class that otherwise would be missed, finish a degree
sooner, save commuting time, arrange a better work schedule, and spend
more time on nonwork activities (Arbaugh & Duray, 2001; Hiltz & Wellman,
1997; Hislop, 1999; Shapley, 2000; Sullivan, 2001). Convenience/flexibility
was measured using Arbaughs (2000a) six items, using a 7-point strongly
disagree to strongly agree scale. Descriptive statistics for these variables are
provided in Table 2.
In light of previous research, the theoretical model is hypothesized below
(see Figure 1). Structural paths all were posited to be positive with the excep
-
tion of gender because a positive or negative coefficient would not be mean
-
ingful for a nominal variable.
The data were analyzed with LISREL 8.52 (du Toit & du Toit, 2001),
using the original framework for SEM developed by Joreskog and Sorbom
(1993). Factor analyses, both orthogonal and oblique, initially were usedto
542 JOURNAL OF MANAGEMENT EDUCATION / August 2005
TABLE 2
Descriptive Statistics
Standard
Variable Mean Deviation
Perceived learning 5.23 1.28
Perceived satisfaction 4.83 1.75
Student-instructor interaction 4.70 1.27
Student-student interaction 3.11 1.45
Student age 31.78 6.41
Student gender 0.40 0.49
# of prior online courses taken 1.57 1.74
# of audio clips 3.73 4.73
# of video clips 0.64 2.35
# of PowerPoint/lecture notes posted 7.85 5.79
Convenience/flexibility 5.28 1.71
# of individual projects 3.21 1.36
# of group projects 0.30 0.63
# of peer teaching techniques 0.55 0.77
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identify possible latent variables. Following this identification, the method
-
ology of J. C. Anderson and Gerbing (1988)—CFA followed by SEM—was
employed to determine the paths between latent variables. CFA (which, in
the interest of brevity, is not discussed in this article) initially identified five
latent variables: learning, instructor-to-student activities, student-to-student
activities, online advantages, and satisfaction. Starting with the five-con
-
struct model of the CFA, latent variables were tested for discriminant validity
by setting phi equal to one and testing for a significant change in the Chi-
squared value (Bollen, 1989). At this juncture, the test for discriminant valid
-
ity between satisfaction and learning failed. Students evidently cannot make
a distinction between satisfaction and perceived learning. Accordingly, satis
-
faction was dropped from the model, and only perceived learning was
retained as the single endogenous (i.e., dependent) variable.
Indicators for instructor-student activities are “the instructor frequently
asked students questions, “interaction between the instructor and classwas
Marks et al. / PREDICTORS FOR ONLINE LEARNING 543
Figure 1: Theoretical Model
+
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high, and “(instructor) used personal examples or commented on experi
-
ences he/she has had outside of class.” Indicators for student-student activi
-
ties (reverse scored) are “the students seldom asked each other questions,”
“there was little interaction between students, and “students seldom
answered each other’s questions. Other student-student indicators included
smaller discussion groups and peer teaching opportunities. (It should be
noted that the last three of these items were dropped in fitting the structural
equation model for lack of statistical significance.) Indicators for online
advantages/flexibility are “taking this class via the Internet saved me a lot of
time commuting to class,” “taking this class via the Internet allowed me to
arrange my work schedule more effectively, and “taking this class via the
Internet allowed me to spend more time on non-work-related activities.
Indicators for learning are “I learned to identify the central issues of the
course,” “I developed the ability to communicate clearly about the subject,
and “I improved my ability to integrate facts and develop generalizations
from the course material.
Single-indicator latent variables included in the analysis are gender, GPA,
experience, and student-content variables, including PowerPoint presenta-
tions, streaming audio presentations, streaming video clips, group and indi-
vidual projects, and embedded links in Web courses, respectively. Many of
the student-content variables represent methods of trying to improve media
variety, richness, and overall content (notwithstanding cost). Error variances
were set at .0 or .10, depending on estimated reliability. This follows the logic
of Hayduk (1987), who suggests that error variance for single indicators
should be fixed depending on the estimated reliability of the measurement.
Gender, for example, would be expected to have little error variance and
would be fixed at .0, whereas the aggregate number of PowerPoint presenta
-
tions would be less reliable and accordingly fixed at .10.
