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69 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 F
- Vol 5 RE: App D
finnish education systemequitable accessteacher professionalismtrust-based governancecomprehensive schoolingtest-based accountabilityearly interventioneducation policy transferBook Evidence summaryAt a glance
Sahlberg explains Finland’s educational development as the result of sustained public policy rather than a transferable package of isolated techniques. Comprehensive schooling, equitable access, publicly funded services, highly educated teachers, trust-based governance, and local professional responsibility developed together over decades.
Key ideas
He contrasts this path with the Global Educational Reform Movement’s emphasis on competition, standardization, test-based accountability, and market choice. Finnish schools use comparatively little external testing, preserve time for play and broad learning, and treat teaching as a research-informed profession requiring a master’s degree. Collaboration and early support aim to reduce variation before it becomes entrenched. Later editions also confront declining international results, demographic change, digitalization, inequality, and the limits of national mythology.
Why it matters
Sahlberg warns that context matters: countries cannot copy outcomes without understanding institutions, social policy, and historical sequencing. The broader lesson is to build coherent systems that combine excellence with equity, trust professionals while maintaining public responsibility, and learn through disciplined adaptation.
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- Vol 4 RE: App D
- Vol 6 RE: Chapters 4, 13, App C, App D
finnish education systemequitable accessteacher professionalismtrust-based governancecomprehensive schoolingtest-based accountabilityearly interventioneducation policy transferBook Evidence summaryAt a glance
Sahlberg explains Finland’s educational development as the result of sustained public policy rather than a transferable package of isolated techniques. Comprehensive schooling, equitable access, publicly funded services, highly educated teachers, trust-based governance, and local professional responsibility developed together over decades.
Key ideas
He contrasts this path with the Global Educational Reform Movement’s emphasis on competition, standardization, test-based accountability, and market choice. Finnish schools use comparatively little external testing, preserve time for play and broad learning, and treat teaching as a research-informed profession requiring a master’s degree. Collaboration and early support aim to reduce variation before it becomes entrenched. Later editions also confront declining international results, demographic change, digitalization, inequality, and the limits of national mythology.
Why it matters
Sahlberg warns that context matters: countries cannot copy outcomes without understanding institutions, social policy, and historical sequencing. The broader lesson is to build coherent systems that combine excellence with equity, trust professionals while maintaining public responsibility, and learn through disciplined adaptation.
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- Vol 5 RE: Chapter 12
learning analyticsgenerative artificial intelligencepersonalized interventionspedagogical decision makingaffective inferencestudent data privacyhuman oversightmachine-generated interpretationsPreprint Evidence summaryAt a glance
Sajja, Sermet, Cwiertny, and Demir describe a prototype learning-analytics tool that uses GPT-4 to help instructors interpret student questions and other learning signals.
Key ideas
The system classifies features such as topic, cognitive level using Bloom’s taxonomy, curiosity, confusion, stress, and preferred study approaches, then aggregates them into dashboards intended to show engagement and learning progression. The paper explains the architecture, learning-management-system integration, synthetic-data testing, and a faculty survey about perceived usefulness and risks. Respondents saw potential for timely instructional adjustment and personalized intervention but raised concerns about privacy, security, reliability, bias, and the validity of machine-generated interpretations. The study is exploratory: it does not demonstrate improved learning outcomes, and inference about affect from text requires careful validation.
Why it matters
Its contribution is a concrete design case showing how generative AI might augment pedagogical decision making while making governance, human oversight, transparent metrics, and protection of student data central requirements rather than afterthoughts.
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- Vol 4 RE: Chapter 22
creative cognitioncreative expertiseintrinsic motivationiterative ideationcollaborative creativityproductive constraintsdomain knowledgeinnovation environmentsBook Evidence summaryAt a glance
Sawyer surveys creativity as an interdisciplinary science rather than a mysterious gift possessed by a few exceptional people. He reviews individual differences, cognitive processes, development, personality, motivation, social context, domains, organizations, and historical change.
Key ideas
Creative products are both novel and appropriate within a community, so evaluation depends partly on fields of experts and culturally developed symbol systems. The book challenges linear stage models and lone-genius myths: ideas usually emerge through iterative cycles of preparation, generation, externalization, feedback, revision, and collaboration. Expertise and domain knowledge enable creativity but can also constrain search when conventions become rigid. Intrinsic motivation, productive constraints, diverse networks, and environments that tolerate informed risk support innovation.
Why it matters
Sawyer connects laboratory research with case studies of art, science, invention, and group improvisation. For education, the implication is to combine explicit knowledge and guidance with sustained opportunities to make, test, discuss, and refine original work.
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- Vol 3 TE: App C
conceptual changecognitive apprenticeshipscaffoldingknowledge buildingmetacognitioncollaborative learningdesign-based researchdisciplinary practicesEdited book Evidence summaryAt a glance
Sawyer’s handbook presents the learning sciences as an interdisciplinary, design-oriented field concerned with how people learn in classrooms, communities, workplaces, and technology-rich settings.
