2015 IEEE Frontiers in Education Conference (FIE)
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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.
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