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9 references
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- Vol 7 RE: Chapter 7, App D
self-regulated learningmetacognitive regulationhypermedia learninggoal settingcomprehension monitoringstrategy selectionadaptive scaffoldingJournal article Evidence summaryAt a glance
Azevedo reviews research on learning complex science topics with open-ended hypermedia and argues that such environments become useful metacognitive tools only when they support self-regulated learning.
Key ideas
Learners must coordinate prior knowledge, planning, monitoring, strategy use, motivation, and adaptation while navigating nonlinear information. Studies from the author's laboratory and classrooms show that learners of different ages often struggle to deploy these processes spontaneously. The article uses self-regulated learning as a framework for analysing both learning outcomes and the moment-by-moment processes that produce them. It also considers how prompts, human tutors, and adaptive computer scaffolds can model or support productive regulation.
Why it matters
The main implication is that access to rich digital resources does not by itself ensure deep learning: effective environments must help students set goals, activate relevant knowledge, monitor understanding, choose strategies, and revise their approach when comprehension breaks down.
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- Vol 4 RE: Chapter 11
- Vol 7 PE: App D
- Vol 7 RE: Chapter 7, App D
self-regulated learning traininghypermedia learningmental model changemetacognitive strategiesverbal protocolsconceptual understandingJournal article Evidence summaryAt a glance
Azevedo and Cromley tested whether explicit self-regulated learning training improves learning from hypermedia. They randomly assigned 131 undergraduates to a training or control condition before students used a hypermedia environment to learn about the circulatory system.
Key ideas
The intervention provided 30 minutes of instruction in specific, research-based regulatory processes; the control group received no such training. Pretests, posttests, and verbal protocols captured both conceptual change and learners' activity during study. Students who received training showed significantly greater shifts toward more sophisticated mental models. Their verbal protocols connected this advantage to use of the strategies taught in training.
Why it matters
The study demonstrates that learners do not necessarily regulate effectively merely because a digital environment permits flexible navigation. Brief, targeted instruction can change how students plan, monitor, and process information, with measurable effects on conceptual understanding.
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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 5 RE: Chapter 8
artificial intelligenceself-regulated learninglearner agencylearning analyticsadaptive feedbacktheoretical alignmentethical reportingJournal article Evidence summaryAt a glance
This systematic mapping review analyses 84 studies at the intersection of artificial intelligence and self-regulated learning.
Key ideas
Using a framework organised around who is involved, what theories and constructs are addressed, how the research is conducted, and why AI is applied, the authors map publication patterns, stakeholders, methods, technologies, and objectives. The review finds rapid growth but uneven theoretical grounding and limited attention to some phases and actors in self-regulation. Much work focuses on higher education and on using learner data to predict, recommend, prompt, or provide feedback. The authors call for clearer links between AI functions and established self-regulated-learning theory, broader contexts, stronger ethical and methodological reporting, and more attention to learner agency.
Why it matters
For educators, the review offers a structured way to judge whether an AI tool genuinely supports planning, monitoring, reflection, or adaptation.
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- Vol 5 RE: Chapters 6, 7, 14
self-regulated learningsubjective fluencyjudgments of learningretrieval practicespaced practicemetacognitive calibrationfeedback useJournal article Evidence summaryAt a glance
Bjork, Dunlosky, and Kornell review why learners often mismanage their own study.
Key ideas
People commonly use immediate performance and subjective fluency as evidence of learning, even though ease during practice may reflect short-lived accessibility rather than durable memory. Beliefs about learning influence strategy selection, and judgments of learning can be distorted by rereading, answer availability, recent success, or familiar presentation. Effective self-regulation requires distinguishing learning from performance, scheduling retrieval and spacing, using tests as learning events, learning from errors with feedback, and monitoring under conditions that resemble future use. The authors also examine educational and cultural practices that reward rapid, error-free performance and thereby reinforce illusions.
Why it matters
For educators, the review supports explicitly teaching how memory works, delaying judgments, using cumulative retrieval, and helping students compare predictions with later outcomes so they can calibrate study decisions.
