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4 references
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- Vol 4 RE: Chapters 12, 18, App A, App C
self-regulation promptslearning analyticstrace datatransfer performanceadaptive promptingdigital learning environmentsinstructional contextJournal article Evidence summaryAt a glance
This quasi-experimental study examines whether prompts embedded in a digital learning environment can activate self-regulation strategies and whether learning-analytics trace data can explain performance. A sample of 110 higher-education students received prompts targeting cognitive, metacognitive, motivational, or resource-management processes, or participated in a control condition.
Key ideas
The researchers compared declarative knowledge, transfer, learner perceptions, and recorded online behavior. Prompting produced small effects on declarative knowledge and transfer, and prompted groups interacted with the environment differently from the control group. However, the available trace measures did not explain transfer performance sufficiently, illustrating the gap between logging observable clicks and inferring the cognitive or motivational processes that matter for learning. The authors conclude that prompts have potential as lightweight support for self-regulated learning, but learning analytics must be more closely tied to theory and instructional context.
Why it matters
They call for studies of adaptive rather than fixed prompts, richer interpretations of trace data, authentic learning settings, and closer attention to how individual learners perceive and respond to interventions.
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- Vol 4 RE: Chapter 12
- Vol 4 TE: App C
social cognitive theoryself-efficacygoal-directed activitymastery experiencesoutcome expectationsproximal goalsself-regulationJournal article Evidence summaryAt a glance
Schunk and DiBenedetto review motivation through Bandura’s social cognitive theory, defining it as processes that initiate and sustain goal-directed activity. Human functioning reflects reciprocal interactions among personal factors, behavior, and environment, while agency enables people to set goals, anticipate outcomes, monitor action, and regulate conditions.
Key ideas
Self-efficacy—beliefs about capability for a particular task—has a central role because it affects choice, effort, persistence, strategy use, and responses to difficulty. Efficacy develops through mastery experiences, social models, credible persuasion, and interpretations of emotional and physiological states. Goals, outcome expectations, values, and self-evaluation operate with efficacy in cyclical self-regulation. Teachers can strengthen motivation through attainable proximal goals, informative progress feedback, models that students perceive as relevant, strategy instruction, and opportunities for successful performance.
Why it matters
The authors emphasize that efficacy is context-specific and is not the same as general confidence, actual skill, or an assurance of success.
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- Vol 5 RE: Chapter 8
self-regulated learningmetacognitionsocially shared regulationco-regulationself-regulation developmentcontextual variationlearning technologiesBook chapter Evidence summaryAt a glance
Schunk and Greene introduce the handbook by tracing how self-regulated learning developed from behavioural, cognitive, metacognitive, social-cognitive, motivational, and sociocultural traditions. They frame self-regulation as learners’ active management of cognition, motivation, affect, behaviour, and context in pursuit of goals, while emphasizing that regulation changes across tasks, settings, technologies, cultures, and stages of development.
Key ideas
The chapter maps the volume’s major domains: foundational processes, disciplinary and performance contexts, technology-supported learning, methods and assessment, and individual and group differences. It also highlights co-regulation and socially shared regulation, showing that learner autonomy does not imply learning in isolation. As an introductory synthesis, the chapter organizes theories and research agendas rather than testing a single model. Its practical implication is to avoid treating self-regulation as a fixed personal trait or generic study skill.
Why it matters
Educators should consider the learner, task, domain, social environment, feedback, and opportunities to practice monitoring and control, and researchers should match measures closely to the processes and contexts their theories claim to explain.
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- Vol 5 RE: Chapters 2, 8
- Vol 5 TE: App C
- Vol 7 PE: App D
formative feedbackelaborated feedbackfeedback timingfeedback specificityself-regulationworked examplestask-oriented feedbackfeedback overloadJournal article Evidence summaryAt a glance
Shute reviews formative feedback, defined as information communicated to change a learner’s thinking or behaviour for improved learning. Feedback can verify accuracy, identify errors, supply the correct answer, offer hints, explain principles, or present worked examples; its effectiveness depends on the learner, task, timing, and level of detail.
Key ideas
Elaborated feedback that addresses what, how, and why is often more useful than simple right–wrong confirmation, but too much information can overwhelm learners. Effective feedback is generally specific, credible, supportive, and focused on the task, process, or self-regulation rather than personal traits or comparisons with peers. Immediate feedback can help difficult or procedural tasks and prevent persistent error, while delayed feedback may sometimes support transfer or fluency; no single timing rule fits all conditions. For educators and designers, feedback should be manageable, aligned with goals, and usable in a next action.
Why it matters
Avoid grades or praise that divert attention to the self, overly controlling language, vague comments, and hints that let students bypass thinking. The review offers conditional guidelines rather than a universal feedback formula.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
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