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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2008: PRICAI 2008: Trends in Artificial Intelligence pp 66–77Cite as

Temporal Data Mining for Educational Applications

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5351)

Abstract

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.

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  • DOI: 10.1007/978-3-540-89197-0_10
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References

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Author information

Authors and Affiliations

  1. University of Arizona, USA

    Carole R. Beal & Paul R. Cohen

Authors
  1. Carole R. Beal
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  2. Paul R. Cohen
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Editor information

Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Asahidai 1-1, 923-12292, Nomi, Japan

    Tu-Bao Ho

  2. Department of Computer Science & Technology, Nanjing University, 22 Hankou Road, 210093, China

    Zhi-Hua Zhou

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© 2008 Springer-Verlag Berlin Heidelberg

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Beal, C.R., Cohen, P.R. (2008). Temporal Data Mining for Educational Applications. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_10

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  • DOI: https://doi.org/10.1007/978-3-540-89197-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89196-3

  • Online ISBN: 978-3-540-89197-0

  • eBook Packages: Computer ScienceComputer Science (R0)