Discussion of Fitted Model
This section is organized by discussing the overall fit of the model, the
insignificant variables, and the significant variables in order of the sizes of
the path coefficients. We will use this information to report the results of our
hypothesis tests. Hypotheses 1 through 3 suggested that flexibility, student-
instructor interaction, and student-student interaction, respectively, would be
significantly associated with student perceived learning/satisfaction. As
detailed in Figure 2 and Tables 3a through 3c, the fit for this model with per
-
ceived learning as an endogenous latent variable was very good with a chi-
square of 58.87 (P = .23), root mean square error of approximation = .014,
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Comparative Fit Index = 1.00, and Adjusted Goodness-of-Fit Index = 1.00.
There is only one residual that exceeds 2.58, meeting the standard of 1 in 20
exceeding 2.58 (Hair et al., 1998). Significant paths are included in the com
-
pletely standardized solution of Figure 2. With the exception of individual
projects, the paths are all positive. These results indicate strong support for
Hypotheses 1 through 3.
Hypotheses 4 through 6 suggest that student-content interaction, student
demographic characteristics, and student experience with online learning, re-
spectively, would be significantly associated with perceived student learning/
satisfaction. However, the variables not statistically significant were gender,
GPA, experience, total number of PowerPoint presentations, streaming audio
Marks et al. / PREDICTORS FOR ONLINE LEARNING 545
Figure 2: Fitted Model
NOTE: Variable names are as follows: I11 = “The instructor frequently asked students ques-
tions”; I14 = “Interaction between the instructor and class was high”; V1 = Instructor) used per-
sonal examples or commented outside of class”; I18 = “The students seldom asked each other
questions”; I20 = “There was little interaction between students”; A3 = Taking this class via the
Internet saved me a lot of time commuting to class”; A4 = “Taking this class via the Internet al-
lowed me to more effectively arrange my work schedule”; A5 = “Taking this class via the Internet
allowed me to spend more time on non-work related activities”; R10 = “I learned to identify the
central issues of the course”; R11 = “I developed the ability to communicate clearly about the
subject”; R12 = “I improved my ability to integrate facts and develop generalizations from the
course material”. GRPJT = group project; INDJPT = individual project.
0.19
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presentations, streaming video presentations, smaller discussion groups,
peer teaching opportunities, and embedded links in Web courses. With the
exception of the marginally significant individual projects (with a negative
coefficient) and group projects (positive), the majority of student-content
variables were insignificant; however, any conclusions from the data should
only be tentative, given the low levels of usage for some of these activities.
546 JOURNAL OF MANAGEMENT EDUCATION / August 2005
TABLES 3a-3c
Completely Standardized Structural Model (LISREL)
Results With Perceived Learning as the Dependent Variable
TABLE 3a
Measurement Model, Standard Path Coefficients,
and Significance Values
Std.
Item Path Coef. t-Value
Measurement model: Exogenous variables
Instructor-student activities
The instructor frequently asked students questions .81 23.50
Interaction between the instructor and class was high .85 24.82
(Instructor) used personal examples or commented outside
of class .76 24.63
Student-student activities (reversed scoring)
The students seldom asked each other questions .85 23.90
There was little interaction between students .90 25.79
Online advantages
Taking this class via the Internet saved me a lot of time
commuting to class .68 17.75
Taking this class via the Internet allowed me to more
effectively arrange my work schedule .85 23.52
Taking this class via the Internet allowed me to spend more
time on non-work-related activities .71 18.68
Projects
Group project 1.00 (fixed)
Individual project 1.00 (fixed)
Measurement model: Endogenous variables
Perceived learning
I learned to identify the central issues of the course .81 (fixed)
a
I developed the ability to communicate clearly
about the subject .85 24.82
I improved my ability to integrate facts and develop
generalizations from the course material .86 24.63
a. Fixed by the program.
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Hence, Hypotheses 5 and 6 failed, whereas Hypothesis 4 is only partially
supported.
Hypothesis 2 is supported, as instructor-student interaction is statistically
significant. In fact, instructor activities have the largest path coefficient, .44,
of any latent variable, meaning that Hypothesis 3a (i.e., path coefficients for
instructor-student and student-student are equivalent) is not supported. It
appears critical that an instructor relates to students by encouraging discus
-
sion, providing feedback, and sharing personal experiences.
