Abstract
We present in this paper the architecture of MetaTutor, an intelligent
tutoring system that teaches students meta-cognitive
strategies while learning about complex science topics. A
more in-depth presentation of the micro-dialogue component
in MetaTutor is provided. This component handles the
meta-cognitive strategy of subgoal generation. This strategy
involves subgoal assessment and feedback generation.
We present a taxonomy-driven method for subgoal assessment
and feedback. The method yields very good to excellent
human-computer agreement scores for subgoal assessment
(average kappa=0.77).
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