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9 references
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- Vol 5 RE: Chapters 1, 3, 5, App B
- Vol 5 TE: App C
formative assessmentfeedback usequality criteriaself-monitoringevaluative expertiseassessment exemplarsfeedback-based revisionself-regulated learningJournal article Evidence summaryAt a glance
Sadler develops a theory of formative assessment for complex work judged against multiple criteria. Feedback becomes formative only when information about performance is used to reduce the gap between a learner’s current work and a desired standard.
Key ideas
To do this independently, students must understand what quality means, compare their own work with that standard, and possess strategies for closing the gap. Teacher comments alone cannot produce self-monitoring if standards remain tacit or students lack evaluative expertise. Exemplars, comparison, discussion of criteria, and authentic experience making judgements can help learners develop a concept of quality, including aspects not readily captured by analytic rubrics. The account applies most strongly to open, qualitative performances rather than tasks scored simply right or wrong.
Why it matters
For educators, formative design should move beyond delivering corrections: make quality visible through varied examples, involve students in justified judgements, connect feedback to revision, and create repeated cycles of production and improvement. The long-term aim is not permanent teacher dependence but the learner’s growing ability to monitor and regulate work while it is being produced.
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- Vol 2 RE: App D
self-regulated learningmetacognitive regulationcognitive strategiesacademic motivationscience inquiryself-explanationconcept mappingJournal article Evidence summaryAt a glance
Schraw, Crippen, and Hartley review self-regulated learning in science education through three interacting components: cognition, metacognition, and motivation. Cognitive strategies help learners encode, organise, elaborate, and retrieve scientific knowledge.
Key ideas
Metacognition includes knowledge about cognition and regulation through planning, monitoring, and evaluation. Motivation supplies beliefs and goals that influence whether strategies are initiated and sustained, including self-efficacy, task value, and goal orientations. Effective learners coordinate all three rather than relying on isolated study techniques. The authors connect the framework to science practices and review instruction using inquiry, collaborative learning, strategy teaching, self-explanation, prompts, concept mapping, and reflective assessment. Support should make expert thinking visible, provide guided practice and feedback, and gradually transfer control. They also emphasise that strategy knowledge does not guarantee use when motivation or contextual support is weak.
Why it matters
For science educators, productive design pairs challenging investigations with explicit planning and monitoring routines, opportunities to explain evidence, feedback on both reasoning and outcomes, and classroom conditions that build competence and value. More domain-specific, longitudinal research is needed.
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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 4 RE: App C
self-regulated learningmetacognitive monitoringmotivational regulationaffective regulationhelp seekingcollaborative regulationlearning analyticsEdited book Evidence summaryAt a glance
This edited volume treats self-regulated learning as the active coordination of cognition, metacognition, motivation, affect, behavior, and environmental resources in pursuit of goals. Models differ in terminology but commonly describe recurring phases of task interpretation and planning, strategic performance and monitoring, and reflection that informs adaptation.
Key ideas
Contributions examine efficacy beliefs, goals, values, emotion, help seeking, collaboration, development, and differences across subject domains. They also show why self-report questionnaires alone cannot capture regulation as it unfolds; think-alouds, traces, diaries, microanalysis, and performance data reveal different parts of the process. Instruction can make strategies explicit, model conditional choices, provide feedback on progress, and gradually shift responsibility to learners. Technology may support prompts and visualization, but dashboards or prompts are not automatically regulatory.
Why it matters
The collection’s central message is that successful learners do not merely possess strategies: they decide when and why to use them, monitor consequences, and revise action within particular tasks and social contexts.
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- Vol 4 RE: Chapters 1, 2, 17, App C
- Vol 4 TE: App C
self-regulated learningacademic motivationself-efficacygoal orientationmotivational regulationstrategy useperformance monitoringEdited book Evidence summaryAt a glance
This edited volume treats self-regulated learning as the active coordination of cognition, metacognition, motivation, affect, behavior, and environmental resources in pursuit of goals. Models differ in terminology but commonly describe recurring phases of task interpretation and planning, strategic performance and monitoring, and reflection that informs adaptation.
Key ideas
Contributions examine efficacy beliefs, goals, values, emotion, help seeking, collaboration, development, and differences across subject domains. They also show why self-report questionnaires alone cannot capture regulation as it unfolds; think-alouds, traces, diaries, microanalysis, and performance data reveal different parts of the process. Instruction can make strategies explicit, model conditional choices, provide feedback on progress, and gradually shift responsibility to learners. Technology may support prompts and visualization, but dashboards or prompts are not automatically regulatory.
Why it matters
The collection’s central message is that successful learners do not merely possess strategies: they decide when and why to use them, monitor consequences, and revise action within particular tasks and social contexts.
