We are engaged in a research project to create a tutorial dialogue system that helps students to explain the reasons behind their problem-solving actions, in order to help them learn with greater understanding. Currently, we are pilottesting a prototype system that is able to analyze student explanations, stated in their own words, recognize the types of omissions that we typically see in these explanations, and provide feedback. The system takes a knowledge-based approach to natural language understanding and uses a statistical text classifier as a backup. The main features are: robust parsing, logic-based representation of semantic content, representation of pedagogical content knowledge in the form of a hierarchy of partial and complete explanations, and reactive dialogue management. A preliminary evaluation study indicates that the knowledge-based natural language component correctly classifies 80 % of explanations and produces a reasonable classification for all but 6 % of explanations.
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