Hypothesis 3 also is supported. Student-to-student activities also had a
positive path coefficient of .21, implying that learning is facilitated by com
-
munication among students themselves. The fact that the coefficient is
approximately half that for instructor activities suggests that faculty first
should emphasize their own interactions with students in Web courses.
Examination of mean values (which peaked at the neither agree nor disagree
Marks et al. / PREDICTORS FOR ONLINE LEARNING 547
TABLE 3b
Interfactor Correlations of the Items
Interfactor Correlation Correlation t-Value
Instructor and student-student activities .64 21.55
Instructor and online advantages .29 6.65
Instructor and group project .00 –0.13
Instructor and individual project –.02 –0.24
Student-student and online advantages .32 7.66
Student-student and group project .01 0.20
Student-student and individual project –.06 –0.88
Online advantages and group project .05 1.92
Online advantages and individual project –.14 –1.92
Group project and individual project –.06 –1.29
TABLE 3c
Lambda Path Coefficients and Overall Statistics
Lambda Path Coefficient Coefficient t-Value
Instructor-student activities .44 8.07
Student-student activities .21 3.99
Advantages of online courses .15 3.66
Group projects .08 2.42
Individual projects –.07 –2.00
NOTE: Chi-square of 58.87 (P = .23); root mean square error of approximation = .014; Compar
-
ative Fit Index = 1.00; Adjusted Goodness-of-Fit Index = 1.00
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scale point) for the student-student indicators implies that students desired
further participation on the part of their classmates. This parallels the finding
of Mason (1991), who found that only one third of students were actively
engaged in providing and receiving online feedback. It also could be possible
that collaborative learning is appropriate but inadequately practiced, as Swan
(2002) suggested, although the relationship is positive here.
Although having a smaller path coefficient (+.15), the advantages of tak
-
ing an online course are positively related to perceived learning, supporting
Hypothesis 1. The ability to reduce time commuting and arrange a more
effective work schedule is viewed as contributing to learning. This is similar
to the findings of other studies (Arbaugh & Duray, 2001; Hiltz & Wellman,
1997; Hislop, 1999; Shapley, 2000; Sullivan, 2001). Results here from a
multicourse sample add confidence to this conclusion.
Limitations
This study has limitations that should cause the reader to take conclusions
drawn from it with caution. First, this study did not have measures of actual
participant interaction such as the number of comments made by instructors
or students. Whereas tracking the number of comments is fairly common in
single-course studies with relatively small enrollments (Ahern & El-Hindi,
2000; Arbaugh, 2000b; Larson, 2002; Poole, 2000; Rovai, 2001), the authors
decided to measure perceptions of participation across a broad sample of
courses instead of actual participation in a small number of courses. Because
research in distance education has found that perceptions of interaction may,
in fact, be a better predictor than actual participation in the course (Fulford &
Zhang, 1993), this approach is acceptable. Second, although the study helps
to answer recent calls for multidiscipline, multisemester studies in online
management education (Hiltz & Arbaugh, 2003), it is based on the findings
at a single institution. Third, the students taking these courses were enrolled
in the university’s regular MBA program and were taking both these courses
and courses in on-site classrooms. This may prevent the study’s findings
from being generalizable to completely online MBA programs or to online
undergraduate programs.
Implications
In spite of these limitations, there are a number of potential implications
from this study for online instructors, learners, and administrators of online
programs. Arguably the most significant contribution of the study is that it
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prioritizes the relative importance of the various types of participant interac
-
tion. By doing so, this study builds on the findings of recent studies of the
effects of interaction in online courses (Arbaugh, in press; Shea, Fred-
ericksen, Pickett, & Pelz, 2004; Swan, 2003). Therefore, our discussion of
implications will focus first on the dimensions of interaction and then move
to the other variables.
EFFECT OF INSTRUCTOR-STUDENT INTERACTION
Instructor behaviors toward students are the most important explanatory
variable in the model. This finding provides further support for recent
research on the importance of the instructors role in an online learning envi
-
ronment (Arbaugh & Duray, 2001; Coppola et al., 2002; Easton, 2003; Mar
-
tins & Kellermanns, 2004) and has definite implications for instructor con
-
duct in online management education. Although much has been made of the
shift of an instructors role in an online environment from the “sage on the
stage” to a “guide by the side” (Gibson, 1996), our study suggests that this
role of instructor as guide certainly is not passive. The instructor’s behaviors
as guide reflect the importance of his or her relationships with students.
