ABSTRACT
Two major techniques in machine learning, explanation-based learning and explanation completion, are both superficially plausible models for Chi’s self-explanation effect, wherein the amount of explanation given to examples while studying them correlates with the amount the subject learns from them. We attempted to simulate Chi’s protocol data with the simpler of the two learning processes, explanation completion, in order to find out how much of the self-explanation effect it could account for. Although explanation completion did not turn out to be a good model of the data, we discovered a new learning technique, called explanation-based learning of correctness, that combines explanation-based learning and explanation completion and does a much better job of explaining the protocol data. The new learning process is based on the assumption that subjects use a certain kind of plausible reasoning.
