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10 references
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- Vol 4 RE: Chapter 6
self-regulated learningmetacognitive monitoringcue utilizationcalibration of judgmentsmetacognitive promptsscaffold fadingdiagnostic feedbackJournal article Evidence summaryAt a glance
Van Merriënboer and de Bruin use a cue-utilization framework to explain how learners monitor and control their own learning. Their central point is that judgments are only as useful as the cues on which learners base them: familiarity, ease, effort, affect, or task features may be available, but they do not all predict later performance equally well.
Key ideas
Reviewing seven contributions to a special issue, the authors argue that instruction should help learners notice and use more diagnostic cues, make better control decisions, and address the full learning cycle rather than monitoring or reflection alone. They propose metacognitive prompts and “second-order” scaffolding that gradually fades as self-regulation develops. They also identify affective states as potentially informative cues whose interaction with cognitive cues remains insufficiently understood. The article is a conceptual synthesis, not an intervention trial.
Why it matters
For practice, it suggests designing prompts, feedback, task sequences, and technology around evidence that helps students calibrate judgments and choose effective next steps, while progressively transferring control to the learner.
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- Vol 4 RE: Chapters 6, 12
metacognitive regulationself-regulated learningcomprehension monitoringstrategy selectionerror diagnosisthink-aloud protocolscalibrated independenceBook chapter Evidence summaryAt a glance
Veenman examines how learners monitor and regulate cognition, clarifying differences between metacognition and self-regulated learning. Regulation includes orienting to a task, planning, monitoring comprehension and progress, checking results, diagnosing errors, choosing or changing strategies, and evaluating performance.
Key ideas
Learners often possess relevant knowledge yet fail to deploy it conditionally and in sequence; inaccurate monitoring then prevents effective control. Skills are best studied during task performance because questionnaires about typical behaviour may not correspond to on-line regulation. Think-aloud protocols, observations, log files, traces, and performance measures reveal different processes and should be triangulated. Instruction is most effective when regulatory actions are explicitly modelled and explained, practised within subject matter over time, prompted at appropriate moments, followed by feedback, and gradually transferred to learners. Merely telling students to “be metacognitive” or providing decontextualised study tips is unlikely to create a coherent programme of self-instructions.
Why it matters
Teachers also need to distinguish a subject-matter gap from a regulation problem. The chapter’s practical aim is calibrated independence: learners should notice task demands, accurately judge their state, select appropriate actions, and evaluate their consequences without external prompting.
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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 4 RE: Chapter 11
self-regulated learning measurementaptitude measuresevent measuresthink-aloud protocolsbehavioural tracesretrospective self-reportmeasurement validitymetacognitive assessmentBook chapter Evidence summaryAt a glance
Winne and Perry examine measurement of self-regulated learning and distinguish two conceptions. Aptitude measures treat regulation as a relatively enduring characteristic and commonly use questionnaires, interviews, or teacher ratings.
Key ideas
Event measures examine regulation as it unfolds in a task through think-aloud protocols, observations, error-detection methods, work products, or behavioural traces. The chapter relates these approaches to metacognition, motivation, monitoring, and strategic action, then analyses what each protocol samples, permitted inferences, and how measurement may affect activity. Retrospective self-report is efficient but depends on memory, interpretation, and aggregation across situations; event data are closer to action but may capture only observable indicators and require demanding coding. No method reveals an entire regulatory process. The chapter is a methodological review rather than validation of one superior instrument.
Why it matters
Researchers and educators should define the process and timescale of interest, align claims with what the measure records, combine complementary sources where feasible, preserve task context, and distinguish reported beliefs about strategy use from evidence that a strategy occurred. Assessment quality depends on defensible inference, not technological detail or questionnaire popularity.
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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 25
generative artificial intelligenceself-regulated learningforethought processesperformance processesself-reflectionpersonalized goalsstrategy generationhuman–machine collaborationJournal article Evidence summaryAt a glance
Xia, Liu, Tlili, and Chiu systematically review how generative AI can support self-regulated learning activities.
Key ideas
They analyze seventy-three articles and map GenAI use across forethought, performance, and self-reflection. Six pedagogical affordances emerge: creating personalized goals; searching, analyzing, and integrating resources; monitoring and evaluating progress; recommending strategies; recording progress and providing feedback; and generating new ideas or examples. Common activities include information searching during forethought, strategy generation for problem solving during performance, and feedback and self-assessment during reflection. The review also separates individual influences, such as prior knowledge and motivation, from environmental influences, including teacher support and task design.