Key ideas
Across cognitive, sociocultural, and computational traditions, chapters emphasize that robust learning involves organized knowledge, participation in meaningful practices, collaboration, metacognition, and the ability to use understanding in new situations. The field studies learning as it unfolds and also designs environments intended to improve it, often through iterative design-based research. Core topics include conceptual change, cognitive apprenticeship, scaffolding, discourse, knowledge building, collaborative learning, disciplinary practices, assessment, informal learning, and educational technology. The handbook rejects a simple opposition between instruction and discovery: effective environments provide structure, representations, feedback, and guidance while learners actively explain, inquire, create, and regulate.
Why it matters
Its practical contribution is a set of complementary theories and methods for connecting fine-grained learning processes with the redesign of curricula, tools, classrooms, and educational systems.
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- Vol 2 RE: Chapter 14, App D, App E
- Vol 3 RE: Chapters 2, 13
conceptual changecognitive apprenticeshipscaffoldingknowledge buildingmetacognitioncollaborative learningdesign-based researchdisciplinary practicesEdited book Evidence summaryAt a glance
Sawyer’s handbook presents the learning sciences as an interdisciplinary, design-oriented field concerned with how people learn in classrooms, communities, workplaces, and technology-rich settings.
Key ideas
Across cognitive, sociocultural, and computational traditions, chapters emphasize that robust learning involves organized knowledge, participation in meaningful practices, collaboration, metacognition, and the ability to use understanding in new situations. The field studies learning as it unfolds and also designs environments intended to improve it, often through iterative design-based research. Core topics include conceptual change, cognitive apprenticeship, scaffolding, discourse, knowledge building, collaborative learning, disciplinary practices, assessment, informal learning, and educational technology. The handbook rejects a simple opposition between instruction and discovery: effective environments provide structure, representations, feedback, and guidance while learners actively explain, inquire, create, and regulate.
Why it matters
Its practical contribution is a set of complementary theories and methods for connecting fine-grained learning processes with the redesign of curricula, tools, classrooms, and educational systems.
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- Vol 2 RE: Chapter 14, App A, App D, App F
- Vol 3 RE: Chapters 1, 2, 3, 11, 12, App A
- Vol 3 TE: App C
- Vol 7 PE: App D
- Vol 7 RE: Chapters 1, 2, 3, 5, 6, 13, 14, 15, App D
knowledge buildingepistemic agencyauthentic problemsconceptual artifactsidea diversitycollective inquiryembedded assessmentknowledge forumBook chapter Evidence summaryAt a glance
Scardamalia and Bereiter distinguish knowledge building from individual learning and ordinary project work. A knowledge-building community takes collective responsibility for advancing public ideas that can be examined, challenged, connected, and improved.
Key ideas
Students exercise epistemic agency by identifying authentic problems, proposing explanations, using authoritative sources constructively, and deciding what the community needs to understand next. Knowledge is treated as improvable conceptual artifacts rather than answers owned by individuals. Principles include real ideas and authentic problems, idea diversity, rise-above syntheses, symmetric knowledge advancement, democratizing knowledge, pervasive knowledge building, and embedded assessment. Technology such as Knowledge Forum can preserve discourse, make connections visible, and support higher-level organization, but software does not create the culture by itself.
Why it matters
The educational aim is to initiate learners into knowledge-creating practices: sustained inquiry, explanatory coherence, responsible use of evidence, and contribution to a shared intellectual frontier rather than completion of teacher-defined tasks.
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- Vol 2 RE: Chapter 4, 5, 6, 12
knowledge buildingknowledge creationepistemic agencyauthentic problemsconceptual artifactscollective inquiryembedded assessmentknowledge forumBook chapter Evidence summaryAt a glance
Scardamalia and Bereiter distinguish knowledge building from individual learning and ordinary project work. A knowledge-building community takes collective responsibility for advancing public ideas that can be examined, challenged, connected, and improved.
Key ideas
Students exercise epistemic agency by identifying authentic problems, proposing explanations, using authoritative sources constructively, and deciding what the community needs to understand next. Knowledge is treated as improvable conceptual artifacts rather than answers owned by individuals. Principles include real ideas and authentic problems, idea diversity, rise-above syntheses, symmetric knowledge advancement, democratizing knowledge, pervasive knowledge building, and embedded assessment. Technology such as Knowledge Forum can preserve discourse, make connections visible, and support higher-level organization, but software does not create the culture by itself.
Why it matters
The educational aim is to initiate learners into knowledge-creating practices: sustained inquiry, explanatory coherence, responsible use of evidence, and contribution to a shared intellectual frontier rather than completion of teacher-defined tasks.
