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Predictors for student success in on...

Mathes, Jennifer Lynn.

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Predictors for student success in online education.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictors for student success in online education./
Author:	Mathes, Jennifer Lynn.
Description:	156 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-03, Section: A, page: 0869.
Contained By:	Dissertation Abstracts International64-03A.
Subject:	Education, Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3086133

Predictors for student success in online education.
Mathes, Jennifer Lynn.

Predictors for student success in online education. - 156 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: A, page: 0869.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.

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.Subjects--Topical Terms:

1017498
Education, Technology.

Predictors for student success in online education.
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500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: A, page: 0869.
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502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.
520 $a 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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