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- Vol 4 RE: Chapters 6, 12, App C
learning analyticsself-regulated learning measurementmultimodal datadigital trace datatemporal alignmentlearner modelingphysiological signalsJournal article Evidence summaryAt a glance
Azevedo and Gašević examine the opportunities and difficulties involved in using multimodal, multichannel data to study self-regulated learning with advanced learning technologies.
Key ideas
Digital environments can capture fine-grained traces such as navigation, eye movements, dialogue, physiological signals, and performance, potentially revealing how regulation unfolds over time. Yet more data do not automatically produce valid inferences. Researchers must connect observable traces to a clear theory of regulation, align channels in time, distinguish meaningful processes from noise, and account for differences among learners and tasks. The article calls for collaboration across learning science, measurement, data science, and system design, together with transparent analytic decisions.
Why it matters
Its practical message is that learning analytics should be designed around educational questions rather than available sensors: evidence from multiple channels is valuable when it helps explain learners' goals, strategies, monitoring, and adaptation.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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