Elicited and mined rules for dropout prevention in online courses | IEEE Conference Publication | IEEE Xplore

Elicited and mined rules for dropout prevention in online courses


Abstract:

This article presents an ongoing investigation that uses mining tools to analyze a large volume of data from students of online courses in a LMS platform to discover Asso...Show More

Abstract:

This article presents an ongoing investigation that uses mining tools to analyze a large volume of data from students of online courses in a LMS platform to discover Association Rules used to identifying dropout situations. These ARs are used along with several explicit (formalized) rules elicited from course operators and are intended to be used by a software agent to detect individuals within a certain risk region, triggering a communication process to a student support team.
Date of Conference: 21-24 October 2015
Date Added to IEEE Xplore: 07 December 2015
Print ISBN:978-1-4799-8454-1
Conference Location: El Paso, TX, USA

References

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