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- Vol 4 RE: Chapter 6
self-regulated learningsocially shared regulationcollaborative learningadaptive learning environmentslearning analyticsfeedback scaffoldinghelp seekinglearner agencyJournal article Evidence summaryAt a glance
Sharma, Nguyen, and Hong systematically review empirical studies connecting adaptive digital learning environments with self-regulated and socially shared regulation in collaborative learning. Their search and coding identify seven recurring objectives: feedback and scaffolding, regulatory skills and strategies, learning trajectories, collaborative processes, adaptation and regulation, self-assessment, and help seeking.
Key ideas
Adaptive systems can personalize prompts, representations, feedback, and paths, while collaboration introduces collective planning, monitoring, control, and reflection that cannot be inferred from individual traces alone. The literature uses heterogeneous theoretical models, settings, technologies, and measures, limiting cumulative conclusions. Important gaps include few informal-learning studies, weak theoretical convergence between individual and shared regulation, and difficulty monitoring how regulation is coordinated across group members. The authors call for clearer constructs, multimodal and process-sensitive measures, and designs that support rather than automate learner agency.
Why it matters
The review provides a map of opportunities but does not establish that adaptation by itself improves regulation or collaboration.
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- Vol 4 RE: Chapter 25
ai literacyself-regulated learninggenerative artificial intelligencewriting performancestudent well-beingstructural equation modelingai ethicsJournal article Evidence summaryAt a glance
Shi, Liu, and Hu investigate associations among AI literacy, self-regulated learning, perceived writing performance, and well-being in generative-AI-supported higher education. Survey responses from 257 university students in China were analyzed with structural equation modeling.
Key ideas
Both AI literacy and self-regulated learning positively predicted students’ perceived writing performance, with self-regulation showing the stronger association. AI literacy also had a positive relationship with generative-AI-related well-being, and writing performance partially mediated that relationship. The model brings technological competence and strategic learning behavior together rather than treating tool knowledge as sufficient on its own. For teaching, the results support developing students’ ability to plan, monitor, and reflect while also teaching awareness, effective use, evaluation, and ethics of AI.
Why it matters
Important limits temper the findings: measures were self-reported, the writing-performance scale had modest reliability, the sample came from one national context, and the cross-sectional design cannot establish causal effects. The study therefore indicates relationships worth supporting and testing, not proof that AI literacy or AI use automatically improves writing or psychological well-being.
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- Vol 4 RE: Chapters 4, 7
- Vol 4 TE: App C
- Vol 5 TE: App C
metacognitionself-regulated learninglearning strategy regulationreflective practiceexam wrapperslearning journalsexpert thinkingJournal article Evidence summaryAt a glance
Tanner argues that students should be explicitly taught to think about and regulate their learning rather than expected to discover effective approaches unaided. Metacognition includes awareness of one’s knowledge and strategies and regulation through planning, monitoring, and evaluating.
Key ideas
The article offers practical prompts that can be embedded before, during, and after learning: identifying prior knowledge and goals, predicting difficulties, explaining reasoning, checking understanding, noticing confusion, comparing strategies, analysing errors, and planning changes. Activities include reflective exam wrappers, learning journals, minute papers, explicit discussion of study strategies, modelling an expert’s thinking, and asking students to connect evidence with claims. The goal is not reflection as an extra assignment but recurring attention to how learning decisions affect outcomes. Students may initially lack vocabulary, accuracy, or willingness to report difficulties, so prompts need modelling, psychological safety, feedback, and repeated use.
Why it matters
For science educators, active learning should be “minds-on” as well as hands-on: instructors can make disciplinary thinking visible and create cycles in which learners choose a strategy, observe its consequences, and deliberately adjust.
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- Vol 3 RE: Chapter 14
- Vol 3 TE: App C
peer assessmentformative feedbackassessment criteriaself-regulated learningpeer judgmentfeedback qualityassessment reliabilityrevision practicesJournal article Evidence summaryAt a glance
This practice-oriented article explains peer assessment as learners judging work by peers of similar status, usually with formative purposes extending beyond a mark. Topping distinguishes qualitative comments or scores, anonymous or identified review, same- or cross-ability matching, reciprocal or one-way roles, individual or group products, and formative or summative use.
Key ideas
Peer assessment may increase feedback while helping students internalise criteria, compare strategies, explain judgements, and reflect on their own work. Reliability can be acceptable, particularly when multiple judgements are combined, but accuracy is not automatic. Students may lack domain knowledge, misunderstand standards, avoid criticism, favour friends, or distrust peer grades. Preparation therefore matters: teachers should clarify purposes and criteria, model examples, involve students in criteria where appropriate, train them to give specific constructive feedback, rehearse with low stakes, monitor exchanges, and provide opportunities for revision.
Why it matters
The teacher remains responsible for design and quality assurance. The central implication is that making and discussing judgements can itself produce learning; peer assessment should be reciprocal participation in evaluative practice, not merely delegated grading.
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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
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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