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- Vol 4 RE: Chapter 6
self-regulated learningmultimodal dataartificial intelligencetemporal process analysisaffective measurementconstruct validityprivacy in learning analyticsJournal article Evidence summaryAt a glance
De Mooij and colleagues systematically review how multimodal data and artificial intelligence have been used to study self-regulated learning. They map studies onto a grid connecting cognitive, affective, metacognitive, and motivational processes with behavioural, contextual, physiological, and other data streams.
Key ideas
The review distinguishes unimodal, horizontal, vertical, and integrated analytic approaches and examines whether methods capture regulation as a temporal and sequential process across learning contexts. Research is moving beyond single self-report measures toward combinations such as log data, gaze, discourse, facial or physiological signals, and performance evidence. However, motivation and affect remain underrepresented, studies define and align modalities inconsistently, and conventional statistics are often used even when integrated temporal analysis is needed. AI offers possibilities for pattern detection but does not remove problems of construct validity, interpretation, privacy, or context.
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The authors call for theoretically grounded measurement, transparent alignment of indicators with processes, stronger validation, and interdisciplinary designs that connect rich sensing with educationally meaningful support.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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