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Mining Data for Student Models

  • Chapter
Advances in Intelligent Tutoring Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 308))

Abstract

Data mining methods have in recent years enabled the development of more sophisticated student models which represent and detect a broader range of student behaviors than was previously possible. This chapter summarizes key data mining methods that have supported student modeling efforts, discussing also the specific constructs that have been modeled with the use of educational data mining. We also discuss the relative advantages of educational data mining compared to knowledge engineering, and key upcoming directions that are needed for educational data mining research to reach its full potential.

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Authors and Affiliations

  1. Department of Social Science and Policy Studies, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA

    Ryan S. J. d. Baker

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  1. Ryan S. J. d. Baker
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Editors and Affiliations

  1. University of Quebec, Montreal, Canada

    Roger Nkambou

  2. Télé-université, UQAM, Montreal, Canada

    Jacqueline Bourdeau

  3. Osaka University, Osaka, Japan

    Riichiro Mizoguchi

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Baker, R.S.J.d. (2010). Mining Data for Student Models. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds) Advances in Intelligent Tutoring Systems. Studies in Computational Intelligence, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14363-2_16

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  • DOI: https://doi.org/10.1007/978-3-642-14363-2_16

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  • Online ISBN: 978-3-642-14363-2

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