Using Similarity to Infer Meta-cognitive Behaviors During Analogical Problem Solving

摘要

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 meta-cognitive behaviors and subsequent learning.