What multimodal data can tell us about the students’ regulation of their learning process?
Open accessAbstract
• New data-capturing devices allow researchers trace a range of cognitive and noncognitive processes. • We discuss and demonstrate how by data triangulating it is possible to study a number of features of regulated learning. • It is concluded that multimodal data can indicate critical features of regulated learning in collaboration.
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Multimodal learning analytics can strengthen inference by triangulating gaze with recorded action and performance evidence.
“Multimodal learning analytics can strengthen this inference by triangulating gaze with recorded action and performance evidence (Järvelä et al., 2021; Mohammadi et al., 2025).”
From Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions (2026) · cites this paper as Järvelä et al., 2021
Cites
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Kreibig, S. D. (2010) · Biological Psychology · doi:10.1016/j.biopsycho.2010.03.010 · Cited by 3,188 · cited 1× · see citing works
“It is not clear whether the changes in the SCL and NSSCRs are due to the students' high engagement, increased interest resulting in excitement or distraction, and/or fear due to task difficulty or fear of failure (Boucsein, 2012; Haapalainen et al., 2010; Kreibig, 2010).”
BackgroundBoucsein, W. (2012) · doi:10.1007/978-1-4614-1126-0 · Cited by 2,526 · see citing works
Handbook of Metacognition in Education
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High-level co-regulation in collaborative learning: How does it emerge and how is it sustained?
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The promises and challenges of artificial intelligence for teachers: A systematic review of research
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022) · TechTrends · doi:10.1007/s11528-022-00715-y · Cited by 984 · see citing works
Self-citationFan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13544 · Cited by 805 · see citing works
Assessment in the age of artificial intelligence
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Human and artificial intelligence collaboration for socially shared regulation in learning
Järvelä, S., Nguyen, A., & Hadwin, A. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13325 · Cited by 342 · see citing works
Self-citationMolenaar, I. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100070 · Cited by 217 · see citing works
Molenaar, I., Mooij, S. D., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107540 · Cited by 217 · see citing works
Self-citationUtilizing Social Clustering-Based Regression Model for Predicting Student’s GPA
Hassan, Y. M. I., Elkorany, A., & Wassif, K. (2022) · IEEE Access · doi:10.1109/access.2022.3172438 · Cited by 15 · see citing works
“These characteristics are represented through multimodal data, which refers to interaction traces that occur across multiple communication channels [26].”
BackgroundMa, J., Yue, H., & Li, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1946196
- Authors
- Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A.
- Affiliations
- University of Oulu (FI)
- Open University of the Netherlands (NL)
- Corresponding author
- Järvelä, Sanna
- Venue
- Learning and Instruction
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 72
- Pages
- 101203
- ISSN
- 0959-4752 (ISSN-L)
- OpenAlex
- W2944775932
- Cited by
- 163
- References
- 6
- Citations by year
- 2026: 23 · 2025: 24 · 2024: 25 · 2023: 29 · 2022: 30 · 2021: 17 · 2020: 10 · 2019: 3 · 2018: 2
- FWCI
- 25.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Learning and Instruction (publishedVersion, cc-by)
- DSpace (Open University in the Netherlands) (submittedVersion, cc-by)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Funders
- Academy of Finland
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- multimodal data, student self-regulation, data triangulation, cognitive processes, cooperative learning
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