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5 references
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- Vol 4 RE: Chapter 12
self-regulated learningmetacognitioncognitive regulationwinne–hadwin modelcopes frameworkmetacognitive monitoringstrategy adaptationlearning analyticsBook chapter Evidence summaryAt a glance
Winne describes self-regulated learning as a fusion of cognition, metacognition, motivation, and action rather than a separate study skill. The Winne–Hadwin model represents regulation through four recursively and flexibly sequenced phases: defining the task, setting goals and planning, enacting tactics and strategies, and adapting future work.
Key ideas
COPES identifies the conditions, operations, products, evaluations, and standards involved as learners interpret tasks and compare emerging products with goals. Metacognitive monitoring generates information about fit; control changes operations, effort, goals, or conditions. Because these events are conditional and unfold over time, retrospective questionnaires provide an incomplete picture. Winne argues for evidence from multiple channels and fine-grained traces of what learners do, particularly to study dynamic motivation and links between monitoring and control.
Why it matters
A practical implication is to help learners act as learning scientists: form hypotheses about strategies, collect evidence from their own performance, and adapt methods rather than merely follow decontextualized advice.
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- Vol 3 RE: Chapter 5
computer-supported collaborative learninglearner agencycollaborative interactionlearning analyticsadaptive supportresearch paradigmssocial media learningeducational changeJournal article Evidence summaryAt a glance
Wise and Schwarz consult the computer-supported collaborative learning community to provoke debate about the field’s identity and future. Eight paired dialogues present opposing positions rather than recommendations.
Key ideas
They question whether CSCL needs one unifying framework; whether learner agency should take priority over scripted collaboration; and how rigorously researchers should decide that collaboration or community exists. Further provocations ask whether analytic and interpretive traditions should be reconciled, whether computational approaches deserve greater emphasis, whether learning analytics and adaptive support should become priorities, and whether the field must engage social media and learning at scale. The final exchange challenges the ambition to produce educational-system change. Each “provocateur” is answered by a “conciliator” defending achievements and complexities in existing traditions.
Why it matters
The article arises from interviews and interactive presentations, not an empirical test, and intentionally offers no resolution. Its value is diagnostic: make assumptions about collaboration explicit, examine agency and power, justify units and methods of analysis, connect small-group theory with new scales, and decide how scholarly identity relates to educational impact.
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- Vol 4 RE: Chapters 12, 25, 27, App C
artificial intelligence chatbotsself-regulated learningadaptive guidanceco-regulationcritical reflectionlearner agencyemancipatory learninglearning analyticsJournal article Evidence summaryAt a glance
Wu and colleagues systematically review 78 studies published from 2018 to 2025 to ask how artificial-intelligence chatbots can be designed to support self-regulated learning. They organize design possibilities through Habermas’s technical, practical, and emancipatory knowledge interests.
Key ideas
Technical features can help learners set goals, obtain adaptive guidance, monitor progress, and receive timely feedback. Practical features emphasize dialogue, social interaction, shared understanding, and co-regulation rather than solitary tool use. Emancipatory features encourage learners to question assumptions, reflect critically, recognize power and bias, and retain agency over decisions. The resulting framework maps chatbot functions onto phases of self-regulation and shows that the literature concentrates more heavily on performance support than on deeper reflection or learner liberation. The review identifies gaps in K–12 settings, multimodal learning analytics, long-term evaluation, and designs that genuinely support emancipatory reflection.
Why it matters
Because the included studies vary in methods and outcomes, the synthesis is a design base rather than proof that any chatbot feature reliably improves achievement. Educators should align tools with learning purposes, provide human guidance, and evaluate both benefits and unintended dependency.
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- Vol 4 RE: Chapter 12
- Vol 4 RE: Chapter 6
- Vol 4 RE: Chapters 18, 25, App A, App C
artificial intelligenceself-regulated learningadaptive learning platformspersonalized guidancelearning analyticsai literacyteacher orchestrationJournal article Evidence summaryAt a glance
Zhu and Sari systematically review 25 empirical studies of artificial-intelligence applications intended to support self-regulated learning. The included tools span chatbots, intelligent tutoring systems, adaptive platforms, electronic books, and educational games, and address planning, performance, monitoring, reflection, and iteration in different ways.
Key ideas
The synthesis finds promising support for personalized guidance, feedback, engagement, and strategy use, but also recurring problems involving inaccurate outputs, limited adaptability, behavioral tracking, weak AI literacy, and possible negative effects or dependency. The authors propose an AI–SRL framework with two connected layers. The learner layer follows forethought, performance, self-reflection, and iteration; the instructor layer includes teaching AI literacy, integrating tools into purposeful activities, providing continuing support, monitoring progress, and improving designs over time. This makes teacher orchestration part of the model rather than assuming AI independently produces self-regulation.
Why it matters
The review is limited to English-language studies found in three databases and a still-small, heterogeneous evidence base. Its conclusions should guide careful design and further testing, not be read as proof that AI tools uniformly improve self-regulated learning.
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- Vol 4 RE: App C
self-regulation theorymetacognitive monitoringco-regulationlearning analyticsstrategy instructionsocially shared regulationmeasurement methodsEdited book Evidence summaryAt a glance
Zimmerman and Schunk’s edited handbook brings together theories, evidence, methods, and applications concerning how learners activate and sustain cognition, affect, motivation, and behaviour toward goals. Its chapters compare social-cognitive, cognitive, metacognitive, motivational, developmental, and socially shared perspectives rather than imposing one definition or mechanism.
Key ideas
Coverage includes the development of regulation, calibration and monitoring, co-regulation, measurement from self-report and traces, learning technologies, individual differences, and applications across academic subjects and performance domains. Contributors also examine how modelling, feedback, strategy instruction, practice, and social contexts can foster regulation. The consistent chapter structure links theoretical propositions with evidence, research needs, and educational implications. Because this is a multidisciplinary edited collection, findings vary in design, population, and strength; the volume is not evidence that a single generic self-regulation programme works everywhere.
Why it matters
Its educational value lies in treating regulation as learnable, situated, and temporally unfolding. Teachers and researchers should specify which phase and process they intend to support, align measures with those processes, combine data sources when possible, and distinguish productive independence from strategic use of peers, teachers, tools, and environments.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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