Where it is used
Each entry identifies the volume, edition, chapter or appendix in which the work appears.
The evidence behind the series
Follow the research.
Explore every source.
Browse the scientific research and professional literature used across all seven volumes of The Science of Learning for Educators.
7book volumes
8author ranges
1searchable library
Search the collection
Find a paper, author or idea.
Choose an author range, search a title or concept, or narrow the collection by source type. Open any result to see where it appears in the books, read its evidence summary and follow available source links.
5 references
-
- Vol 4 RE: Chapter 6
self-regulated learningmultimodal dataartificial intelligencetemporal process analysisaffective measurementconstruct validityprivacy in learning analyticsJournal article Evidence summaryAt a glance
De Mooij and colleagues systematically review how multimodal data and artificial intelligence have been used to study self-regulated learning. They map studies onto a grid connecting cognitive, affective, metacognitive, and motivational processes with behavioural, contextual, physiological, and other data streams.
Key ideas
The review distinguishes unimodal, horizontal, vertical, and integrated analytic approaches and examines whether methods capture regulation as a temporal and sequential process across learning contexts. Research is moving beyond single self-report measures toward combinations such as log data, gaze, discourse, facial or physiological signals, and performance evidence. However, motivation and affect remain underrepresented, studies define and align modalities inconsistently, and conventional statistics are often used even when integrated temporal analysis is needed. AI offers possibilities for pattern detection but does not remove problems of construct validity, interpretation, privacy, or context.
Why it matters
The authors call for theoretically grounded measurement, transparent alignment of indicators with processes, stronger validation, and interdisciplinary designs that connect rich sensing with educationally meaningful support.
-
- Vol 4 TE: App C
self-regulated learningmetacognitive processescognitive strategiesacademic achievementstrategy measurementsubject-specific instructionJournal article Evidence summaryAt a glance
Dent and Koenka synthesise research on associations between self-regulated learning and academic achievement from elementary through secondary school. Separate meta-analyses examine metacognitive processes—such as planning, monitoring, and regulation—and cognitive strategies, including rehearsal, organisation, and elaboration.
Key ideas
Average correlations with achievement are positive but modest: approximately .20 for metacognitive processes and .11 for cognitive strategies. Effects vary systematically by the particular process or strategy, school level, subject, how self-regulation is measured, and how achievement is assessed. Measures closer to actual learning activity often differ from broad self-report questionnaires, illustrating why method matters. Because most included evidence is correlational, the synthesis does not establish that increasing a reported strategy directly causes higher achievement.
Why it matters
The findings nevertheless support teaching learners to plan, monitor understanding, select strategies, and adapt, while embedding regulation in subject-specific tasks. Researchers and schools should use multiple, context-sensitive measures and avoid treating self-regulation as a single stable trait.
View or buy the cited work >>Read a similar article free online >>
-
- Vol 4 RE: App A, App C
self-regulated learning interventionsmetacognitive reflectionstrategy instructionmotivational componentsindependent practicesubject-embedded instructionteacher-delivered programmesJournal article Evidence summaryAt a glance
Dignath and Büttner meta-analyse school-based interventions intended to foster self-regulated learning at primary and secondary levels. They examine effects on academic performance, cognitive and metacognitive strategies, and motivation, while coding programme features, theoretical orientation, instructional approach, duration, subject, and implementer.
Key ideas
Overall, interventions produce positive effects, but outcomes vary across age groups and designs. Programmes are more effective when strategy instruction is explicit, learners receive knowledge about why and when strategies work, and metacognitive reflection links planning, monitoring, and evaluation with actual subject tasks. Motivational components and opportunities for independent practice also matter. Primary-school interventions often show strong effects, demonstrating that self-regulation need not wait until adolescence.
Why it matters
Teacher-delivered programmes can be effective when teachers are prepared and implementation is well supported. The findings favour integrated, subject-embedded instruction over isolated study-skills advice, while heterogeneity and variable study quality caution against assuming that any single component or programme format will work equally well in every context.
-
- Vol 5 RE: Chapter 15
intelligent tutoring systemslearning analyticsnatural language processinglarge language modelsgenerative aimultimodal learning analyticshuman–ai collaborationself-regulated learningPreprint Evidence summaryAt a glance
Feng, Zhang, and Gašević map changes in artificial intelligence in education by analysing 2,398 articles published from 2020 through 2024 across eight core AIED and learning-analytics venues. Their three-level keyword co-occurrence network analysis examines the field’s overall structure, major knowledge clusters, and emerging bridging topics.
Key ideas
Established technical themes—including intelligent tutoring systems, learning analytics, natural language processing, MOOCs, computer vision, and causal inference—remain prominent. Longitudinal patterns show the rapid rise of large language models and generative AI, alongside multimodal learning analytics and human–AI collaboration. Within generative-AI work, personalisation, self-regulated learning, feedback, assessment, motivation, and ethics are central interests. The authors interpret these developments as movement toward more co-adaptive and human-centred educational AI, while the field remains strongly technology focused.
Why it matters
Because results depend on selected venues, author keywords, preprocessing, and network thresholds, the map describes publication patterns rather than establishing instructional effectiveness or forecasting which technologies will endure.
-
- Vol 4 RE: Chapter 18
generative artificial intelligenceself-regulated learningutility valuecognitive strategiesstudent engagementphysics instructionenglish instructionteacher oversightJournal article Evidence summaryAt a glance
Fütterer and colleagues test whether customised generative-AI support can strengthen self-regulated learning in regular secondary classrooms. In a randomized trial, 371 students in grades seven to nine completed six physics or English sessions in one of three conditions: a CustomGPT targeting utility value, a CustomGPT prompting cognitive learning strategies, or standard ChatGPT.
Key ideas
The utility-value condition produced a more favourable change in perceived usefulness than the cognitive-strategy condition. Neither tailored intervention showed clear advantages over the control for effort, domain knowledge, or elaboration-based strategy use. Exploratory analyses suggested that more meaningful engagement with the AI was associated with better-maintained interest, and effects did not differ clearly by subject. The findings provide limited, differentiated evidence rather than showing that generic AI automatically improves self-regulation.
Why it matters
Carefully designed prompts may support motivation, but strategic behaviour and learning outcomes require stronger instructional integration, reliable measures, and teacher oversight. The supplied citation calls this work a systematic review; the verified publication is an open-access randomized controlled trial.
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.
From sources to a coherent pathway