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Browse the scientific research and professional literature used across all seven volumes of The Science of Learning for Educators.
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4 references
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- Vol 5 RE: Chapter 8
retrieval practicetesting effectsspaced retrievalpretestingprequestioningtest-potentiated learningmetacognitive regulationauthentic educational settingsJournal article Evidence summaryAt a glance
Pan, Dunlosky, Xu, and Ouwehand introduce a special issue on the next phase of test-enhanced learning research.
Key ideas
They note that retrieval practice and related testing effects are well established, then organize emerging questions across practice formats and research themes. The issue covers retrieval practice, spaced retrieval, prequestioning and pretesting, and test-potentiated new learning, alongside comparisons and combinations with other learning strategies. It also highlights metacognition and self-regulation, applications in authentic educational settings, and communication of findings to teachers and learners. The commentary maps the included reviews, empirical studies, and reflections onto these themes and identifies unresolved questions for future investigation.
Why it matters
Its practical message is that research should move beyond demonstrating that testing can improve learning toward understanding boundary conditions, implementation, learner regulation, and effective translation into educational practice.
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- Vol 4 RE: Chapter 12
- Vol 4 RE: Chapters 6, 11, 12, 18, 26, 27, App C
- Vol 5 RE: Chapters 4, App A
- Vol 7 RE: Chapter 7, App D
self-regulated learningmetacognitive regulationgoal settingstrategy monitoringmotivational regulationemotional regulationco-regulationsocially shared regulationJournal article Evidence summaryAt a glance
Panadero compares six influential models of self-regulated learning: those developed by Zimmerman, Boekaerts, Winne and Hadwin, Pintrich, Efklides, and Hadwin, Järvelä, and Miller.
Key ideas
The review shows that self-regulation is not a single study skill but an interacting set of cognitive, metacognitive, behavioural, motivational, and emotional processes. Although the models use different terminology and levels of analysis, they commonly describe cyclical activity in which learners interpret a task, set goals, select and monitor strategies, and adapt in light of feedback. The paper traces how the field has expanded from individual regulation toward co-regulation and socially shared regulation in collaborative learning. It also identifies directions for research, including integrating models, improving measurement, studying emotion and motivation, and examining regulation in groups and technology-rich settings.
Why it matters
For educators, the comparison supports designing tasks that make goals and standards clear, prompt planning and monitoring, provide usable feedback, and gradually help learners assume control of their learning.
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- Vol 4 RE: Chapters 2, 5, App A, App B, App C
goal orientationself-regulated learningmastery goalsperformance goalsachievement motivationmetacognitive regulationclassroom goal structuresBook chapter Evidence summaryAt a glance
Pintrich integrates achievement-goal theory with a general model of self-regulated learning. The framework treats learners as active participants who can monitor and regulate cognition, motivation, behaviour, and features of their environment across phases of forethought, monitoring, control, and reaction.
Key ideas
Goal orientations provide purposes for achievement activity and influence how students interpret tasks, judge competence, choose strategies, persist, and respond to difficulty. The chapter distinguishes mastery and performance goals and their approach and avoidance forms. Evidence reviewed suggests that mastery-approach goals are generally associated with adaptive beliefs, deeper processing, effort, and self-regulation, while avoidance orientations are more consistently maladaptive; performance-approach findings are mixed and depend on outcomes and context. Goals are not isolated traits, and students can pursue multiple goals shaped by classroom structures.
Why it matters
For educators, the account supports emphasising understanding, improvement, meaningful challenge, strategy use, and informative feedback while reducing conditions that make avoiding failure or public comparison dominant. The chapter also cautions that relationships are probabilistic and calls for more precise longitudinal and contextual research.
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- Vol 4 RE: Chapters 25, 27
artificial intelligenceself-regulated learningintelligent tutoring systemslearning analyticsconversational agentsadaptive learningmetacognitive regulationlearner agencyJournal article Evidence summaryAt a glance
Ren, Lee, and May systematically review how artificial intelligence has been used to support self-regulated learning in education.
Key ideas
Searching literature from 2004 through 2024, they retain twenty-seven studies and classify them by educational level, research method, subject area, AI technology, self-regulated-learning framework, and reported outcome. The review identifies intelligent tutoring and adaptive systems, learning analytics and prediction, conversational agents, and other data-driven supports that can prompt planning, monitoring, feedback use, strategy adjustment, and reflection. It also finds uneven theoretical grounding and limited evidence across contexts, with many studies concentrated in particular settings and phases of regulation.
Why it matters
The authors call for stronger alignment between AI functions and explicit self-regulation theory, more rigorous empirical designs, attention to learner agency and ethics, and research that tests sustained effects rather than short-term performance alone.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
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