CEC Theses and Dissertations

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Date of Award

2008

Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

Graduate School of Computer and Information Sciences

Advisor

Yair Levy

Committee Member

Steven Terrell

Committee Member

Steven D Zink

Abstract

In recent years, the application of Information Technologies has fostered a tremendous growth in e-learning courses at colleges and universities in the United States. Subsequently, some colleges and universities have reported dropout rates of over 60% in e-learning courses. Therefore, the persistence of identifiable groups of students enrolled in e-learning courses has garnered increased attention and research. Information Systems researchers suggested that studies of persistence e-learning courses identify and investigate specific constructs as well as identifiable target populations. Furthermore, as a separate and identifiable group, the college student athlete has received extensive coverage in the research literature, however, limited attention for their dropout in e-learning courses. Therefore, this research investigated persistence in e-learning courses of an identified population of college student athletes. In order to predict the persistence of college student athletes enrolled in e-learning courses, this research an empirically assessed a conceptual model, e-Learning Persistence Model (e-LPM). e-LPM was based on selected constructs that have previously shown tendencies to persistence in e-learning courses. This research, therefore, empirically assessed the constructs of e-LPM in the predication of persistence in a population of 187 college student athletes enrolled in e-learning courses.

The constructs of e-LPM includes, student's attitude toward computers, student's intrinsic and extrinsic motivation, student's perceived satisfaction from the e-learning system, and student's previous academic performance measures (high school GPA and SAT score). The e-LPM constructs were empirically assessed and weighted in order to evaluate their contribution to persistence in e-learning courses. Survey response data from college student athletes at the beginning and at the end of e-learning courses were quantitatively analyzed using Ordinal Logistic Regression, ANOVA, chi-square, and t-test statistical techniques.

Results of this research showed that e-LPM was able to predict persistence in e-learning course 81.4% of the time. The previous academic performance measure of GPA was shown to significantly predict e-learning course persistence in the research population. In the analysis of gender, female college student athletes exhibited higher intrinsic and extrinsic motivation than their male counterparts.

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