Granularity-Based Reasoning and Belief Revision in Student Models

In this chapter we discuss two important research topics surrounding student modelling: 1) how to represent knowledge about a student at various grain sizes and reason with this knowledge to enhance the capabilities of an intelligent tutoring system, and 2) how to maintain a consistent view of a student’s knowledge as the system-student interaction evolves. The ability to represent and reason about knowledge at various levels of detail is important for robust tutoring. A tutor can benefit from incorporating an explicit notion of granularity into its representation and can take advantage of granularity-based representations in reasoning about student behaviour. As the student’s understanding of concepts evolves and changes, the student model must track these changes. This leads to a difficult student model maintenance problem. Both of these topics are full of interesting subtleties and deep issues requiring years of research to be resolved (if they ever are), but a start has been made. In this chapter we characterize the main requirements for each topic, discuss some of our work that tackles these topics, and, finally, indicate important areas for future research.