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58 references
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- Vol 3 RE: Chapters 5, 9
- Vol 7 RE: Chapter 5, App B
computer-supported collaborative learningscience, technology, engineering, and mathematics educationcollaborative knowledge constructioninstructional scaffoldinggroup processeslearning outcomesJournal article Evidence summaryAt a glance
Jeong, Hmelo-Silver, and Jo meta-analyse computer-supported collaborative learning in STEM education published from 2005 through 2014. The review codes studies across science, mathematics, engineering, computer science, health, and education-related domains and examines learning outcomes alongside instructional, technological, and contextual characteristics.
Key ideas
Overall, CSCL produces a positive moderate effect, with variation across designs and settings. The authors investigate how collaboration is structured, what technologies mediate activity, which supports are provided, and whether studies occur in classrooms, laboratories, or other environments. Classroom effects are not significantly different from other settings in the reported comparison, underscoring that location alone does not explain success. Technology is not treated as an independent causal ingredient: outcomes depend on tasks, group processes, pedagogy, scaffolds, and how tools enable interaction and shared knowledge construction.
Why it matters
The synthesis is bounded by the quality and reporting of included studies and by heterogeneity in interventions and measures. Its practical implication is to design the collaborative process deliberately rather than assume that placing learners together around digital tools will produce effective STEM learning.
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- Vol 3 RE: Chapters 1, 2, 6, 10, 12, 14, App A
- Vol 3 TE: App C
- Vol 4 RE: App C
social interdependence theorycooperative learningpositive interdependenceindividual accountabilitypromotive interactiongroup processingJournal article Evidence summaryAt a glance
Johnson and Johnson present social interdependence theory as an example of sustained interaction among theory, research, and educational practice. Interdependence exists when individuals perceive that their outcomes are linked.
Key ideas
Positive interdependence promotes cooperative action; negative interdependence promotes competition; and no interdependence supports individualistic effort. Across decades of studies, cooperative structures generally outperform competitive and individualistic ones on achievement, reasoning, time on task, relationships, social support, and several psychological outcomes. Benefits do not arise from unstructured group seating. Effective cooperative learning deliberately establishes positive interdependence, individual accountability, promotive interaction, appropriate interpersonal skills, and group processing. These conditions influence how participants interact, and interaction mediates outcomes. The authors trace the theory’s origins, research programme, validation, controversies, and translation into classroom procedures.
Why it matters
Reported aggregate effects span diverse studies, so implementation, task, population, and methodological quality still matter. The “success story” is not that one technique works everywhere, but that a clearly specified theory generated testable propositions, cumulative evidence, refinements, and practical designs that educators can implement and evaluate.
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- Vol 7 PE: App D
- Vol 7 RE: Chapters 2, 3
communities of learnersconstructivist learningcognitive apprenticeshipknowledge constructionintellectual toolsteacher scaffoldingJournal article Evidence summaryAt a glance
Jonassen presents a vision of schools as communities in which learners collaboratively construct knowledge and technology supports, rather than delivers, that activity. Drawing on constructivism and cognitive apprenticeship, he characterizes meaningful learning as active, constructive, intentional, authentic, and cooperative.
Key ideas
Learners should investigate problems, articulate interpretations, share representations, and negotiate understanding with others. In this model, computers are not primarily tutors that transmit predetermined content or test recall. They serve as intellectual partners and tools for accessing information, representing knowledge, communicating, modelling, and reflecting. Teachers organize rich contexts, model expert practices, coach inquiry, and provide scaffolding while gradually transferring responsibility to students. The short article therefore reframes technology integration as a pedagogical and cultural question: purchasing devices or software does not create a learning community.
Why it matters
Productive integration depends on tasks, roles, discourse, and assessment that value knowledge construction and collaboration. Technology is useful when it enables learners to do meaningful cognitive and social work that would otherwise be difficult, not when it merely automates conventional instruction.
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- Vol 4 RE: App D
regulated learningtemporal processesadaptive regulationmultimodal dataprocess tracingmetacognitive monitoringphysiological measurestemporal alignmentJournal article Evidence summaryAt a glance
Järvelä and Bannert argue that regulated learning must be studied as a temporal and adaptive process rather than inferred only from retrospective questionnaires or final outcomes. Learners and groups continually interpret tasks, set goals, monitor cognition, motivation, emotion, and behaviour, and adjust strategies when conditions change.
