Temporal Data Mining for Educational Applications
Received:February 10, 2009  Revised:July 15, 2009  Download PDF
Paul R. Cohen,Carole R. Beal. Temporal Data Mining for Educational Applications. International Journal of Software and Informatics, 2009,3(1):31~46
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Fund:This e?ort is sponsored by the the U.S. National Science Foundation and the U.S. Institute ofEducation Sciences.
Abstract:Intelligent tutoring systems (ITSs) acquire rich data about students' behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.
keywords:educational data mining  student modeling
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