Predictors for Student Success in Online Education
Mathes, Jennifer Lynn
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https://hdl.handle.net/2142/79736
Description
Title
Predictors for Student Success in Online Education
Author(s)
Mathes, Jennifer Lynn
Issue Date
2003
Doctoral Committee Chair(s)
James Levin
Department of Study
Education
Discipline
Education
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Education, Community College
Language
eng
Abstract
This study was conducted to identify those factors (demographic and personal, attitudinal, behavioral, and instructional) that may be useful as predictors to student success in an online course. Included in the sample were students who enrolled in a full-semester online credit course at a Midwestern community college during the spring 2002 semester. Participation in the study was voluntary. The students were enrolled in courses that crossed several disciplines. To measure student success, two academic outcomes---course completion and final course grade---were used. Students who completed the course and received a grade of C or better were considered to have successfully completed the course. Students who did not complete the online course or received a D or lower were considered unsuccessful. The actual final course grade was also used for the second analysis. A logistic regression analysis was conducted to examine course completion while final course grade was analyzed using multinomial logistic regression analysis. Two strategies were employed for each of these methods. The first was empirically-derived. Based on the statistics that showed the greatest significance on their own, several models were developed. This was followed by a conceptually-derived strategy. Variables identified as important in the conceptual framework for the study were entered into a final model. Age, Marital Status, Academic Intent, Discipline, Predicted Course Grade, Attitude (LASSI scale), Anxiety (LASSI scale) and Rotter's Locus of Control Score were all identified as significant predictors of online student success.
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