Revising learner misconceptions without feedback: Prompting for reflection on anomalies

Joseph Jay Williams, Tania Lombrozo, Anne Hsu, Bernd Huber, Juho Kim

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

10 Scopus citations

Abstract

The Internet has enabled learning at scale, from Massive Open Online Courses (MOOCs) to Wikipedia. But online learners may become passive, instead of actively constructing knowledge and revising their beliefs in light of new facts. Instructors cannot directly diagnose thousands of learners' misconceptions and provide remedial tutoring. This paper investigates how instructors can prompt learners to reflect on facts that are anomalies with respect to their existing misconceptions, and how to choose these anomalies and prompts to guide learners to revise incorrect beliefs without any feedback. We conducted two randomized experiments with online crowd workers learning statistics. Results show that prompts to explain why these anomalies are true drive revision towards correct beliefs. But prompts to simply articulate thoughts about anomalies have no effect on learning. Furthermore, we find that explaining multiple anomalies is more effective than explaining only one, but the anomalies should rule out multiple misconceptions simultaneously.

Original languageEnglish (US)
Title of host publicationCHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages470-474
Number of pages5
ISBN (Electronic)9781450333627
DOIs
StatePublished - May 7 2016
Externally publishedYes
Event34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose, United States
Duration: May 7 2016May 12 2016

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Other

Other34th Annual Conference on Human Factors in Computing Systems, CHI 2016
CountryUnited States
CitySan Jose
Period5/7/165/12/16

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Keywords

  • Explanation
  • MOOCs
  • Online learning
  • Prompts

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  • Cite this

    Williams, J. J., Lombrozo, T., Hsu, A., Huber, B., & Kim, J. (2016). Revising learner misconceptions without feedback: Prompting for reflection on anomalies. In CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems (pp. 470-474). (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/2858036.2858361