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

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Date: Sept. 2016
Publisher: Online Learning Consortium
Document Type: Report
Length: 4,597 words
Lexile Measure: 1510L

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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.

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Gale Document Number: GALE|A615363453