A Model of the Self-Explanation Effect

Abstract

Several investigation have taken protocols of students learning sophisticated skills, such as physics problem solving and Lisp coding, by studying examples and solving problems. These investigations uncovered the self- explanation effect: students who explain examples to themselves learn better, make more accurate self-assessments of their understanding and use analogies more economically while solving problems. This paper describes a computer model, named Cascade, that accounts for these findings. Explaining an example causes Cascade to acquire both domain knowledge and derivational knowledge. Derivational knowledge is used analogically to control search during problem solving. Domain knowledge is acquired when the current domain knowledge is incomplete and causes an impasse. If the impasse can be resolved by applying an overly general rule, then a specialization of the rule becomes a new domain rule.