Key ideas
Multimodal data—such as trace logs, discourse, video, eye movement, physiological measures, and self-reports—can reveal complementary aspects of these otherwise partly hidden processes. The promise lies not in collecting more streams indiscriminately but in aligning theory, timescale, units of analysis, and methods for detecting meaningful events and sequences. Data sources differ in what they measure and cannot automatically identify regulation; physiological synchrony or a click, for example, requires contextual interpretation. Researchers must integrate temporally aligned evidence, distinguish individual, co-regulated, and socially shared activity, and validate computational indicators against theoretically meaningful human judgements.
Why it matters
The article outlines methodological opportunities and cautions rather than offering a single predictive model. Its central agenda is adaptive support: process evidence may eventually help recognise when regulation is needed and provide well-timed scaffolds without oversimplifying learning.
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- Vol 3 RE: Chapters 1, 2, 3, 4, 5, 8, App D
- Vol 3 TE: App C
- Vol 6 RE: Chapter 13, App C, App D, App E
- Vol 7 RE: App C, D
self-regulationco-regulationsocially shared regulationcollaborative learningcomputer-supported collaborationregulatory scaffoldsshared awarenessprocess-sensitive supportJournal article Evidence summaryAt a glance
Järvelä and Hadwin explain why productive computer-supported collaborative learning requires regulation at several interacting levels. Self-regulation concerns an individual’s planning, monitoring, motivation, emotion, and strategy use.
Key ideas
Co-regulation is temporary support through which one participant helps another regulate. Socially shared regulation occurs when a group jointly constructs goals, monitors progress, manages motivation and emotion, adapts strategies, and takes collective responsibility for the activity. Collaboration creates regulatory demands that individual-learning models alone cannot capture: participants must coordinate interpretations, effort, roles, standards, and responses to emerging challenges. The authors connect research on computer-based support for individual regulation with tools for collaborative knowledge construction and propose a new design frontier. Technology can prompt planning, externalise goals and progress, create shared awareness, support reflection, and make otherwise hidden processes available for group discussion.
Why it matters
Tools should not mechanically regulate learners or merely display data; prompts must be timed, meaningful, and integrated with the task. The article provides a conceptual agenda rather than definitive intervention evidence, calling for process-sensitive research that traces how regulation develops over time.
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- Vol 4 RE: App D
socially shared regulationregulation toolscollaborative learningshared goalslearning analyticsregulatory promptsgroup awarenesslearner agencyJournal article Evidence summaryAt a glance
Järvelä and colleagues develop design principles for technological tools that support socially shared regulation of learning in collaborative groups. Effective collaboration requires more than exchanging ideas: learners must regulate cognition, motivation, emotion, behaviour, goals, and strategy both individually and together.
Key ideas
The authors identify three connected design aims. Tools should increase awareness of one’s own and others’ learning processes; help learners externalise and share goals, plans, progress, and internal states; and prompt the activation or acquisition of regulatory processes when needed. Their illustrative tool uses structured prompts and shared visual information to help groups recognise challenges, discuss regulation, and coordinate action. Support should foster learners’ agency and shared responsibility rather than prescribe every step or substitute a dashboard for dialogue.
Why it matters
The article synthesises theory and prior evidence and demonstrates a design application, but it is not a conclusive trial of effects on achievement. Its contribution is a bridge from socially shared regulation theory to testable CSCL features, highlighting that timing, interpretation, group interaction, and integration with authentic tasks determine whether an intervention becomes useful regulation rather than additional workload.
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- Vol 4 RE: App D
multimodal datacollaborative regulationphysiological synchronyelectrodermal activitytemporal alignmentinteraction analysisregulatory episodesqualitative interpretationJournal article Evidence summaryAt a glance
Järvelä and colleagues examine how multimodal evidence can illuminate students’ regulation during collaborative learning. Regulation involves cognition, motivation, emotion, and behaviour unfolding over time, yet many processes are internal, transient, and difficult to capture with a single method.
