ABSTRACTIn this study, structural equation modeling is applied to examine the determinants of students’ satisfaction and their perceived learning outcomes in the context of universityonline courses.Independent variables included are course structure instructor feedback self-motivation learning style interaction and instructor facilitation A total 397 valid unduplicated responses from student taking at least one online courseOf the six antecedent variables only instructor feedback and learning outcome are significant.

The findings suggest 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) and with timely, meaningful instructor feedback of various types.

The distance learning system can be viewed as having several human/nonhuman entities interacting together via computer-based instructional systems to achieve the goals of education, including perceived learning outcomes and student satisfaction.

The primary objective of this study is to investigate the determinants of students’ perceived learning outcomes and satisfaction in university online education using e-learning systems.

INTRODUCTION

THE IMPORTANT FACTORS THAT CONTRIBUTE TO THE SUCCESS OF E-LEARNING SYSTEMS

1. Student Self-Motivationwe 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.

2. Students’ Learning Styles

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.

3. Instructor Knowledge and Facilitation

we hypothesized:

H3a: A higher level of instructor knowledge and facilitation will lead to a higher level of user satisfaction.

H3b: A higher level of instructor knowledge and facilitation will lead to higher levels of student agreement that the learning outcomes of online courses are equal to or better than in face-to-face courses.

4. Instructor Feedback

we hypothesized:H4a: A high level of instructor feedback will lead to a high level of usersatisfaction.

H4b: A higher level of instructor feedback will lead to higher levels of student agreement that the learning outcomes of online courses are equal to or better than in face-to-face courses.

5. Interaction we hypothesized:H5a: A high level of perceived interaction between the instructor and students and between students and students will lead to a high level of user satisfaction.

H5b: A higher level of perceived interaction between the instructor and students and between students and students will lead to higher levels of student agreement that the learning outcomes of online courses are equal to or better than in face-to-face courses.

6. Course Structure

we hypothesized: H6a: A good course structure will lead to a high level of user satisfaction.

H6b: A good course structure will lead to higher levels of student agreement that the learning outcomes of online courses are equal to or better than in face-to-face courses.

STRUCTURAL MODEL RESULTS

DISCUSSION

We found that all six factors—course structure, self-motivation, learning styles, instructor knowledge and facilitation, interaction, and instructor feedback—significantly influenced students’ satisfaction.

Of the six factors hypothesized to affect perceived learning outcomes, only two (learning styles and instructor feedback) were supported.

Contrary to other research findings, no significant relationships were found between students’ self-motivation and perceived learning outcomes.

Additional work is needed to better specify the conditions under which self-motivation is likely to have a positive, negative, or neutral effect on perceived learning outcomes.

LIMITATIONS AND DIRECTIONS FOR FUTURE

RESEARCH future research should seek to further investigate the

non significant relationships between the remaining constructs (course structure, self-motivation, and interactions) and perceived learning outcomes.

future studies should use more sophisticated measures of course structure, self-motivation, and interactions and their engagement in learning activities, either quantitatively or qualitatively.

Although students are in general satisfied with online courses, they believe that they did not learn more in online courses or they believe that the quality of online courses was not better than face-to-face class.

In future research, it would be interesting to know the critical success factors for improving the quality of online learning using multilevel hierarchical modeling.

PRACTICAL IMPLICATIONS

This study is one of the first to extend the structural equation modeling to student satisfaction and perceived learning outcomes in asynchronous online education courses.

The results indicated that online education is not a universal innovation applicable to all types of instructional situations. Our findings suggest 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) and with timely, helpful instructor feedback of various types.

More specifically, there is a clear relationship between instructor feedback and student satisfaction and perceived outcomes.

Online quizzes can provide preprogrammed feedback to learners.

online learning will be enhanced when there is a better understanding of critical online learning factors.