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58 references
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- Vol 4 RE: Chapter 24
student agencysubjectificationstudent freedomrelational autonomyeducational responsibilityqualificationsocialisationlearner subjectivityJournal article Evidence summaryAt a glance
Lee examines student freedom and agency through subjectification: becoming a person who can respond as a unique subject rather than merely acquire competencies or exercise consumer choice. Policy often celebrates agency as the capacity to set goals, act strategically, and shape learning.
Key ideas
Lee argues that this can reduce freedom to measurable self-management, achievement, or choice among options structured by institutions. Drawing on educational philosophy, the article distinguishes acting from existing as a subject who encounters limits, other people, resistance, and responsibility. Freedom is relational and uncertain; it includes interrupting one’s desires and responding to what or whom is encountered. Education cannot manufacture subjectivity as a predictable outcome, but teachers can create spaces where students meet the world, speak, act, and bear consequences without being abandoned to themselves.
Why it matters
The argument is philosophical rather than an empirical test of agency interventions. Its practical challenge is to balance qualification and socialisation with opportunities for students to appear as subjects, avoiding both controlling instruction and an individualistic rhetoric that makes learners solely responsible for navigating conditions they did not choose.
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- Vol 5 RE: App D
computer-supported collaborative learningpowerful learning environmentsdistributed cognitionsociocultural theoryknowledge buildingcollaborative regulationshared workspacesinstructional scaffoldingBook chapter Evidence summaryAt a glance
Lehtinen reviews computer-supported collaborative learning as an approach to constructing powerful learning environments. He rejects assumptions that computers improve education simply by delivering information more efficiently or supporting an isolated “solo learner.” From sociocultural and distributed-cognition perspectives, learning is participation in shared intellectual activity in which people use language, representations, tools, and one another’s expertise.
Key ideas
Collaboration can prompt explanation, argument, perspective-taking, mutual regulation, and the externalization of incomplete ideas, but productive interaction does not arise automatically from placing learners in groups. Tasks must be sufficiently complex and interdependent, participants need relevant knowledge and collaborative norms, and technologies must make thinking and communication visible without imposing unnecessary barriers. The chapter surveys synchronous and asynchronous environments, shared workspaces, knowledge-building systems, and forms of scaffolding that support coordination and inquiry. It also notes uneven empirical results caused by differences in task design, group composition, guidance, and measures of learning.
Why it matters
The central implication is that technology should be designed as part of a social and pedagogical system, with teachers structuring goals, discourse, resources, and support for genuine joint knowledge construction.
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- Vol 6 PE: App E
transformational leadershipteacher motivationteacher capacityorganisational conditionsclassroom practiceslarge-scale reforminstructional leadershipstudent achievementJournal article Evidence summaryAt a glance
Leithwood and Jantzi test a school-specific model of transformational leadership using data from an evaluation of England’s National Literacy and Numeracy Strategies. Surveys from 2,290 teachers in 655 primary schools measured leadership, teacher motivation and capacity, work settings, and classroom practices; student outcomes were gains on national Key Stage 2 tests.
Key ideas
Path analyses indicated that transformational leadership influenced teachers’ classroom practices through conditions such as motivation, capacity, and organisational setting, but showed no significant direct or indirect effect on achievement gains in this study. Leadership included setting directions, developing people, and redesigning the organisation rather than relying on charismatic behaviour alone. Findings caution against assuming that favourable teacher perceptions automatically translate into measurable student gains, particularly during large-scale reform where policy, curriculum, assessment, and local context interact. The observational design supports model testing but not definitive causal attribution, and survey measures share common-source limitations.
Why it matters
The practical implication is to judge leadership through the mechanisms it changes: instructional focus, professional learning, working conditions, and classroom practice, while measuring whether those changes are sufficiently strong and coherent to affect students.
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- Vol 6 PE: App E
- Vol 6 RE: Chapters 1, 5
collective leadershipdistributed influenceteacher motivationworking conditionsorganisational variablesshared directionschool achievementparticipatory decision-makingJournal article Evidence summaryAt a glance
Leithwood and Mascall investigate collective leadership using 2,570 teacher surveys from 90 schools and three-year language and mathematics achievement data. Respondents estimated influence exercised by principals, school teams, teachers, parents, and students.
