A Model of the Self-Explanation Effect

Kurt VanLehn, Randolph M. Jones, Michelene Chi

Research output: Contribution to journalArticle

154 Citations (Scopus)

Abstract

Several investigators 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. We describe a computer model, 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. Computational experiments indicate that Cascade's learning mechanisms are jointly sufficient to reproduce the self-explanation effect, but neither alone can reproduce it.

Original languageEnglish (US)
Pages (from-to)1-59
Number of pages59
JournalJournal of the Learning Sciences
Volume2
Issue number1
DOIs
StatePublished - Jan 1 1992
Externally publishedYes

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Learning
Students
knowledge
cause
Physics
self-assessment
Computer Simulation
specialization
learning
physics
coding
student
Research Personnel
experiment
Self-Assessment

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Education

Cite this

A Model of the Self-Explanation Effect. / VanLehn, Kurt; Jones, Randolph M.; Chi, Michelene.

In: Journal of the Learning Sciences, Vol. 2, No. 1, 01.01.1992, p. 1-59.

Research output: Contribution to journalArticle

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