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Bayesian Student Modeling

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Book cover Advances in Intelligent Tutoring Systems

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

Abstract

Bayesian networks are a formalism for reasoning under uncertainty that has been widely adopted in Artificial Intelligence (AI). Student modeling, i.e., the process of having an ITS build a model of relevant student’s traits/states during interaction, is a task permeated with uncertainty, which naturally calls for probabilistic approaches. In this chapter, I will describe techniques and issues involved in building probabilistic student models based on Bayesian networks and their extensions. I will describe pros and cons of this approach, and discuss examples from existing Intelligent Tutoring Systems that rely on Bayesian student models

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6G3C1

    Cristina Conati

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  1. Cristina Conati
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Editor information

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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Conati, C. (2010). Bayesian Student Modeling. 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_14

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  • DOI: https://doi.org/10.1007/978-3-642-14363-2_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14362-5

  • Online ISBN: 978-3-642-14363-2

  • eBook Packages: EngineeringEngineering (R0)

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