Key ideas
Path analyses found that collective leadership explained a significant portion of between-school achievement variation, largely through teacher motivation, working conditions, and organisational variables. Principals retained the greatest influence at all achievement levels, yet higher-achieving schools attributed more influence to a wider set of participants, especially teams, parents, and students. The authors describe influence as potentially expansive: formal leaders can enable others’ participation without necessarily losing their own capacity. Collective leadership is not equivalent to every person deciding everything or to uncoordinated delegation. Distribution needs shared direction, expertise, supportive relationships, and structures connecting influence to teaching and learning.
Why it matters
Because the study is cross-sectional at the survey level and uses observational path modelling, associations do not establish that distributing leadership causes higher achievement; successful schools may also distribute influence more readily. The findings support examining who influences decisions, through what mechanisms, and with what effects on teachers and students.
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- Vol 6 RE: Chapter 12
collective intelligencecyberspacenetworked knowledgeknowledge communitiespeer productionparticipatory communicationdistributed expertisedemocratic participationBook Evidence summaryAt a glance
Lévy presents collective intelligence as intelligence distributed across people, continually enhanced, coordinated in real time, and mobilized toward shared activity. No individual knows everything, but everyone knows something; the ethical and political challenge is to recognize, connect, and value these dispersed capabilities.
Key ideas
He argues that cyberspace can provide an infrastructure for such coordination, enabling communities to exchange knowledge, construct identities, deliberate, and create a dynamic shared “knowledge space.” This possibility is not equivalent to a uniform group mind. Collective intelligence should preserve singular contributions and increase reciprocal recognition rather than subordinate people to centralized authority or automated systems. The book situates networked communication within a broad anthropology of human spaces, economies, knowledge, and social bonds, and contrasts emerging participatory forms with bureaucratic hierarchies that waste expertise. Lévy’s account is deliberately prospective and normative: technology alone does not guarantee humane outcomes.
Why it matters
Institutions and communities must design practices that encourage inclusion, learning, cooperation, and democratic participation. The work anticipated online collaboration, peer production, and networked knowledge communities while supplying a vocabulary for examining their emancipatory potential and risks.
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- Vol 6 RE: Chapter 8, App A, App D, App E
lesson studyfractions knowledgeresearch-based resourcesteacher collaborationprofessional learninginstructional adaptationstudent thinkingrandomized controlled trialJournal article Evidence summaryAt a glance
Lewis and Perry test whether lesson study combined with research-based mathematical resources can scale knowledge about fractions. Thirty-nine educator teams across the United States were randomly assigned to locally managed lesson study using a fractions resource kit or to one of two control conditions.
Key ideas
Most participants were elementary teachers, and teams worked independently over three months. The kit integrated research on student thinking and linear representations with a lesson-study cycle of investigation, planning, a live research lesson, observation, and reflection. Hierarchical analyses found significantly greater improvement in educators’ and students’ fractions knowledge in the resource-supported lesson-study condition. The design sought to avoid a common scale-up trade-off: tightly scripted materials may preserve content but reduce teacher ownership, while locally controlled professional learning may lack access to specialised research. Combining high-quality resources with collaborative inquiry allowed teachers to adapt while studying the underlying ideas.
Why it matters
The trial supports this particular package, not lesson study under all conditions. Results depend on team engagement, resource quality, facilitation, and implementation, and the sample size limits claims about settings unlike those studied.
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- Vol 6 RE: Chapter 8, App D
lesson studymathematics instructionteacher knowledgeprofessional learning communitiesresearch lessonsstudent thinkinginstructional resourcesevidence-based reflectionJournal article Evidence summaryAt a glance
Lewis, Perry, and Hurd propose a model explaining how lesson study can improve mathematics instruction and examine it against a North American case. Four features structure the process: investigation of curriculum, student thinking, and goals; collaborative planning; observation of a live research lesson; and reflection using evidence.
Key ideas
Improvement can occur through three pathways: changes in teachers’ knowledge and beliefs, development of professional community, and creation or refinement of teaching–learning resources. Better lesson plans are therefore only one possible product. The case provides an “auditable trail” connecting activities, teacher learning, collaboration, and instructional resources, illustrating how mechanisms may operate. Lesson study focuses attention on students’ responses to a carefully planned lesson rather than evaluating the teacher who delivers it, which can support shared inquiry and revision. The article presents theory and case evidence, not a controlled estimate of achievement effects.
Why it matters
Outcomes depend on access to content expertise, useful evidence, norms of observation, time, and repeated cycles. The model helps schools evaluate whether a lesson-study initiative changes professional knowledge and practice rather than merely completing meetings and producing a polished lesson.
