Proceedings of the Annual Meeting of the Cognitive Science Society

Several investigations have found that students learn more when they explain examples to themselves while studying them. Moreover, they refer less often to the examples while solving problems, and they read less of the example each time they refer to it. These findings, collectively called the self-explanation effect, have been reproduced by our cognitive simulation program. Cascade. Cascade has two kinds of learning. It learns new rules of physics (the task domain used in the human data modeled) by resolving impasses with reasoning based on overly-general, non-domain knowledge. It acquires procedural competence by storing its derivations of problem solutions and using them as analogs to guide its search for solutions to novel problems. This paper discusses several runs of Cascade wherein the strategies for explaining examples is varied and the initial domain knowledge b held constant. These computational experiments demonstrate the computational sufficiency of a strategy-based account for the self-explanation effect.