Accession Number : ADA247698


Title :   Learning by Explaining Examples to Oneself: A Computational Model


Descriptive Note : Book chapter


Corporate Author : PITTSBURGH UNIV PA LEARNING RESEARCH AND DEVELOPMENT CENTER


Personal Author(s) : VanLehn, Kurt ; Jones, Randolph M


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a247698.pdf


Report Date : Feb 1992


Pagination or Media Count : 61


Abstract : 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 example each time they refer to it. These findings, collectively called the self- explanation effect, have been reproduced by our cognitive simulation program, Cascade. Moreover, when Cascade is forced to explain exactly the parts of the examples 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. Learning, Problem-solving, Cognitive Modeling, Analogy, Explanation-based Learning.


Descriptors :   *PROBLEM SOLVING , *LEARNING , *COGNITION , BEHAVIOR , REASONING , ANALOGIES , TIME , SIMULATION , PARTS , HUMANS , STUDENTS , ANALOGS


Subject Categories : Psychology


Distribution Statement : APPROVED FOR PUBLIC RELEASE