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2 references
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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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- Vol 7 RE: Chapter 9
student engagementlearning analyticsdigital trace databehavioural engagementcognitive engagementemotional engagementsocial engagementproxy validityJournal article Evidence summaryAt a glance
Bergdahl and colleagues systematically review how higher-education learning-analytics research conceptualises and measures student engagement through digital trace data.
Key ideas
Engagement is multidimensional, including behavioural, cognitive, emotional, and social participation, yet studies often equate it with easily counted actions such as logins, clicks, submissions, or time online. The review examines the dimensions, indicators, data sources, analytic methods, and theoretical frameworks used across the field. It warns that platform activity is an indirect proxy whose meaning depends on task, design, context, and complementary evidence; more activity is not necessarily deeper engagement.
Why it matters
For educators and analytics designers, the study recommends defining engagement before selecting data, aligning indicators with learning design, combining traces with qualitative or self-report evidence where appropriate, and avoiding interventions or labels that overinterpret what digital behaviour can establish.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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