Proceedings of the Annual Meeting of the Cognitive Science Society

We used the Quinlan's C4.5 machine learning algorithm to analyze tutorial dialogues as part of the derivation of planning rules for CIRCSIM-Tutor v. 3, a natural-language based intelligent tutoring system. We annotated a corpus of tutoring dialogues with an SGML-based representation of tutorial goals in order to make mechanical processing possible. We looked for rules of the form "under what conditions is goal x implemented with plan y?". We discovered rules for high-level plaiming of the tutoring session and dynamic modification of the tutorial agenda. At a lower level of planning, we looked at rules for generating sections of the tutor's utterance. The use of the rule induction algorithm has helped us discover which knowledge available to the planner is significant in making these decisions, as well as producing some decision trees we can actually use in CIRCSIM-Tutor.