Measuring secondary education students' self-regulated learning processes with digital trace data
Lämsä, Joni; de Mooij, Susanne; Aksela, Olli; Athavale, Shruti; Bistolfi, Inti; Azevedo, Roger; Bannert, Maria; Gasevic, Dragan; Molenaar, Inge; Järvelä, Sanna
Elsevier
Lämsä, J., De Mooij, S., Aksela, O., Athavale, S., Bistolfi, I., Azevedo, R., Bannert, M., Gasevic, D., Molenaar, I., & Järvelä, S. (2025). Measuring secondary education students’ self-regulated learning processes with digital trace data. Learning and Individual Differences, 118, 102625. https://doi.org/10.1016/j.lindif.2024.102625
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202501081073
https://urn.fi/URN:NBN:fi:oulu-202501081073
Tiivistelmä
Abstract
This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students (N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support.
This study investigates secondary education students' self-regulated learning (SRL) processes with digital trace data, particularly whether SRL processes found in secondary education are comparable to those observed in higher education. We therefore adapted a digital learning environment and rule-based AI algorithm originally designed to measure SRL in higher education and collected multi-trace data from 13-year-old students (N = 179) across three European countries during an essay-writing task. Hidden Markov modeling was employed to capture latent SRL processes. Four latent SRL processes emerged: orientation, first-reading, writing, and re-reading combined with monitoring. By clustering sequences of these latent SRL processes, we identified four sequential patterns of SRL processes at the task level: writing with metacognitive monitoring, writing intensively, reading first, writing next, and reading and writing simultaneously. Our findings highlight how AI and multi-trace data can be used to measure SRL during learning, providing a basis for enhancing personalized support.
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