Bayesian Student Modeling

  • Cristina Conati
Part of the Studies in Computational Intelligence book series (SCI, volume 308)


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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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cristina Conati
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouver

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