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- Vol 6 PE: App E
- Vol 6 RE: Chapter 8
lesson studyinstructional improvementimplementation researchmechanism-based researchiterative improvementlocal adaptationresearch-practice partnershipstransparent documentationJournal article Evidence summaryAt a glance
Lewis, Perry, and Murata ask how research can help an emerging innovation improve before policy enthusiasm or disappointment settles its fate. Lesson study is a Japanese professional-learning process centred on collaborative study, planning, observation of a live research lesson, and evidence-based reflection.
Key ideas
The authors argue that three forms of research are needed: a descriptive knowledge base documenting implementations; explanatory work identifying mechanisms through which activities affect teacher and student learning; and iterative improvement research that tests and refines designs with practitioners. Simple comparisons of adopters and non-adopters cannot explain variation in quality or show which supports matter. They propose changes in research norms and infrastructure, including cumulative measures, transparent documentation, attention to local adaptation, long-term collaboration, and stronger links between researchers and practitioners. The article is a methodological agenda rather than proof that lesson study always succeeds.
Why it matters
Its broader lesson applies to reform research: define the intervention precisely, study enactment and mechanisms, preserve evidence of adaptation, and build usable knowledge through repeated cycles instead of treating a complex practice as a fixed package.
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- Vol 4 RE: App D
music playschoolphoneme processingvocabulary developmentpreschool childrenlongitudinal studyinhibitory controlnonverbal reasoninglanguage developmentJournal article Evidence summaryAt a glance
This longitudinal study investigated whether an accessible, weekly music playschool could support the language development of preschool children. Sixty-six children aged five to six were assessed four times across two school years.
Key ideas
The researchers compared children attending music playschool with peers attending similarly organised dance lessons or neither activity, while also measuring nonverbal reasoning and inhibitory control. Participation in music playschool was associated with stronger growth in phoneme processing and vocabulary; comparable advantages did not appear for dance, perceptual reasoning, or inhibition. The differences emerged over the two-year follow-up, suggesting that sustained, playful group music activity may influence developmental trajectories rather than produce only an immediate training effect. Because assignment was not random and participation could reflect family or contextual differences, the findings should not be treated as definitive causal proof.
Why it matters
Educationally, the study supports regular, professionally led musical activity as a plausible complement to early language learning.
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- Vol 3 TE: App C
computer-supported collaborative learningsociocultural learningdistributed cognitioncollaborative knowledge buildingshared meaning-makingsocial interactioninteraction analysiscommunity normsConference paper Evidence summaryAt a glance
Lipponen reviews the emerging foundations of computer-supported collaborative learning (CSCL) six years after it was identified as a distinct educational-technology paradigm. The paper argues that CSCL should not be reduced to placing learners together around networked tools.
Key ideas
Its central concern is how technology can support social interaction, shared meaning-making, and the construction of knowledge within a community. Lipponen surveys theoretical roots in sociocultural learning, distributed cognition, and collaborative knowledge building, then examines recurring empirical and methodological difficulties. These include defining genuine collaboration, analysing interaction at both individual and group levels, and connecting the affordances of software with pedagogical practices. The review also distinguishes cooperation, in which work may be divided, from collaboration involving sustained joint engagement.
Why it matters
For educators and designers, the main implication is that productive CSCL depends on deliberately structured tasks, discourse, participation, and community norms; technology provides conditions for collaboration but does not itself create it.
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- Vol 7 RE: Chapter 8, App D
flipped learningstudent engagementsecondary mathematicspre-class resourcesself-determination theorycollaborative problem solvinglearning scaffoldsdesign-based researchJournal article Evidence summaryAt a glance
Lo and Hew use a two-year design-based research process to develop principles for sustaining engagement in flipped secondary mathematics. Their sequence included exploratory studies with different student groups, randomized experiments comparing pre-class materials and questions, a literature review, an action-research implementation, and a year-long quasi-experiment.
Key ideas
The resulting approach combines concise, accessible pre-class learning resources with accountability and support, while reserving classroom time for teacher help, peer interaction, and increasingly challenging problem solving. Self-determination theory informs attention to autonomy, competence, and relatedness. The studies show that flipping content delivery does not automatically engage learners: students may fail to prepare, struggle to understand videos independently, or experience in-class tasks as poorly matched to their needs. Design therefore requires scaffolds for preparation, opportunities to check understanding, meaningful collaborative work, differentiation, and iterative adjustment based on student evidence.
Why it matters
The authors frame their contribution as context-sensitive design principles rather than a universal recipe. Effective flipped learning depends on coordinating the pre-class and classroom phases so that each creates a clear reason and adequate preparation for the other.
