Towards Tutorial Dialog to Support Self- Explanation: Adding Natural Language Understanding to a Cognitive Tutor *

Self-explanation is an effective metacognitive strategy, as a number of cognitive science studies have shown. In a previous study we showed that self-explanation can be supported effectively in a cognitive tutor for geometry problem solving. In that study, students explained their own problem-solving steps by selecting from a menu the name of a problem-solving principle that justifies the step. They learned with greater understanding, as compared to students who did not explain their reasoning. Currently, we are working toward testing the hypothesis that students will learn even better when they provide explanations in their own words rather than selecting them from a menu. We have implemented a prototype of a cognitive tutor that understands students' explanations and provides feedback. The tutor uses a knowledge-based approach to natural language understanding. We are entering a phase of pilot testing, both for the purpose of assessing the coverage of the natural language understanding component and for gaining insight into the kinds of dialog strategies that are needed.

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