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2 references
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- Vol 4 RE: Chapter 6
self-regulated learningsocially shared regulationcollaborative learningadaptive learning environmentslearning analyticsfeedback scaffoldinghelp seekinglearner agencyJournal article Evidence summaryAt a glance
Sharma, Nguyen, and Hong systematically review empirical studies connecting adaptive digital learning environments with self-regulated and socially shared regulation in collaborative learning. Their search and coding identify seven recurring objectives: feedback and scaffolding, regulatory skills and strategies, learning trajectories, collaborative processes, adaptation and regulation, self-assessment, and help seeking.
Key ideas
Adaptive systems can personalize prompts, representations, feedback, and paths, while collaboration introduces collective planning, monitoring, control, and reflection that cannot be inferred from individual traces alone. The literature uses heterogeneous theoretical models, settings, technologies, and measures, limiting cumulative conclusions. Important gaps include few informal-learning studies, weak theoretical convergence between individual and shared regulation, and difficulty monitoring how regulation is coordinated across group members. The authors call for clearer constructs, multimodal and process-sensitive measures, and designs that support rather than automate learner agency.
Why it matters
The review provides a map of opportunities but does not establish that adaptation by itself improves regulation or collaboration.
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- Vol 5 RE: Chapters 10, 13, 15, App A
learning analyticseducational data miningpredictive modelinghuman sense-makingactionable analyticsdata-informed decision makingprivacylearner agencyConference paper Evidence summaryAt a glance
Siemens and Baker compare learning analytics and educational data mining, two overlapping communities using educational data to understand and improve learning. Educational data mining had tended to emphasise automated discovery, modelling, prediction, and adaptation at the level of learners and software, drawing strongly from computer science.
Key ideas
Learning analytics had placed relatively greater emphasis on human interpretation, sense-making, organisational decision making, social networks, and the needs of instructors and institutions. The authors argue that the differences are productive but that limited communication risks duplicated work and fragmented standards. They propose collaboration around shared research methods, tools, data, theory, conferences, and ethical challenges while preserving complementary perspectives. The short position paper does not present a new empirical evaluation; its contribution is field-building and agenda setting. For educational practice, analytics should connect technically credible models with actionable human judgement.
Why it matters
Predictions alone do not improve learning, and dashboards without theory or intervention can mislead. Researchers and institutions need transparent definitions, validation across contexts, attention to privacy and agency, and evaluation of whether data-informed actions actually benefit learners.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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