Encouraging and motivating students to learn could include seeking student
involvement in discussion, telling a case story about a subject to aid remem-
bering, using positive reinforcement for successful performance, and asking
questions.
More specifically, the instructor can discuss Web-based course protocols
online; reinforce the importance of participation during the term (Bowman,
2001); post assignment directions, lecture notes, and grades online; and use
e-mail to communicate to the students. A recent study of 18 instructor behav
-
iors revealed that these instructor-initiated activities were the five most val
-
ued by students (Frey et al., 2003). Sanders and Morrison-Shetlar (2001) also
found support for instructor-initiated communications, as students had very
positive attitudes toward handling assignments and completing course work,
taking quizzes, and finding their grades on the Internet. Communicating to
students can start before the course begins to welcome them to the course and
continue throughout the course to offer assignment directions, explain infor
-
mation, stimulate critical thinking, reinforce major concepts and applica
-
tions, and bestow psychological rewards. E-mail for lecture notes and assign
-
ments was used most heavily by faculty, but also by students, and was
considered by both as the technology causing the highest increase in their
productivity (Zhao, Alexander, Perreault, & Waldman, 2003). One caution is
that the instructor should not be perceived as the authoritarian master in com
-
municating to students because of the detrimental effect on subsequent stu
-
dent communications (Jetton, 2003-2004).
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Even the successful online instructor runs into the problem of student
motivation and student interaction in the Internet learning arena. Contrary to
the instructors expectations, the students may focus on their personal experi
-
ences rather than engage in critical thinking (Angeli, Valanides, & Bonk,
2003), or they may have a lull in critical thinking and participation. Ways to
solve this problem include introducing new approaches to stimulate the stu
-
dents such as writing letters to motivate students for enduring learning (May,
1995).
These types of activities have the potential for dramatically increasing the
time an instructor spends on a Web-based course relative to a classroom-
based course (Berger, 1999; Dumont, 1996). In the interest of controlling
burgeoning faculty workloads, we offer two suggestions to address this con
-
cern. First, colleges and universities should make training available forfac
-
ulty in online teaching that goes beyond how to use course software plat
-
forms (Alavi & Gallupe, 2003). This training should focus on skills relatedto
self-management and online course organization. Second, the suggestions
provided in previous paragraphs will help faculty members incorporate the
concepts of teaching presence (Garrison, Anderson, & Archer, 2000; Shea et
al., 2004) into their online courses. Teaching presence addresses issues
related to course organization, facilitation of discourse, and direct instruc-
tion. In addition to the suggestions already provided, an instructor can incor-
porate these principles by doing such things as providing expectations for
course progress and student participation in online discussions at the start of
the course, including a section of “frequently asked questions,” and making
responsibilities for learning activities such as leading and/or summarizing
class discussions part of the student’s course grade. These practices can help
the instructor engage in learner-instructor interaction in a more holistic man
-
ner, rather than continually dealing with each individual student, thereby
helping to make the instructor’s time investment in the course more
manageable.
STUDENT-STUDENT INTERACTION
The effect of student-student interactivity as reported was less than
expected. This may not reflect its importance as much as its practice, such as
how the students are working in the small groups assigned by the instructor.
Contrary to the expectations of the instructor seeking student-student
interactivity on assignments, the students might be dividing the assignments
among group members, resulting in limited interaction as one group member
takes leadership on an assignment. This practice would explain the modest
interactivity perceptions of the students. After receiving questions from stu
-
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dents in online courses, the instructor can either send the questions and
answers to all of the group members or reply only to the sender of each ques
-
tion. If the latter were how the instructor communicated in the online course
and students were not aware of this, then student-student interaction would
not be perceived as great. Remediation of the issue could be handled by send
-
ing answers to all members of the student group or, if merited, to the class asa
whole. A related question is whether students who answer questions and post
to the bulletin board on a regular basis tend to do better on tests and demon
-
strate a greater degree of critical thinking in completing the course require
-
ments (Baugher, Varanelli, & Weisbord, 2003; Sanders & Morrison-Shetlar,
2001).
