Temporal data mining for educational applications

Carole R. Beal, Paul R. Cohen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    16 Scopus citations


    Intelligent tutoring systems (ITSs) acquire rich data about studentsÖ 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.

    Original languageEnglish (US)
    Title of host publicationPRICAI 2008
    Subtitle of host publicationTrends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings
    Number of pages12
    StatePublished - 2008
    Event10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008 - Hanoi, Viet Nam
    Duration: Dec 15 2008Dec 19 2008

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5351 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
    Country/TerritoryViet Nam

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)


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