Scaffolding problem solving with annotated, worked-out examples to promote deep learning

Michael A. Ringenberg, Kurt VanLehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 8th International Conference, ITS 2006, Proceedings
PublisherSpringer Verlag
Pages625-634
Number of pages10
ISBN (Print)3540351590, 9783540351597
DOIs
StatePublished - Jan 1 2006
Externally publishedYes
Event8th International Conference on Intelligent Tutoring Systems, ITS 2006 - Jhongli, Taiwan, Province of China
Duration: Jun 26 2006Jun 30 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4053 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Intelligent Tutoring Systems, ITS 2006
CountryTaiwan, Province of China
CityJhongli
Period6/26/066/30/06

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ringenberg, M. A., & VanLehn, K. (2006). Scaffolding problem solving with annotated, worked-out examples to promote deep learning. In Intelligent Tutoring Systems - 8th International Conference, ITS 2006, Proceedings (pp. 625-634). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4053 LNCS). Springer Verlag. https://doi.org/10.1007/11774303_62