TY - JOUR
T1 - A Model of the Self-Explanation Effect
AU - VanLehn, Kurt
AU - Jones, Randolph M.
AU - Chi, Michelene T.H.
N1 - Funding Information:
Research for this article was supported by the Cognitive Sciences Division (N00014-88-K-0086) and the Information Sciences Division (N00014-86-K-0678) of Office of Naval Research.
PY - 1992/1/1
Y1 - 1992/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0003126106&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0003126106&partnerID=8YFLogxK
U2 - 10.1207/s15327809jls0201_1
DO - 10.1207/s15327809jls0201_1
M3 - Article
AN - SCOPUS:0003126106
SN - 1050-8406
VL - 2
SP - 1
EP - 59
JO - Journal of the Learning Sciences
JF - Journal of the Learning Sciences
IS - 1
ER -