Collaborative dialog while studying worked-out examples

Robert G M Hausmann, Timothy J. Nokes, Kurt VanLehn, Brett Van De Sande

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

6 Scopus citations


Self-explaining is a beneficial learning strategy for studying worked-out examples because it either supplies missing information through the generation of inferences or because it provides a mechanism for repairing flawed mental models. Although self-explanation is generated with the purpose of helping the individual, is it also helpful to produce explanations in a collaborative setting? Can individuals help each other infer missing information or repair their flawed mental models collaboratively? To find out, we coded the dialog from dyads collaboratively studying examples and contrasted it with individuals studying examples alone. The results suggest that dyads were more likely to attempt to reconcile the examples with their attempted solutions, and avoid shallow processing of examples through paraphrasing.

Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Number of pages3
ISBN (Print)9781607500285
StatePublished - Jan 1 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


  • Peer collaboration
  • Physics
  • Prior knowledge
  • Self-explanation

ASJC Scopus subject areas

  • Artificial Intelligence


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