VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self- explanation effect. Journal of the Learning Sciences, 2(1), 1-60.

Several investigations have taken protocols of students learning sophisticated skills, such as physics problem solving and Lisp coding, by studying examples and solving problems. These studies uncovered the self-explanation effect: students who explain examples to themselves learn better and use analogies more economically while solving problems. This article describes a computer model, Cascade, that accounts for these correlations. Explaining an example causes Cascade to acquire new domain knowledge as well as derivational knowledge that is used analogically to control search during later problem solving. New domain knowledge is acquired when Cascade's current domain knowledge is incomplete and causes it to reach an impasse. If the impasse can be resolved by applying an overly general rule or some other non-domain rule, a specialization of the rule becomes a new domain rule. Computational experiments show that Cascade's learning mechanisms are able to reproduce the self-explanation effect.

 

Download the 3 MB pdf file