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dc.contributor.authorSullins, Jeremiah
dc.contributor.authorConsole, Katie
dc.contributor.authorDenton, Rebecca
dc.contributor.authorHenrichson, Clayton
dc.date.accessioned2018-11-04T23:35:55Z
dc.date.accessioned2018-11-04T22:41:31Z-
dc.date.available2018-11-04T23:35:55Z
dc.date.available2018-11-04T22:41:31Z-
dc.date.issued2018-07
dc.identifier.citationSullins, J., Console, K., Denton, R., & Henrichson, C. (2018). Identifying Methods to Induce Productive Confusion for Improving Performance in Physics. In Kay, J. and Luckin, R. (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 3. London, UK: International Society of the Learning Sciences.en_US
dc.identifier.urihttps://doi.dx.org/10.22318/cscl2018.1585
dc.identifier.urihttps://repository.isls.org//handle/1/748-
dc.description.abstractA gap currently exists in the literature regarding how to most effectively harness the power of confusion in learning. In order to address this gap, the present study sought to explore which methods of confusion induction are most beneficial for deep learning. Results revealed that Breakdown Scenarios were the most effective confusion induction technique compared to a lecture based format. Additionally, significant interactions were discovered between Breakdown Scenarios and the individual difference measures: Attributional Complexity and Goal Orientation. Immediate applications for education are discussed.en_US
dc.language.isoenen_US
dc.publisherInternational Society of the Learning Sciences, Inc. [ISLS].en_US
dc.titleIdentifying Methods to Induce Productive Confusion for Improving Performance in Physicsen_US
dc.typeBook chapteren_US
Appears in Collections:ICLS 2018

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