Optimising ITS Behaviour with Bayesian Networks and Decision Theory
In IJAIED
12 (2)
Publication information
Abstract
We propose and demonstrate a methodology for building tractable normative
intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term
student modelling and decision theory to select the next tutorial action. Because normative
theories are a general framework for rational behaviour, they can be used to both define and
apply learning theories in a rational, and therefore optimal, way. This contrasts to the more
traditional approach of using an ad-hoc scheme to implement the learning theory. A key step of
the methodology is the induction and the continual adaptation of the Bayesian network student
model from student performance data, a step that is distinct from other recent Bayesian net
approaches in which the network structure and probabilities are either chosen beforehand by an
expert, or by efficiency considerations. The methodology is demonstrated by a description and
evaluation of CAPIT, a normative constraint-based tutor for English capitalisation and
punctuation. Our evaluation results show that a class using the full normative version of CAPIT
learned the domain rules at a faster rate than the class that used a non-normative version of the
same system.