Key ideas
The authors align video and interaction records with electrodermal activity and other process evidence to identify episodes in which learners encounter challenges, share arousal, and regulate together. Their methodological argument is that physiological data do not explain learning by themselves. Arousal may reflect many things, and meaningful inference requires temporal alignment with observable activity, discourse, task demands, and theory-based coding. Multimodal analysis can expose discrepancies between what learners report and what occurs, reveal synchrony or transitions, and help researchers locate consequential moments for closer qualitative interpretation. The study illustrates opportunities and limitations rather than offering a ready automated intervention.
Why it matters
Its contribution is a framework for grounding analytics in regulated-learning theory and preserving context. Future systems might support awareness and timely scaffolding, but indicators require validation, transparent interpretation, and careful attention to privacy and the risk of reducing complex regulation to convenient signals.
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- Vol 4 RE: App D
human–ai collaborationsocially shared regulationtrigger eventsmachine learningmultimodal tracesexplainable ailearner agencygroup dialogueJournal article Evidence summaryAt a glance
Järvelä, Nguyen, and Hadwin propose human–AI collaboration for socially shared regulation of learning rather than automation that replaces human judgement. Collaborative groups encounter trigger events—cognitive, motivational, emotional, or social challenges—that require members to notice a problem, interpret it, set or revise goals, select strategies, monitor action, and adapt together.
Key ideas
Multimodal traces and machine learning may help detect patterns that are difficult for participants or researchers to see in real time. The authors’ hybrid human–AI shared regulation model treats people and AI as interacting subsystems with different strengths: computation can integrate streams and recognise candidate events, while learners and teachers contribute contextual meaning, values, goals, agency, and responsibility. AI outputs should therefore prompt awareness, reflection, and group dialogue rather than issue opaque prescriptions. The paper illustrates a research programme and propositions, not a validated classroom product.
Why it matters
Key challenges include theoretical grounding, explainability, privacy, bias, data quality, timing, and maintaining learner control. Productive systems must be co-designed and evaluated for whether they genuinely strengthen regulation and learning.
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- Vol 4 RE: App D
regulatory activitiestrigger eventsmultimodal process dataepisode-rule miningphysiological arousalsequence analysislong short-term memorypredictive analyticsJournal article Evidence summaryAt a glance
Järvelä and colleagues investigate whether multimodal process data can identify and predict regulatory activity during collaborative learning. They focus on trigger events: difficult situations that may disrupt progress and invite groups to regulate cognition, motivation, emotion, or social interaction.
Key ideas
The study combines coded interaction with process and episode-rule mining and examines shared physiological arousal events as possible signals of moments requiring adaptation. Sequential analyses reveal patterns in socially shared regulation, and long short-term memory models show potential for predicting regulatory activities. The aim is not to equate arousal with regulation; physiological coincidence is an ambiguous indicator that gains meaning only when aligned with discourse, behaviour, task context, and theoretically grounded coding. Findings illustrate how AI might help researchers and, eventually, learners recognise consequential moments in complex collaboration.
Why it matters
Prediction accuracy alone does not establish educational benefit, and deployment would raise questions about generalisation, privacy, interpretation, and intervention timing. The work is best understood as a proof of concept for augmenting human awareness and inquiry, with further research needed before automated prompts can responsibly optimise group learning.
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- Vol 2 RE: Chapters 1, 4, 5, 7
expertise reversal effectcognitive load theoryworked examplesredundancy effectadaptive instructionprior knowledgeJournal article Evidence summaryAt a glance
Kalyuga reviews the expertise reversal effect: instructional methods that help novices can become ineffective or harmful as learners acquire domain knowledge. Cognitive load theory explains this change through the interaction between working-memory limits and schemas stored in long-term memory.
Key ideas
Novices lack schemas and therefore benefit from explicit explanations, worked examples, integrated information, and other external guidance. For knowledgeable learners, the same support may duplicate what their schemas already provide. Processing and reconciling redundant guidance then consumes resources without adding useful knowledge, so less guided problem solving can be superior. The review connects this evidence to aptitude–treatment interactions and surveys approaches for adapting instruction to current expertise, including fading worked steps, selecting formats according to prior knowledge, and rapid diagnostic assessment during learning. It emphasizes that expertise is domain- and task-specific rather than a fixed learner trait.
