Metacognitive calibration: a methodological expansion and empirical application
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Open access
Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
The ability to judge performance accurately is essential for successful learning. However, statistics or measures to do so are frequently limited to binary judgments and not scalable. Moreover, they primarily assess only one dimension of the metacognitive calibration accuracy. In this methodological paper, we develop and discuss a new set of statistics to determine the calibration accuracy and the direction of miscalibration. Together, they indicate the extent of confidence accuracy and whether learners are overconfident or underconfident in their judgments. These statistics are scalable to non-binary judgment data. We then illustrated them in an empirical study with 34 doctoral students’ performance judgment data which were assessed when answering domain-specific conceptual questions. Results from traditional measures were calculated, serving as a reference for the new measures’ reliability. In addition, we developed an R-package implementing and visualizing the latter. The theoretical and practical implications are discussed. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000600979Publication status
publishedBook title
Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023Pages / Article No.
Publisher
International Society of the Learning SciencesEvent
Subject
Metacognitive Calibration; Learning Sciences; MethodologyOrganisational unit
09590 - Kapur, Manu / Kapur, Manu
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ETH Bibliography
yes
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