Using Similarity to Infer Meta-cognitive Behaviors During Analogical Problem Solving
We present a computational framework designed to provide adaptive support aimed at triggering learning from problem-solving activities in the presence of worked-out examples. The key to the framework’s ability to provide this support is a user model that exploits a novel classification of similarity to infer the impact of a particular example on a given student’s metacognitive behaviors and subsequent learning.
KeywordsNormal Force Fact Node Intelligent Tutor System Superficial Similarity Adaptive Support
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