Providing Adaptive Support for Meta-Cognitive Skills to Improve Learning

Kasia Muldner and Cristina Conati

We describe a computational framework designed to provide adaptive support for learning from problem solving activities that make worked-out examples available. This framework targets several meta-cognitive skills required to learn effectively in this type of instructional setting, including explanation-based-learning-of-correctness and min-analogy. The generated interventions are based on an assessment of a student’s knowledge and meta-cognitive skills provided by the framework’s student model, and thus are tailored to that student’s needs.

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