VanLehn, K., & Jones, R. M. (1993). Learning by explaining examples to oneself: A computational model. In S. Chipman & A. L. Meyrowitz (Eds.), Foundations of knowledge acquisition: Cognitive models of complex learning (pp. 25- 82). Boston: Kluwer.

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 examples each time they refer to them. These findings, collectively called the self-explanation effect, have been reproduced by the authors' cognitive simulation program, Cascade. Moreover, when Cascade is forced to explain exactly the parts of the example that a subject explains, then it predicts most (60 to 90%) of the behavior that the subject exhibits during subsequent problem solving. 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.

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