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Each entry identifies the volume, edition, chapter or appendix in which the work appears.
The evidence behind the series
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Browse the scientific research and professional literature used across all seven volumes of The Science of Learning for Educators.
7book volumes
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2 references
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- Vol 4 RE: Chapters 25, 27
artificial intelligenceself-regulated learningintelligent tutoring systemslearning analyticsconversational agentsadaptive learningmetacognitive regulationlearner agencyJournal article Evidence summaryAt a glance
Ren, Lee, and May systematically review how artificial intelligence has been used to support self-regulated learning in education.
Key ideas
Searching literature from 2004 through 2024, they retain twenty-seven studies and classify them by educational level, research method, subject area, AI technology, self-regulated-learning framework, and reported outcome. The review identifies intelligent tutoring and adaptive systems, learning analytics and prediction, conversational agents, and other data-driven supports that can prompt planning, monitoring, feedback use, strategy adjustment, and reflection. It also finds uneven theoretical grounding and limited evidence across contexts, with many studies concentrated in particular settings and phases of regulation.
Why it matters
The authors call for stronger alignment between AI functions and explicit self-regulation theory, more rigorous empirical designs, attention to learner agency and ethics, and research that tests sustained effects rather than short-term performance alone.
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- Vol 5 RE: Chapter 12
educational data mininglearning analyticsknowledge discovery cyclepredictive modelingprocess mininglearning management system dataalgorithmic interpretabilityeducational data privacyJournal article Evidence summaryAt a glance
Romero and Ventura survey the expanded fields of educational data mining and learning analytics.
Key ideas
Updating their earlier reviews, they map terminology, communities, milestones, and the knowledge-discovery cycle, then describe data from learning management systems, intelligent tutors, assessment platforms, games, social media, and institutional systems. The article organizes commonly used methods, including prediction, classification, clustering, relationship mining, process mining, text mining, and visualization, and links them to goals such as modeling learners, predicting performance, recommending resources, supporting teachers, and improving institutions. It also inventories tools and publicly available datasets and discusses privacy, interpretability, generalizability, and translation into educational decisions.
Why it matters
The survey is a broad field guide rather than evidence that every analytic technique improves learning; it highlights the need to connect technical models with educational theory, stakeholders, and ethical use.
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What it contributes
Evidence summaries explain the central idea and why the source matters to educators.
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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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