The significance of online advantages implies that course convenience
and flexibility still are important; however, the relative strength of the find
-
ings on instructor-student and student-student interactions suggests that the
online advantage of convenience may be lessening as a competitive advan
-
tage over traditional courses or other forms of distance education courses
(Arbaugh & Duray, 2002). Therefore, those institutions offering online
courses may need to focus on enhancing the learning effectiveness and/or the
cost-effectiveness of these courses if they wish to remain competitive in the
future (Mayadas, Bourne, & Moore, 2002).
Using small discussion groups and student peer-teaching opportunities
appears not to have an influence on perceptions of learning. In this study, the
no-influence results may be due to the relatively low usage. Support for this
conclusion comes from a comparison of three instructional methods, varying
the use of group discussions, group activities, and personal experiences. The
results suggest that two methods with either less or more group discussions
and related activities had lower levels of learning than the hybrid model with
moderate use of group discussions and related activities (Nadkarni, 2003).
Another reason is that students may not realize the value of such activitiesin
leading others, working with others, and developing higher level results. The
part-time evening graduate students in this sample, most having 5 to 10 years
of career experience, may believe that their experiences with teams and
teaching others, especially subordinates, in their formal organizations may
have resulted in a flat learning curve for them. It also is true that females,
especially, with greater time demands at home, may see limited value to these
assignments in distance education (May, 1994, 1996). Thus, much of the
benefits from the small group dynamics and leadership roles in teaching
other students are lost to these groups, and the real value of these types of
assignments is less from the process and more from the outcome of the
assignment. One solution is straightforward, with the instructor providing
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explicit reasons to the students on the learning value of such assignments in
online courses.
STUDENT-CONTENT INTERACTION
The use of PowerPoint presentations and embedded links in online
courses has been popular due to both the desire of instructors to provide
value-added content and embellish the course beyond the textbook and the
ease of uploading prepared presentations and links by textbook publishers.
Although intuitively these additions should enhance learning, the students do
not perceive this. One reason might be the inundation of PowerPoint presen
-
tations and embedded links in virtually all business courses, both online and
in class, resulting in a “so what” attitude toward them by time-constrained
students with career, social, and family responsibilities. Furthermore, these
additions to the course may not help learning relative to the textbook, mate
-
rial uploads, and other student-content tools and assignments. The instructor
of the online course needs to consider ways to substitute for or complement
the PowerPoint presentations and embedded links to provide enhancements
to learning for students. These might include problem-based learning exer-
cises, based on work or community interactions; creative exercises, such as
using a “game show” format with follow-up answers; quizzes relating to the
applications of the material; a “scavenger hunt” of relevant course topics and
issues on the Internet; or a wordplay in creating a written assignment (e.g.,
brand name, slogan, or headline for an advertisement) for a known product.
In summary, these findings suggest that behavioral and administrative factors
may be more important and more cost efficient than technological factors for
conducting effective online graduate courses.
The lack of significance of streaming technology might be explained by
the limited number of students exposed to streaming audio and video clips,
by the students having better materials for learning within the time con
-
straints of the online course, or by the students’ concept of learning. Instead
of considering themselves the creators of new knowledge in the social
constructivism model, students may depend on the instructor to provide
knowledge to them (Lee et al., 2001; Oliver & Omari, 2001). Thus, group and
individual interactions are not perceived as helpful to these students for
learning.
PERSONAL CHARACTERISTICS AND
DEMOGRAPHICS OF STUDENTS
The student experience variable and the demographic and personal vari
-
ables of gender, age, and GPA were not related significantly to perceptionsof
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learning performance. Student experience was a surprise because it had been
found to be significant in other studies on computer and Internet usage
(Atkinson & Kydd, 1997; Whitley, 1997). However, how much online course
experience for a graduate student is necessary to have an influence on learn
-
ing? It is entirely possible that after a student takes one online course and
learns the course management software, additional online course experience
has no or very little effect on learning relative to other more important vari
-
ables, such as interactivity with the instructor and other students and just
finding time to complete the exercises and assignments.
Of these predictors, age is the easiest to explain because of the homogene
-
ity in age of the students due to the night MBA program being targeted at full-
time employed students with 5 to 10 years of career experience. Gender and
GPA may not be useful predictors any longer, as experience with Internet
courses increases. Internet courses at the graduate level have expanded from
a few initial pilot courses to a rapid growth in the past few years, with many
students now having completed several courses on the Internet in pursuing
their degrees. Thus, demographic and personal variables may no longer pro-
vide meaningful distinctions of students and their performance.
