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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.
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1 reference
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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.
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.
From sources to a coherent pathway