Do Instrumentation Tools Capture Self-Regulated Learning?
Open accessAbstract
Researchers have been struggling with the measurement of Self-Regulated Learning (SRL) for decades. Instrumentation tools have been proposed to help capture SRL processes that are difficult to capture. The aim of the present study was to improve measurement of SRL by embedding instrumentation tools in a learning environment and validating the measurement of SRL with these instrumentation tools using think aloud. Synchronizing log data and concurrent think aloud data helped identify which SRL processes were captured by particular instrumentation tools. One tool was associated with a single SRL process: the timer co-occurred with monitoring. Other tools co-occurred with a number of SRL processes, i.e., the highlighter and note taker captured superficial writing down, organizing, and monitoring, whereas the search and planner tools revealed planning and monitoring. When specific learner actions with the tool were analyzed, a clearer picture emerged of the relation between the highlighter and note taker and SRL processes. By aligning log data with think aloud data, we showed that instrumentation tool use indeed reflects SRL processes. The main contribution is that this paper is the first to show that SRL processes that are difficult to measure by trace data can indeed be captured by instrumentation tools such as high cognition and metacognition. Future challenges are to collect and process log data real time with learning analytic techniques to measure ongoing SRL processes and s…
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The instrumentation tools used in the learning environment have proven to be effective in supporting and measuring SRL.
“These instrumentation tools have proven to be effective in supporting and measuring SRL (van der Graaf et al., 2021).”
From Towards reliable generative AI-driven scaffolding: Reducing hallucinations and enhancing quality in self-regulated learning support (2026) · cites this paper as van der Graaf et al., 2021
Cites
A Review of Self-regulated Learning: Six Models and Four Directions for Research
Panadero, E. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00422 · Cited by 2,915 · cited 1× · see citing works
“SRL is a dynamic and cyclical process that unfolds over time with interactions across and within cognitive and metacognitive activities [26].”
BackgroundMeasuring Self-Regulated Learning
Winne, P. H., & Perry, N. E. (2000) · Handbook of Self-Regulation · doi:10.1016/b978-012109890-2/50045-7 · Cited by 1,383 · see citing works
A metacognitive view of individual differences in self-regulated learning
Winne, P. H. (1996) · Learning and Individual Differences · doi:10.1016/s1041-6080(96)90022-9 · Cited by 486 · see citing works
Azevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008) · Educational Technology Research and Development · doi:10.1007/s11423-007-9067-0 · Cited by 380 · see citing works
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Assessment in the age of artificial intelligence
Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100075 · Cited by 409 · see citing works
Towards hybrid <scp>human‐AI</scp> learning technologies
Molenaar, I. (2022) · European Journal of Education · doi:10.1111/ejed.12527 · Cited by 348 · 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
Self-citationMolenaar, 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-citationChen, A., Xiang, M., Zhou, J., Jia, J., Shang, J., Li, X., Gašević, D., & Fan, Y. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105198 · Cited by 115 · see citing works
Self-citationTowards investigating the validity of measurement of self-regulated learning based on trace data
Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., Moore, J., Molenaar, I., Bannert, M., & Gašević, D. (2022) · Metacognition and Learning · doi:10.1007/s11409-022-09291-1 · Cited by 101 · see citing works
Self-citationQian, K., Liu, S., Li, T., Raković, M., Li, X., Guan, R., Molenaar, I., Nawaz, S., Swiecki, Z., Yan, L., et al. (2026) · Computers & Education · doi:10.1016/j.compedu.2025.105448 · Cited by 39 · see citing works
Self-citationHan, B., Nawaz, S., Buchanan, G., & McKay, D. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00456-4 · Cited by 32 · see citing works
- Authors
- van der Graaf, J., Lim, L., Fan, Y., Kilgour, J., Moore, J., Bannert, M., Gasevic, D., & Molenaar, I.
- Affiliations
- Radboud University, Netherlands
- Technical University of Munich (DE)
- University of Edinburgh (GB)
- Monash University, Australia
- Radboud University Nijmegen (NL)
- Venue
- LAK21: 11th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2021-04-12
- Pages
- 438-448
- OpenAlex
- W3140039345
- Cited by
- 44
- References
- 4
- Citations by year
- 2026: 1 · 2025: 16 · 2024: 9 · 2023: 6 · 2022: 10 · 2021: 2
- FWCI
- 21.74
- Type
- conference paper
- Language
- en
- Open access
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- HAL (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- NWO (NWO464.18.104)
- ESRC (ES/S015701/1)
- DFG (BA20144)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- student self-regulation, think-aloud, logs, learning analytics, metacognition, planning, monitoring, note-taking
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