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Assessing Readiness for Online Education--Research Models for Identifying Students at Risk
ARTICLE

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Journal of Asynchronous Learning Networks Volume 20, Number 3, ISSN 1939-5256

Abstract

This study explored the interaction between student characteristics and the online environment in predicting course performance and subsequent college persistence among students in a large urban U.S. university system. Multilevel modeling, propensity score matching, and the KHB decomposition method were used. The most consistent pattern observed was that native-born students were at greater risk online than foreign-born students, relative to their face-to-face outcomes. Having a child under 6 years of age also interacted with the online medium to predict lower rates of successful course completion online than would be expected based on face-to-face outcomes. In addition, while students enrolled in online courses were more likely to drop out of college, online course outcomes had no direct effect on college persistence; rather other characteristics seemed to make students simultaneously both more likely to enroll online and to drop out of college.

Citation

Wladis, C., Conway, K.M. & Hachey, A.C. (2016). Assessing Readiness for Online Education--Research Models for Identifying Students at Risk. Journal of Asynchronous Learning Networks, 20(3), 97-109. Retrieved November 29, 2022 from .

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