Conclusions
Of the types of interactions, instructor behaviors toward students are the
most important relationships to manage when teaching online courses for
perceived learning. The instructor has to be an active participatory leader to
motivate students to learn. The instructor is responsible for course organiza-
tion, process management, and potential learning outcomes. Most of the
work and time is managing the process with the course management soft
-
ware. Without a doubt, instructor-initiated communications are instrumental
in creating positive attitudes of students, motivating them to learn, and in
keeping them focused on the topic. Initially, the instructor using the soft
-
ware’s tools should welcome the students to the course and discuss the sylla
-
bus, especially the expectations of success and the importance of involve
-
ment in the course. Also, the pairing of students to introduce each other has
merit to start establishing communications between students and in the
course (Bowman, 2001).
The instructor should seek student involvement in the course by creating
interesting discussion topics, stimulating critical thinking, asking questions,
and bringing in relevant points. In addition, the instructor can have the stu
-
dents synthesize and integrate these discussions. Some students will desire to
communicate about personal issues rather than the course’s topics on discus
-
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sion boards (Woods, 2002). Some instructors believe that discussions on per
-
sonal issues contribute to the sense of the online community; thus, they may
create a special discussion forum for this activity. Research has supported the
relationship between the online community and learning (Rovai, 2002,
2004).
The influence of student-student interactivity was less than expected.
Small discussion groups and student peer-teaching opportunities appear not
to have an influence on perceptions of learning. However, the active instruc
-
tor still should develop ways to increase this interactivity of groups in pro
-
ductive ways. Meaningfulness could be enhanced if the instructor provided
explicit reasons to the student groups on the learning value of such assign
-
ments. One method is to create a question-and-answer forum for the assign
-
ment to discuss its value, to answer basic questions, and to allow all class
members to raise questions and to see the answers for the benefit of all. Other
ways to increase student interactivity are debates, synchronous meetings,
brainstorming, opinion surveys, and guest speakers with discussion (Bow-
man, 2001). One problem is the tendency for some small groups to let one
person in the group do all the work for the assignment and then rotate people
for the next assignment. The easiest solution is to have group members evalu-
ate each other on performance on the assignment and then to form new
groups for the next one.
The use of PowerPoint presentations, embedded links, and streaming
technology in online courses appears to be not helpful for explaining per-
ceived learning. However, the use of a variety of these tools may itself be
important (Arbaugh, in press). One study indicated that possibility, although
the variance explained in a regression analysis was limited (Marks & Sibley,
2004). The instructor may need to rely less on technological factors and focus
more on the behavioral and managerial issues.
Online courses offer much promise as instructors become more knowl
-
edgeable on how to behave toward students taking online courses and how to
manage the course content to enhance perceived and actual learning. The
excitement and opportunity with online courses is the continuously dynamic
learning process as both instructors and students learn from more interactive
experiences with online courses. Therefore, as we learn more about the effec
-
tive use of this delivery medium, instructors need to continually study and
apply the information generated from new research findings on online
courses.
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Appendix
Initial Evaluation Measures
(measured using 7-point Likert-type scales)
Course Interaction
1. It was easy to follow class discussions.
2. Classroom dynamics were not much different from those in other MBA
courses I have taken.
3. The level of interaction between class participants was high.
4. In general, the instructor was effective in motivating the students to interact in
this course.
5. Interaction was low in this course.
6. Once we became familiar with Blackboard, it had very little effect on the class.
7. Student-instructor interaction was more difficult than in other MBA courses I
have taken.
8. The instructor frequently offered opinions to students.
9. Students often stated their opinions to the instructor.
10. Students often asked the instructor questions.
11. The instructor frequently asked the students questions.
12. Interacting with other students and the instructor via Blackboard became more
natural as the course progressed.
13. The instructor frequently attempted to elicit student interaction.
14. Interaction between the instructor and the class was high.
15. The instructor seldom answered the students’ questions.
16. Students seldom answered questions that the instructor asked.
17. Class discussions were more difficult to participate in than other MBA courses
I have taken.
18. The students seldom asked each other questions.
19. Student-student interaction was more difficult than in other MBA courses I
have taken.