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- Vol 7 RE: Chapter 9, App D
technological pedagogical content knowledgeteacher self-efficacypreservice teachersconfirmatory factor analysisintegrative knowledge modeltechnology integrationscale developmentteacher assessmentJournal article Evidence summaryAt a glance
Schmid, Brianza, and Petko develop TPACK.xs, a concise self-report instrument for teachers’ technological pedagogical content knowledge. Existing questionnaires were often lengthy, difficult to use, or psychometrically ambiguous.
Key ideas
The authors generate and refine items representing technological, pedagogical, and content knowledge and their intersections, then test the scale with preservice teachers. Reliability and confirmatory factor analyses support a short instrument that can be administered economically. The study also compares two conceptions of TPACK: an integrative model in which component knowledge domains remain distinguishable and combine, and a transformative model in which TPACK functions as a more unified competence. Results provide stronger support for the integrative structure in this sample.
Why it matters
Because scores are self-reports, they indicate perceived knowledge rather than direct classroom performance, and the factor solution needs replication across populations, subjects, and languages. For researchers and teacher educators, TPACK.xs offers a practical diagnostic or programme-evaluation measure, best supplemented with lesson plans, observations, performance tasks, or portfolios when claims concern enacted technology integration.
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- Vol 7 RE: Chapter 9
learning experience designhuman-centered designlearner experiencesociocultural contextuser experience methodslearning goalsinteraction designemotional experienceJournal article Evidence summaryAt a glance
Schmidt and Huang analyse how practitioners and scholars define learning experience design.
Key ideas
They describe it as a human-centred, theoretically grounded, and socioculturally sensitive approach that draws on user-experience methods while remaining directed toward learning goals. The account broadens design beyond isolated instructional effectiveness to include the learner, context, interaction, usability, emotion, and the experience over time. For educators and designers, the article supports examining how the whole environment feels and functions for learners while retaining a clear grounding in learning theory and educational purpose. The definition is especially useful when teams need shared criteria for judging learner-centred digital experiences.
Why it matters
The definition is especially useful when teams need shared criteria for judging learner-centred digital experiences overall.
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- Vol 4 RE: Chapter 10
mathematical problem solvingproblem-solving heuristicsmetacognitive controlstrategic flexibilitymathematical beliefssolution monitoringmathematical reasoningsense-makingBook Evidence summaryAt a glance
Schoenfeld investigates why knowing mathematical facts and procedures does not guarantee success on unfamiliar problems. Detailed analyses of learners’ work show that performance depends on four interacting components: resources such as concepts and procedures; heuristics for exploring, representing, and transforming problems; control or metacognitive decisions about planning, monitoring, and allocating effort; and beliefs about mathematics and oneself.
Key ideas
Students may possess relevant knowledge yet pursue an unproductive path because they fail to assess progress or abandon it. The book combines empirical problem-solving sessions with instruction designed to make strategic decisions visible. Teachers model questions, compare solution paths, discuss errors, and help students justify why an approach is promising rather than simply name a heuristic. Schoenfeld treats mathematical competence as sense-making within a community, not the memorization of tricks.
Why it matters
Productive instruction therefore develops knowledge, strategic flexibility, monitoring, and beliefs that mathematics is intelligible and that solutions should be explained and checked.
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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 7 RE: Chapter 10, App D
learning analyticsstudent expectationsstudy planningself-assessmentadaptive recommendationspersonalized feedbackdata privacyJournal article Evidence summaryAt a glance
Schumacher and Ifenthaler investigate what university students want from learning-analytics systems and whether they believe particular features would support their learning. An exploratory qualitative study with 20 students generated a set of expected functions, followed by a quantitative study in which 216 students rated 15 features, their willingness to use them, and perceived learning value.
Key ideas
Students especially valued tools for planning and organizing study, self-assessment, adaptive recommendations, and personalized analysis of learning activity. These expectations map onto phases of self-regulated learning, including preparation, performance monitoring, and reflection. Participants saw potential benefits in timely feedback and individualized support, yet also worried that analytics might become invasive or reduce autonomy. The findings show why dashboard design should begin with learner needs and educational theory rather than the data that happen to be available.
Why it matters
The authors recommend aligning features with self-regulation, feedback, and instruction, while making data practices and purposes transparent. Meaningful user involvement is therefore both a design requirement and a condition for acceptance.
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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 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.
View or buy the cited work >>Read a similar article free online >>
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- Vol 4 RE: Chapter 5
academic motivationself-efficacyachievement goalstask valuesaffective regulationhelp seekingmotivational beliefsBook 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.
View or buy the cited work >>Read a similar article free online >>
A transparent research trail
More than a bibliography.
What it contributes
Evidence summaries explain the central idea and why the source matters to educators.
How to access it
Entries link to lawful full text where available, or to a publisher, library or purchase page.
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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