Proceedings of the Annual Meeting of the Cognitive Science Society

Interpreting the student's actions and inferring the student's solution plan during problem solving is one of the main challenges of tutoring based on cognitive apprenticeship, especially in domains with large solution spaces. We present a student modeling framework that performs probabilistic plan recognition by integrating in a Bayesian network knowledge about the available plans and their structure and knowledge about the student's actions and mental state. Besides predictions about the most probable plan followed, the Bayesian network provides probabilistic knowledge tracing, that is assessment of the student's domain knowledge. We show how our student model can be used to tailor scaffolding and fading in cognitive apprenticeship. In particular, we describe how the information in the student model and knowledge about the structure of the available plans can be used to devise heuristics to generate effective hinting strategies when the student needs help.