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- Vol 4 RE: Chapters 5, 8, 17, App C
- Vol 4 TE: App C
- Vol 7 PE: App D
goal settingtask motivationgoal commitmentself-efficacyperformance feedbacklearning goalsself-regulationtask complexityJournal article Evidence summaryAt a glance
Locke and Latham synthesise thirty-five years of goal-setting research into a practical theory of motivation and task performance. Across many laboratory and field studies, specific and difficult goals generally produce higher performance than vague encouragement or easy goals, provided people have sufficient ability, commitment, and resources.
Key ideas
Goals influence action by directing attention, mobilising effort, increasing persistence, and prompting the discovery or use of task strategies. Important moderators include feedback, goal commitment, self-efficacy, task complexity, and situational constraints. The authors also explain when learning goals may be preferable to immediate performance goals, especially on complex or unfamiliar tasks where people must first acquire effective strategies. They integrate goals with satisfaction, incentives, participation, and self-regulation, while identifying limits and areas for further research.
Why it matters
For education, the review supports clear, challenging goals paired with progress information and strategy support; simply assigning demanding targets without building competence or commitment can be ineffective.
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- Vol 2 RE: Chapter 6, App D, App F
active knowledge constructionprior conceptionsmetacognitioncognitive conflictinquiry-oriented teachingstudent agencyemotional engagementcreative thinkingBook Evidence summaryAt a glance
Lonka presents learning as an active, emotionally charged process in which insight emerges when prior knowledge, curiosity, motivation, collaboration, and reflection are coordinated. Drawing on educational psychology, she explains memory, conceptions, metacognition, interest, emotion, creativity, and social interaction, then connects them to activating and inquiry-oriented teaching.
Key ideas
Learners do not simply receive information: they interpret it through existing beliefs, construct and revise explanations, test ideas, and regulate effort. Productive teaching therefore surfaces prior conceptions, poses meaningful problems, creates cognitive conflict without overwhelming students, and provides feedback and support for reflection. Stories, cases, phenomena, and collaborative tasks can stimulate curiosity, while technology is valuable when it expands inquiry, participation, and knowledge construction rather than digitizing passive transmission. The book also attends to wellbeing and the reciprocal influence of emotion and cognition.
Why it matters
Its practical message is that durable learning combines disciplined knowledge building with wonder, agency, and creativity, and that teachers should design environments in which students can become aware of and increasingly regulate their own learning.
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- Vol 2 RE: Chapters 7, 12, App D, App F
- Vol 3 RE: Chapters 1, 4, 6, 7, 10, 11, 12, 13, 14, App D
- Vol 3 TE: App C
- Vol 4 RE: App D
- Vol 5 RE: App D
- Vol 6 RE: Chapter 13, App C, App D, App E
- Vol 7 RE: App C
finnish education systemteacher autonomyphenomenon-based learningtransversal competencesmultidisciplinary learningstudent wellbeingequityresearch-based teacher educationBook Evidence summaryAt a glance
Lonka explains the historical, social, and pedagogical foundations of Finnish education while examining how the system can respond to globalization, digitalization, automation, and rapidly changing knowledge. The book connects Finland’s development of an equitable public school system and research-based teacher education with the newer curriculum emphasis on transversal competences and multidisciplinary, phenomenon-based learning.
Key ideas
Learning is presented as active knowledge construction involving prior conceptions, inquiry, collaboration, emotion, motivation, embodiment, and reflection rather than passive reception. Phenomena provide shared objects around which subject perspectives can be integrated without discarding disciplinary knowledge. The book discusses engaging learning environments, digital practices, assessment, teacher expertise, student wellbeing, and seven broad competence areas in the Finnish curriculum. Examples and contributions from educational specialists illustrate how schools can organize projects and support creativity, participation, multiliteracy, and learning-to-learn.
Why it matters
Lonka does not present Finnish schooling as a finished export model; she identifies tensions and the need for continuing reform. The central message is that future-ready education combines strong knowledge, equitable structures, professionally autonomous teachers, and pedagogies that make learners active participants in meaningful collective inquiry.
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- Vol 4 RE: App C
- Vol 5 RE: Chapters 4, 8, 12, 13, 15
- Vol 6 RE: Chapters 12, 14, App D, App E
- Vol 7 PE: App D
artificial intelligence in educationmachine learninghuman intelligenceadaptive supportmetacognitioneducational dataalgorithmic accountabilityhuman agencyBook Evidence summaryAt a glance
Luckin argues that education should use artificial intelligence to amplify distinctly human intelligence rather than automate teaching around narrow performance measures.