Why it matters
Its central design message is that GenAI should function as a human–machine collaborative scaffold for regulation, not simply an answer generator, and that activities must preserve learner monitoring, judgement, and agency.
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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: Chapters 1, 2
self-regulated learningsocial cognitive theoryself-efficacyself-observationself-judgementself-reactionreciprocal causationJournal article Evidence summaryAt a glance
Zimmerman presents academic self-regulation as a dynamic interaction among personal beliefs, behaviour, and environment rather than a fixed learner trait. Within this social-cognitive account, students regulate learning through self-observation, self-judgement, and self-reaction.
Key ideas
They monitor aspects of performance, compare them with goals or standards, and respond in ways that can sustain or alter subsequent effort and strategy use. Self-efficacy is central because beliefs about capability influence goal choice, persistence, monitoring, and reactions to results. Regulation is reciprocally caused: students shape study environments and behaviours, while feedback from those environments changes beliefs and later action. The article integrates theory and existing evidence; it is not a single classroom experiment establishing one intervention’s effect. It also avoids equating self-regulation with solitary learning, because models, feedback, rewards, and social support contribute to its development.
Why it matters
Educationally, teachers can make goals and criteria explicit, model strategic processes, support accurate monitoring, provide informative feedback, and help learners interpret errors as information for adaptation. Assessment should attend to processes and changing contexts, not infer a stable capacity solely from an outcome score or retrospective questionnaire.
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- Vol 4 RE: Chapters 1, 2, 4, 8, 13, 25, App A, App C
- Vol 4 TE: App C
- Vol 5 RE: Chapters 2, 3, 5, 6, 13, 14, 15, App B
- Vol 5 TE: App C
- Vol 7 PE: App D
- Vol 7 RE: Chapters 2, 7, App D
self-regulated learningforethought phaseperformance phaseself-reflectiongoal settingstrategic planningcausal attributionBook chapter Evidence summaryAt a glance
Zimmerman defines self-regulation as context-sensitive processes through which people pursue goals, not as an inherited stage, general trait, or purely metacognitive skill. His social-cognitive model is cyclical.
Key ideas
Forethought includes task analysis, goal setting, strategic planning, self-efficacy, outcome expectations, interest, and goal orientation. During performance, learners deploy strategies, manage attention, use self-instruction or imagery, structure environments, and observe their actions and conditions. Self-reflection then combines self-evaluation and causal attribution with satisfaction and adaptive or defensive reactions. These reactions feed the next cycle, so interpretations of success and failure influence later goals, effort, and strategy choice. The chapter also distinguishes proactive regulation, in which learners set goals and create favourable conditions, from reactive attempts made only after difficulty occurs. Social models, instruction, feedback, and guided practice contribute to development. This is a theoretical synthesis of research, not one intervention trial.
Why it matters
For teaching, it supports modelling strategies, helping students set specific proximal goals, making performance information visible, prompting defensible attributions, and revising approaches after reflection. Difficulties should be diagnosed across the cycle rather than attributed to motivation or ability alone.
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- Vol 2 RE: Chapter 14, App A, App D
- Vol 2 TE: App B
- Vol 3 RE: Chapter 14
- Vol 4 RE: Chapters 1, 2, 4, 8, 10, 15, 16, 17, 19, 26, 27, App A, App B, App C
- Vol 4 TE: App C
self-regulated learninggoal settingstrategy adaptationself-monitoringhelp seekingtime managementguided practiceJournal article Evidence summaryAt a glance
Zimmerman offers an overview of self-regulated learning as self-generated thoughts, feelings, and behaviours directed toward goals. Self-regulated learners set goals, adapt strategies, monitor progress, manage time and environments, seek assistance, and evaluate outcomes.
Key ideas
The cyclical model has three phases. Forethought covers goal setting, planning, efficacy beliefs, and motivation; performance includes self-control and self-observation; self-reflection includes evaluation, attribution, satisfaction, and decisions shaping the next attempt. Expertise involves strategic adaptation rather than independence from help. The article contrasts effective regulation with difficulties such as vague goals, inaccurate monitoring, defensive attributions, and failure to adjust methods. It reviews theory and evidence rather than testing one classroom programme. Zimmerman stresses that self-regulatory processes can be taught through modelling, guided practice, feedback, and gradual transfer of responsibility.
Why it matters
Educators can support development by making strategies and standards visible, asking students to record process and outcomes, teaching time and environment management, normalising strategic help seeking, and prompting reflection linking results to controllable methods. One questionnaire score cannot capture a process that changes across tasks and contexts.
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Evidence summaries explain the central idea and why the source matters to educators.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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