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Calibration Discrepancy Predicts Students' Subsequent Metacognitive Strategy Use in Computer-Based Learning Environments

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Title: Calibration Discrepancy Predicts Students' Subsequent Metacognitive Strategy Use in Computer-Based Learning Environments
Language: English
Authors: HaeJin Lee, Nigel Bosch
Source: International Journal of Artificial Intelligence in Education. 2025 35(6):3746-3779.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 34
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Predictor Variables, Metacognition, Learning Strategies, Computer Assisted Instruction, Evaluative Thinking, Accuracy, Test Wiseness, Tests, Electronic Learning, Pretesting, Feedback (Response)
DOI: 10.1007/s40593-025-00514-5
ISSN: 1560-4292
1560-4306
Abstract: Students often misjudge their understanding of learning material, which can lead to the use of ineffective learning strategies and result in suboptimal learning outcomes. However, it remains unclear how misjudgments relate to the use of metacognitive strategies in online learning settings, which is essential context for developing effective interventions that support students in making (and using) accurate judgments of their performance. To address this, we analyze data from 210 college students using a computer-based learning environment, investigating the relationships among calibration discrepancy, judgments, and strategies, as well as the factors affecting shifts in metacognitive judgments during learning. Students who overestimated their pretest retrospective judgments engaged less in metacognitive strategies, particularly in preparatory actions before quizzes (b = -9.100, p < 0.001). Notably, pretest retrospective judgments--rather than actual pretest scores--significantly predicted students' engagement in these metacognitive strategies (b = -9.841, p < 0.001). Furthermore, increased engagement in repeated quiz-taking was a significant negative predictor of changes in metacognitive judgments (b = -1.792, p = 0.036), indicating that students engaging in repeated quizzes tended to adjust their judgments more conservatively. These results highlight the role of pretest retrospective judgments in shaping engagement with metacognitive strategies, underscoring the importance of correcting early calibration discrepancies. Our findings advocate for early, proactive metacognitive support tools that go beyond merely presenting information, offering guidance on interpreting feedback, and implementing strategies to better align students' judgments with their actual performance.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1500082
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Students often misjudge their understanding of learning material, which can lead to the use of ineffective learning strategies and result in suboptimal learning outcomes. However, it remains unclear how misjudgments relate to the use of metacognitive strategies in online learning settings, which is essential context for developing effective interventions that support students in making (and using) accurate judgments of their performance. To address this, we analyze data from 210 college students using a computer-based learning environment, investigating the relationships among calibration discrepancy, judgments, and strategies, as well as the factors affecting shifts in metacognitive judgments during learning. Students who overestimated their pretest retrospective judgments engaged less in metacognitive strategies, particularly in preparatory actions before quizzes (b = -9.100, p &lt; 0.001). Notably, pretest retrospective judgments--rather than actual pretest scores--significantly predicted students&#39; engagement in these metacognitive strategies (b = -9.841, p &lt; 0.001). Furthermore, increased engagement in repeated quiz-taking was a significant negative predictor of changes in metacognitive judgments (b = -1.792, p = 0.036), indicating that students engaging in repeated quizzes tended to adjust their judgments more conservatively. These results highlight the role of pretest retrospective judgments in shaping engagement with metacognitive strategies, underscoring the importance of correcting early calibration discrepancies. Our findings advocate for early, proactive metacognitive support tools that go beyond merely presenting information, offering guidance on interpreting feedback, and implementing strategies to better align students&#39; judgments with their actual performance.
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