@inproceedings{a9684f0977f2485aa26bc86b97e12713,
title = "Targeting Metacognition by Incorporating Student-Reported Confidence Estimates on Self-Assessment Quizzes",
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.",
keywords = "confidence, high performers, low performers, metacognition, self-assessment",
author = "Priscilla Lee and Liao, {Soohyun Nam}",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 52nd ACM Technical Symposium on Computer Science Education, SIGCSE 2021 ; Conference date: 13-03-2021 Through 20-03-2021",
year = "2021",
month = mar,
day = "3",
doi = "10.1145/3408877.3432377",
language = "English (US)",
series = "SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education",
publisher = "Association for Computing Machinery, Inc",
pages = "431--437",
booktitle = "SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education",
}