Scaffolding Problem Solving with Annotated, Worked-Out Examples to Promote Deep Learning

This study compares the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples for learning college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered...

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Bibliographic Details
Published inIntelligent Tutoring Systems pp. 625 - 634
Main Authors Ringenberg, Michael A., VanLehn, Kurt
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Cumulative Grade Point Average
Deep Structure
Exact sciences and technology
General aspects
Grade Point Average
Homework Problem
Occupational training. Personnel. Work management
Software
Training Problem
User assistance
Computer assisted teaching
Cognitive development
Education
Knowledge transfer
Intelligent tutoring systems
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Summary:This study compares the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples for learning college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that providing examples was at least as effective as the hint sequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery.
ISBN:3540351590
9783540351597
ISSN:0302-9743
1611-3349
DOI:10.1007/11774303_62