Bayesian student modeling

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 © 2010 Springer-Verlag Berlin Heidelberg.