Why it matters
Effective adaptive instruction must therefore monitor changing knowledge and adjust support over time. The central design lesson is that no instructional format is universally best: guidance should be sufficient for the learner’s present needs and withdrawn as internal guidance develops.
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- Vol 2 TE: App B
goal orientation theorymastery goalsperformance goalsachievement motivationclassroom goal structuresself-regulated learningJournal article Evidence summaryAt a glance
Kaplan and Maehr review goal orientation theory, which explains achievement behaviour partly through the purposes learners perceive for engaging in a task. Mastery orientations emphasise understanding, improvement, and competence development; performance orientations concern demonstrating competence relative to others, with approach and avoidance distinctions adding complexity.
Key ideas
The authors argue that rapid expansion of measures and labels created theoretical vagueness, especially about whether orientations are stable traits, situational responses, or dynamic constructions. They outline six possible accounts in which goal orientations emerge from situation schemas, self-schemas, self-priming, psychological needs, values, or situated meaning-making. Convergent evidence links mastery-focused contexts with more adaptive engagement, while performance effects depend on definition, approach versus avoidance, and context. Classroom structures communicate what counts as success through tasks, authority, recognition, grouping, evaluation, and time.
Why it matters
The review cautions against treating questionnaire categories as fixed learner types or using simple prescriptions. Future research should clarify constructs, trace development and change, examine multiple goals and culture, and study how learners interpret situations while regulating attention, emotion, and action.
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- Vol 2 RE: Chapter 3, 8, App C, App D
- Vol 4 RE: Chapter 9
- Vol 5 RE: Chapters 5, 11
- Vol 5 TE: App C
productive failuretransfer learningnewtonian kinematicscollaborative problem solvingconceptual knowledgeinstructional consolidationJournal article Evidence summaryAt a glance
Kapur provides an existence proof for productive failure in a computer-supported collaborative-learning study of eleventh-grade science students solving complex Newtonian-kinematics problems. Groups asked to generate solutions without the usual support struggled, explored divergent representations, and performed poorly during the initial problem-solving phase.
Key ideas
Groups receiving more structured support achieved better immediate solutions. After subsequent consolidation, however, students from the initially unsuccessful condition outperformed their counterparts on transfer measures. Their failed attempts had activated prior knowledge, exposed conceptual gaps, differentiated important features, and produced representations that prepared them to understand later instruction. The result does not imply that leaving novices unsupported is generally effective. Productive failure depends on carefully chosen problems, opportunities to generate and compare ideas, a psychologically safe context, and later teaching that organises and explains the relevant concepts.
Why it matters
Initial performance and later learning are therefore not interchangeable measures. The study challenges designs that optimise short-term success alone and motivates a two-phase sequence: problem solving that makes knowledge gaps visible, followed by explicit consolidation that helps learners extract the underlying structure.
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- Vol 2 RE: Chapters 3, 8
productive failureproductive successtransfer-oriented assessmentdelayed learning outcomesinstructional sequencingsolution generationJournal article Evidence summaryAt a glance
Kapur separates performance during an initial learning activity from learning demonstrated later and uses that distinction to describe four instructional possibilities. Productive success combines strong initial performance with later learning; productive failure combines weak initial performance with later learning; unproductive failure yields neither; and unproductive success produces apparent immediate success without durable understanding or transfer.
Key ideas
Direct instruction may be productive relative to unguided discovery yet unproductive relative to better sequenced designs. Productive-failure approaches typically ask learners to generate and compare solutions to a carefully designed problem before explicit instruction. This process can activate prior knowledge, reveal gaps, draw attention to deep features, and prepare learners to understand canonical methods during consolidation. Failure is not valuable by itself: problems must be accessible yet challenging, learners need space to explore multiple representations, and subsequent instruction must connect their attempts with target concepts.
Why it matters
The framework expands the design space beyond a discovery-versus-instruction dichotomy and warns against evaluating learning from fluency or correctness during acquisition alone. Delayed, transfer-oriented assessments are needed to distinguish genuinely productive designs from superficially successful ones.
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- Vol 4 RE: Chapter 23
self-regulated learningteacher professional competencelearning strategy instructionmetacognitive reflectionprocess-focused feedbackteacher modellingJournal article Evidence summaryAt a glance
Karlen and Hertel introduce a special issue connecting teachers’ professional competence in self-regulated learning with the ways teachers promote it in ordinary classrooms. They position self-regulation as a future literacy that helps learners set goals, select and adapt strategies, monitor progress, manage motivation and emotion, and persist through changing demands.