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- Vol 4 RE: Chapters 2, 5
self-regulated learningthree-layer modelmetacognitive regulationcognitive strategiesgoal orientationemotional regulationself-protectionJournal article Evidence summaryAt a glance
Boekaerts integrates three traditions that shaped research on self-regulated learning: learning styles and cognitive processing, metacognition and regulation of learning, and theories of goals, motivation, and the self.
Key ideas
Her three-layer model distinguishes regulation of processing modes at the inner layer, regulation of the learning process at the middle layer, and regulation of the self at the outer layer. Learners select cognitive strategies, plan and monitor activity, and decide whether a task serves valued goals or threatens well-being. These layers interact, so knowing a strategy does not guarantee its use when motivation, emotion, or competing goals redirect effort.
Why it matters
For educators, the model supports teaching strategies and metacognitive control while also attending to appraisal, confidence, interest, and classroom conditions that influence whether students enter a growth-oriented learning pathway or protect the self by withdrawing.
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- Vol 5 RE: Chapter 15
sustainable assessmentformative assessmentself-regulated learningevaluative judgementfeedback literacyassessment designself-assessmentcalibrationJournal article Evidence summaryAt a glance
Boud and Soler revisit sustainable assessment: assessment designed not only to support current coursework but also to develop learners' capacity to meet future learning demands.
Key ideas
Reviewing how the idea evolved over fifteen years, they connect it with formative assessment, self-regulation, feedback, and students' ability to make informed judgements about the quality of their own work. Sustainable practice cannot depend indefinitely on teachers supplying corrective information; learners need opportunities to interpret standards, compare work with criteria, seek and use feedback, and calibrate their decisions. The authors argue that these capacities must be built across a programme rather than attached to isolated tasks.
Why it matters
For educators, the article recommends examining how assessment design contributes to long-term learning, creating repeated opportunities for evaluative judgement, and aligning present requirements with the kinds of decisions students will later make independently.
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- Vol 7 RE: Chapter 8
self-regulated learningonline higher educationtime managementmetacognitioneffort regulationcritical thinkingpeer learningacademic achievementJournal article Evidence summaryAt a glance
Broadbent and Poon systematically review studies linking self-regulated learning strategies with achievement in fully online higher education. Twelve studies met their criteria.
Key ideas
Time management, metacognition, effort regulation, and critical thinking showed the most consistent positive relationships with academic outcomes; rehearsal, elaboration, and organisation had weaker support. Peer learning showed a moderate positive association, but its confidence interval included zero. The authors caution that the small and varied evidence base limits firm causal conclusions and that relationships found in face-to-face settings may be weaker online. For educators and course designers, the review highlights the need to make planning, monitoring, persistence, and time use visible and teachable rather than assuming online learners will regulate themselves.
Why it matters
It also points toward designing prompts, schedules, progress cues, and support that help students sustain productive strategies.
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- Vol 4 RE: Chapter 9
- Vol 4 TE: App C
- Vol 5 RE: Chapters 1, 3, 5, 6, 10, 15, App A
- Vol 5 TE: App C
self-regulated learningfeedback interpretationmetacognitive monitoringinternal feedbackexternal feedbackfeedback effectslearning strategiesJournal article Evidence summaryAt a glance
Butler and Winne integrate research on feedback with a cognitive model of self-regulated learning.
Key ideas
Learners interpret tasks using knowledge, beliefs, goals, and motivational conditions; select tactics and strategies; generate products; and monitor the relationship between their current state and internal standards. Monitoring creates internal feedback, while external feedback from teachers, peers, or tasks can confirm, challenge, or supplement those judgements. Because learners filter feedback through prior beliefs and knowledge, information does not automatically produce improvement and may prompt changes to goals, strategies, effort, or understanding. The synthesis places monitoring at the centre of recursive engagement and explains why feedback effects vary with task and learner.
Why it matters
For educators, it suggests designing feedback that helps students compare work with criteria, diagnose causes, select strategies, and monitor subsequent performance, while explicitly developing the knowledge and beliefs required to interpret feedback productively.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
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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