The measurement of calibration in real contexts |
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| Institution: | 1. Faculty of Education, University of Erfurt, Postfach 900221, 99105 Erfurt, Germany;2. Department of Learning, Instruction, and Teacher Education, Faculty of Education, University of Haifa, 199 Abba Khoushy Ave., Mount Carmel, Haifa 3498838, Israel;3. Department of Psychology, Westfälische Wilhelms-Universität Münster, Fliednerstrasse 21, 48149 Muenster, Germany;1. Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;2. Universität Erlangen-Nürnberg, Bismarckstraße 1, 91054 Erlangen, Germany;1. Curtin University, Kent Street, Bentley 6102, Australia;2. University of Burgundy, Esplanade Erasme, 21078 Dijon, France;1. Academic Affairs and Assessment, Sullivan University College of Pharmacy, Louisville, KY;2. Department of Clinical and Administrative Sciences, Sullivan University College of Pharmacy, Louisville, KY;1. Department of Education, Utrecht University, The Netherlands;2. Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, The Netherlands;3. Learning and Innovation Centre, Avans University of Applied Sciences, Breda, The Netherlands;4. Early Start Research Institute, University of Wollongong, Australia |
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| Abstract: | Accurate judgment of performance, or calibration, is an important element of self-regulated learning (SRL) and itself has been an area of growing study. The current study contributes to work on calibration by presenting practical and predictive results of varying calibration measures from authentic educational data: elementary-aged students' interactions with a year-long digital mathematics curriculum. Comparison of predictive validity of measures show only small differences in explained variance in models predicting posttest performance while controlling for pretest. A combined model including Sensitivity and Specificity outperforms other single measures, confirming results in Schraw, Kuch, & Gutierrez (2013); however, results show that student patterns of calibration within these data differ from those assumed in simulation studies and these differences have implications for the calculability of popular calibration measures. |
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| Keywords: | Metacognitive monitoring Accuracy Calibration Data Self-regulated learning |
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