Key ideas
Because these processes do not develop automatically, teachers need more than general encouragement to make students independent. The authors organize teacher support through the INSPIRE model: instructor, navigator, strategist, promoter, innovator, role model, and encourager. These roles include explicitly teaching and modelling strategies, designing environments that permit choice and reflection, diagnosing learner needs, giving process-focused feedback, demonstrating teachers’ own regulation, and sustaining students’ confidence and effort. The article also stresses teachers’ knowledge, beliefs, motivation, self-regulation, and diagnostic skills as conditions for effective practice.
Why it matters
It calls for closer connections among research, teacher education, professional development, and classroom observation so that self-regulated learning becomes an integrated feature of everyday instruction rather than an occasional add-on.
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- Vol 4 RE: Chapter 23
teacher self-regulated learningself-regulated learning promotionteacher competencestrategy knowledgediagnostic awarenessprofessional developmentJournal article Evidence summaryAt a glance
This study examines how teachers’ own self-regulated learning and their professional competence as promoters of self-regulation relate to students’ learning. The authors use a holistic competence framework that distinguishes teachers as self-regulated learners from teachers as agents who teach and support self-regulated learning.
Key ideas
Relevant competence includes knowledge of strategies and instruction, beliefs about learnability and value, motivation to promote self-regulation, teachers’ own strategy use, and classroom practices. Linking teacher and student data, the study tests whether these dimensions form pathways through which teachers influence students’ strategy knowledge, use, motivation, and achievement-related processes. The findings underline that teacher effects are not captured by a single disposition: different competence components make distinct contributions, and what teachers know or believe does not automatically translate into practice. Promotion is more likely when teachers possess coordinated knowledge, motivation, diagnostic awareness, and self-regulatory experience.
Why it matters
The article therefore argues for teacher education and professional development that integrate conceptual understanding with modelling, practice, observation, feedback, and reflection, enabling teachers to make self-regulated learning visible and teachable in everyday lessons.
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- Vol 4 RE: Chapters 3, 4, 8, App A, App C
- Vol 4 TE: App C
retrieval-based learningretrieval practiceactive recallspacing effectdelayed retentionmemory reconstructionJournal article Evidence summaryAt a glance
Karpicke argues that retrieval is not merely a neutral way to measure knowledge encoded earlier; retrieving changes memory and is itself a powerful learning event. Every expression of knowledge depends on available cues, and effortful retrieval strengthens future access, supports organisation, and can improve long-term learning and transfer.
Key ideas
Experiments comparing repeated study with retrieval practice show that rereading can produce short-term fluency and confidence while yielding poorer delayed retention. Retrieval can involve free recall, practice questions, explaining from memory, or reconstructing ideas, and should be followed by feedback when errors or omissions need correction. Effective practice is spaced and repeated after some forgetting rather than massed into immediate repetitions. Students often underuse retrieval because they identify learning with putting information in and mistake ease during study for durable mastery. The review cautions that retrieval tasks should align with desired understanding and not become narrow high-stakes testing.
Why it matters
Used as low-stakes learning, active recall makes gaps visible, improves monitoring, and strengthens the ability to use knowledge later. Encoding and elaboration still matter, but durable learning requires opportunities to reconstruct what was learned.
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- Vol 1 RE: Chapter 7
- Vol 2 RE: App D
- Vol 2 TE: App B
- Vol 3 RE: Chapter 4
- Vol 4 TE: App C
- Vol 5 RE: Chapter 7
retrieval practiceconcept mappingverbatim recallinference performancedelayed learningmetacognitive mismatchJournal article Evidence summaryAt a glance
Karpicke and Blunt compare retrieval practice with elaborative concept mapping using science texts. Students studied material and then either repeatedly retrieved it from memory or constructed concept maps while viewing the text.
