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Date of Award
Dissertation - NSU Access Only
Doctor of Philosophy in Information Systems (DISS)
Graduate School of Computer and Information Sciences
Steven D Zink
academic performance, attitutde, e-learning, motivation, satisfaction
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.
Anthony Jeffrey Nichols. 2008. An Empirical Assessment of Attitude toward Computers, Motivation, Perceived Satisfaction from the e-learning System, and Previous Academic Performance and their Contribution to Persistence of College Student Athletes Enrolled in e-Learning Courses. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Graduate School of Computer and Information Sciences. (261)