20. There was little interaction between students.
21. In this class, I learned more from my fellow students than in other MBA courses.
22. I felt I had adequate opportunities to participate in class discussions.
23. In this class, students seldom stated their opinions to each other.
24. Students seldom answered each other’s questions.
25. I felt that the quality of class discussions was high throughout the course.
Perceived Learning/Course Quality
1. I learned to interrelate the important issues in the course material.
2. I learned a great deal of factual material in this course.
3. I gained a good understanding of the basic concepts of the material.
4. I learned to identify the central issues of the course.
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5. I developed the ability to communicate clearly about the subject.
6. I improved my ability to integrate facts and develop generalizations from the
course material.
7. The quality of the course compared favorably to my other MBA courses.
8. Conducting the course over the Internet improved the quality of the course com
-
pared to other MBA courses I have taken.
9. I feel the quality of the course I took was largely unaffected by conducting it over
the Internet.
10. Conducting the course over the Internet made it more difficult than other courses
I have taken.
Instructor Behaviors
1. Used personal examples or commented on experiences he or she has had outside
of class.
2. Asked questions or encouraged students to talk.
3. Got into discussions based on something a student brought up even when it didn’t
seem to be part of his or her course plan.
4. Used humor in class.
5. Addressed students by name.
6. Addressed me by my name.
7. Referred to our class as “our” class or what “we” are doing.
8. Provided feedback on my individual work through comments on papers, discus-
sions, etc.
9. Asked students how they felt about an assignment, due date, or discussion topic.
10. Invited students to call or meet with him or her if they had questions or wanted to
discuss something.
11. Asked questions that solicited viewpoints or opinions.
12. Praised students’ work or comments.
13. Had discussions about things unrelated to class with individual students or the
class as a whole.
14. Was addressed by his or her first name by the students.
Advantages/Flexibility
1. Taking this class via the Internet allowed me to take a class I would otherwise
have to miss.
2. Taking this class via the Internet should allow me to finish my degree more
quickly.
3. Taking this class via the Internet saved me a lot of time commuting to class.
4. Taking this class via the Internet allowed me to arrange my work schedule more
effectively.
5. Taking this class via the Internet allowed me to spend more time on non-work-
related activities.
556 JOURNAL OF MANAGEMENT EDUCATION / August 2005
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6. Taking this class via the Internet allowed me to arrange my work for the class
more effectively.
7. The advantages of taking this class via the Internet outweighed any
disadvantages.
8. There were no serious disadvantages to taking this class via the Internet.
Course Satisfaction
1. I was very satisfied with this course.
2. If I had another opportunity to take another course via the Internet I would gladly
do so.
3. I am satisfied with the amount of time required for this course.
4. I was disappointed with the way this course worked out.
5. I was satisfied with the amount of work required for this course.
6. I am satisfied with my decision to take this course via the Internet.
7. If I had it to do over, I would not take this course via the Internet.
8. I feel that this course served my needs well.
9. My choice to take this course via the Internet was a wise one.
10. I will take as many MBA courses via the Internet as I can.
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... A similar strong association was demonstrated by others [93,94]. Ref. [95] narrated that an instant output of a productive learning experience is a contented student, and observed that the student-perceived learning consequence is an acceptable predictor of student satisfaction in online learning. Ref. [58] observed that the output of perceived learning added to student satisfaction and positively impacted it in the online environment. ...
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... One typical contingency outcome assumed from a successful learning experience, and it can be argued, is that the student is satisfied with the experience. Satisfaction with course activities has frequently been included as a dependent variable in studies of distance education, computer-mediated communication, and Web-based courses (Marks et al., 2005). The COVID-19 outbreak has spawned a pandemic in every region of the globe. ...
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... Oleh karena itu, pemahaman prinsip dan faktor yang memengaruhi keefektifan teknologi dalam pembelajaran adalah hal yang esensial bagi para pendidik (Putrawangsa & Hasanah, 2018;Syihabuddin, 2017). Dalam implementasi pembelajaran daring, ada tiga kriteria yang diperhatikan untuk mengukur kesuksesannya, hubungan antara guru dan siswa; siswa dan siswa: siswa dan bahan ajar (Marks, Sibley, & Arbaugh, 2005) Untuk itu, dibutuhkan perantara penghubung elemen-elemen tersebut melalui jejaring internet. ...
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