Key ideas
On delayed tests one week later, retrieval practice produced substantially better verbatim recall and inference performance, even when the final assessment required creating a concept map. Learners nevertheless predicted that repeated study would be more effective, illustrating a metacognitive mismatch between immediate fluency and durable learning. The experiments do not show that concept maps are inherently ineffective. Their learning value depends on how they are used: constructing a map with the source present is elaborative study, whereas reconstructing relationships from memory can itself become retrieval practice. Active recall requires learners to search for and organise knowledge without external support, revealing gaps and strengthening later access.
Why it matters
The findings concern specific texts, schedules, and assessments, so application should include feedback and spacing. The central result is that meaningful learning is not produced only by elaborative encoding; effortful reconstruction from memory can improve conceptual understanding and transfer more than additional exposure.
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- Vol 7 RE: App D
- Vol 7 RE: Chapters 5, 6, 7, 11, 13, 14, App B
large language modelsartificial intelligence literacygenerated content verificationalgorithmic biasdata privacyassessment redesignhuman oversightJournal article Evidence summaryAt a glance
Kasneci and colleagues survey opportunities and challenges created by large language models in education shortly after ChatGPT’s public release. Potential uses include explanations, examples, dialogue, feedback, language support, writing assistance, assessment development, programming help, and teacher preparation of materials.
Key ideas
These systems may broaden access and personalise interaction, but fluent output is not reliable evidence of truth or understanding. Models can fabricate information, reproduce bias, expose private data, obscure sources, enable plagiarism, and encourage overreliance or reduced human judgement. Effects differ by learner knowledge: novices may be least able to detect plausible errors. The authors discuss implications for students, teachers, researchers, and institutions and argue for AI literacy, verification, transparent policies, redesigned assessment, and human oversight. LLMs should augment rather than replace educators or the social purposes of learning.
Why it matters
The article is a multidisciplinary position and review, not an evaluation demonstrating learning gains from ChatGPT. Its durable contribution is a balanced agenda: explore pedagogically grounded uses while studying accuracy, equity, privacy, agency, assessment validity, and the competencies needed to question and responsibly use generated content.
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- Vol 6 PE: App E
immunity to changecompeting commitmentsbig assumptionspsychological equilibriumdiagnostic mappingadaptive challengesbehavioral experimentationBook Evidence summaryAt a glance
Kegan and Lahey explain why sincere commitments and strong incentives often fail to produce lasting personal or organizational change. People may be pursuing a visible improvement goal while simultaneously protecting less visible competing commitments, such as avoiding embarrassment, conflict, loss of status, or evidence of inadequacy.
Key ideas
These protective behaviors are coherent responses to “big assumptions” about what would happen if the person acted differently. Together they form an immunity to change: a system that preserves psychological equilibrium while defeating the stated goal. The authors provide a four-column diagnostic map for identifying the improvement commitment, behaviors that work against it, hidden competing commitments, and underlying assumptions. Change then proceeds through safe, observable tests of those assumptions rather than willpower or exhortation alone.
Why it matters
Case studies apply the method to individuals, teams, and organizations and connect it with adult development, arguing that adaptive challenges require changes in meaning-making, not merely new technical skills. The framework treats resistance as intelligible and potentially protective, enabling leaders and learners to approach it with curiosity, evidence, and gradual experimentation.
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- Vol 4 RE: Chapter 24
self-regulated learningemotional intelligencestress copingpostgraduate wellbeingresearch-related pressureadaptive copingstudent supportJournal article Evidence summaryAt a glance
This quantitative study investigates how self-regulated learning, emotional intelligence, and coping with stress relate to postgraduate students’ wellbeing. The authors frame postgraduate research as a demanding activity that requires learners to organize time and resources, monitor progress, regulate motivation and emotion, and respond constructively to setbacks.
Key ideas
Survey data were used to estimate students’ levels on the focal constructs and examine correlations and predictive relationships with wellbeing. The reported analyses indicate positive associations between wellbeing and both self-regulated learning and emotional intelligence, while effective coping strategies also contribute to managing research-related pressure. The findings suggest that academic success and wellbeing should not be treated as separate concerns: planning, monitoring, emotional awareness, emotion management, and adaptive coping can jointly help students sustain productive research activity. The authors recommend that universities and supervisors provide explicit support for these capabilities through mentoring, counselling, workshops, and structured research guidance.
Why it matters
Because the study relies on self-report and a particular postgraduate context, its relationships should be interpreted as associative rather than definitive evidence of causation.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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