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5 references
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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 A, App C
- Vol 4 RE: Chapters 25, 27
artificial intelligenceself-regulated learningmetacognitive awarenessadaptive feedbacklearner agencyhuman-centred regulationlearning analyticsalgorithmic biasJournal article Evidence summaryAt a glance
Lan and Zhou qualitatively synthesise research on artificial-intelligence applications supporting self-regulated learning in higher education. They organise findings around phases and functions of regulation, including goal setting and planning, performance and monitoring, feedback and strategy adjustment, and reflection.
Key ideas
AI systems can collect and visualise learning data, recommend resources or pathways, provide adaptive prompts and feedback, support metacognitive awareness, and help learners make decisions. The review distinguishes human-centred regulation, in which learners retain access and control, from designs that risk outsourcing judgement to opaque automation. Evidence is uneven: studies concentrate on selected phases, tools, and short-term outcomes, while emotional and motivational regulation, long-term development, diverse populations, and transfer receive less attention. Challenges include privacy, bias, explainability, data quality, overreliance, learner agency, and whether recommendations genuinely improve regulation rather than compliance.
Why it matters
Because included interventions and methods are heterogeneous, the review does not establish a single effect size or prove that AI causes better learning. It calls for theory-grounded, longitudinal, and human-centred design in which AI augments learners’ awareness and choice and teachers remain responsible for pedagogical interpretation.
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- Vol 7 RE: Chapters 10, 13, 14, 15, App B, App D
learning analyticspredictive modellingmultimodal analyticsself-regulation analyticshuman-centred feedbackdata literacyalgorithmic fairnesslearning data governanceEdited book Evidence summaryAt a glance
This open, peer-reviewed handbook surveys learning analytics as a field concerned with collecting and interpreting data to understand and improve learning.
Key ideas
Its chapters introduce definitions and foundations, measurement, predictive modelling, network, language, multimodal, and temporal methods, and applications to self-regulation, collaboration, writing, discourse, assessment, and institutional decision-making. Later sections address implementation at scale, data literacy, fairness and bias, human-centred feedback, policy, privacy, and global K–12 perspectives. Across these topics, contributors repeatedly connect analytic techniques to learning theory, educational purposes, stakeholder interpretation, and responsible action rather than treating prediction as an end in itself. The volume is a field-level synthesis written by many authors, not evidence that every analytics intervention improves outcomes.
Why it matters
For educators and leaders, its key contribution is a map of design choices and risks: clarify the learning problem, select valid indicators, involve intended users, interpret data in context, evaluate consequences, and attend to equity, agency, and governance when analytics are deployed.
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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.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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