Key ideas
She introduces a broad account of human intelligence that includes academic, social, emotional, and metacognitive capacities, then contrasts it with what contemporary machine-learning systems can infer and optimise. The book explains how educational AI may model learners, provide adaptive support, assist assessment, and make aspects of learning visible to teachers. Its central proposal is a reciprocal design agenda: people need enough understanding of AI to judge its outputs, while systems should be built around rich models of learning and human development. Luckin also discusses data, ethics, accountability, and the danger of adopting technology without a clear educational purpose.
Why it matters
For schools, the practical message is to begin with valued learning goals and teacher expertise, evaluate where automation genuinely adds insight, and preserve human agency in consequential decisions.
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- Vol 2 RE: Chapters 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, App E
artificial intelligence in educationlearning sciencesmultimodal learner dataadaptive scaffoldingteacher decision-makingeducator–developer collaborationethical safeguardshuman judgmentJournal article Evidence summaryAt a glance
Luckin and Cukurova argue that artificial intelligence for education should be designed from evidence about human learning, not simply adapted from technically successful applications in other fields. Three case studies illustrate how rich and multimodal learner data can support scaffolding, collaborative problem solving, and teacher decision-making when analysis is guided by learning-science constructs.
Key ideas
The authors caution that data are not self-explanatory: useful models depend on valid interpretations of cognition, emotion, interaction, and context. They propose a co-design ecosystem linking educators, AI developers, and researchers. Educators need enough AI understanding to evaluate systems and articulate classroom needs; developers need stronger knowledge of pedagogy and learning; researchers help establish evidence, measurement, and ethical safeguards. The framework keeps educational values and stakeholder expertise central throughout development and evaluation.
Why it matters
Rather than replacing teachers, well-designed AI should augment human judgment, make otherwise difficult learning processes visible, and enable timely support. The paper ultimately calls for interdisciplinary partnerships that assess educational benefit alongside technical performance.
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- Vol 3 RE: Chapters 5, 9, 13, 14, 15, 16, App D
artificial intelligence literacymachine learninglearning analyticsadaptive feedbackhuman–ai collaborationalgorithmic biasdata privacyethical ai adoptionBook Evidence summaryAt a glance
This practical introduction helps teachers and school leaders understand artificial intelligence well enough to make deliberate educational decisions without becoming technical specialists.
Key ideas
The authors distinguish AI from ordinary automation, explain the role of data and machine learning, and ask educators to begin with valued learning goals rather than available products. Schools are encouraged to become “AI ready” by identifying genuine educational challenges, examining what data are collected and what those data represent, and deciding which tasks require human judgment, relationships, and contextual knowledge. Examples show how AI can support analysis, feedback, personalization, and planning, while also exposing limitations involving bias, privacy, transparency, accountability, and unequal access. The book treats effective adoption as an organizational learning process: educators need shared language, evidence-informed experimentation, ethical review, and a strategy for combining artificial and human intelligence.
Why it matters
Its central message is that AI should expand educational opportunity and strengthen professional capacity, with teachers retaining responsibility for defining success and judging whether a system actually serves learners.
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- Vol 7 RE: Chapter 13, App D
intelligent tutoring systemsdialogue-based tutoringlearning analyticspersonalized learning pathwaysautomated assessmentteacher–ai collaborationeducational data ethicslifelong learning companionsReport Evidence summaryAt a glance
This report introduces artificial intelligence in education for a non-specialist audience and argues that it can improve teaching and learning when used to address clearly defined educational needs. It explains established applications such as intelligent tutoring systems, dialogue-based tutors, exploratory environments, automated assessment, and learning analytics.
Key ideas
These systems build models of subject matter, pedagogy, and learners in order to adapt tasks, feedback, or support. The authors distinguish current, relatively narrow capabilities from speculative general intelligence and emphasize that teachers remain essential. They propose combining AI with educators' social, emotional, and contextual expertise to create new forms of support, including continuous assessment, personalized pathways, collaborative-learning analysis, and tools that help teachers see otherwise hidden learning processes. The report also identifies infrastructure, evidence, ethics, privacy, and workforce development as prerequisites for responsible adoption.
Why it matters
Its forward-looking agenda includes lifelong learning companions and better support for complex skills, but insists that progress requires rigorous evaluation and public discussion about educational aims, data, and acceptable human–machine roles.
A transparent research trail
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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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