Simple but effective feedback generation to tutor abstract problem solving

To generate natural language feedback for an intelligent tutoring system, we developed a simple planning model with a distinguishing feature: its plan operators are derived automatically, on the basis of the association rules mined from our tutorial dialog corpus. Automatically mined rules are also used for realization. We evaluated 5 different versions of a system that tutors on an abstract sequence learning task. The version that uses our planning framework is significantly more effective than the other four versions. We compared this version to the human tutors we employed in our tutorial dialogs, with intriguing results.

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