Targeting Metacognition by Incorporating Student-Reported Confidence Estimates on Self-Assessment Quizzes

Priscilla Lee, Soohyun Nam Liao

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

Abstract

Being able to accurately self-assess one's own understanding is a crucial metacognitive skill that enables students to allocate their study time and energy more effectively. Prior works have explored different metacognition-based interventions but they were either not reliably effective or heavy-weight. In this work, we present Compass, an intervention composed of self-assessment quizzes that additionally ask students to self-report their confidence level per answer in order to automatically recommend prioritized sets of resources. We found that although frequent self-assessment quiz taking correlated with higher exam performance, the repeated practice of self-reporting confidence levels did not seem to benefit students' metacognitive accuracy over time. Our findings also challenge the commonly accepted hypothesis that high-performing students have high metacognitive accuracy.

Original languageEnglish (US)
Title of host publicationSIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery, Inc
Pages431-437
Number of pages7
ISBN (Electronic)9781450380621
StatePublished - Mar 3 2021
Event52nd ACM Technical Symposium on Computer Science Education, SIGCSE 2021 - Virtual, Online, United States
Duration: Mar 13 2021Mar 20 2021

Publication series

NameSIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education

Conference

Conference52nd ACM Technical Symposium on Computer Science Education, SIGCSE 2021
Country/TerritoryUnited States
CityVirtual, Online
Period3/13/213/20/21

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Education

Keywords

  • confidence
  • high performers
  • low performers
  • metacognition
  • self-assessment

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