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Learning Analytics Goes to School presents a framework for engaging in education research and improving education practice through the use of newly available data sources and analytical approaches. The application of data-intensive research techniques to understanding and improving learning environments has been growing at a rapid pace. In this book, three leading researchers convey lessons from their own experiences-and the current state of the art in educational data mining and learning analytics more generally-by providing an explicit set of tools and processes for engaging in collaborative data-intensive improvement.
Dr. Andrew Krumm is Director of Learning Analytics Research at Digital Promise, a nonprofit organization that brings together the expertise of educators, researchers, and technology developers in the interest of improving teaching and learning. Dr. Krumm has launched multiple research-practice partnerships and his research addresses the use of data-intensive research techniques to improve learning environments. Dr. Barbara Means is Executive Director for Learning Sciences Research at Digital Promise. Formerly the founder and director of the Center for Technology in Learning at SRI International, Dr. Means is a nationally recognized expert in defining issues and approaches for evaluating the implementation and efficacy of technology-supported educational innovations. Dr. Marie Bienkowski is Director of the Center for Technology in Learning at SRI International, a nonprofit research and development organization based in Silicon Valley that takes innovative ideas and technologies from the laboratory to the end-user and marketplace. Dr. Bienkowski is a computer scientist and education researcher leading efforts to improve student learning, effective teaching, and meaningful assessment.
1. Introduction 2. Data Used in Educational Data-Intensive Research 3. Methods Used in Educational Data-Intensive Research 4. Legal and Ethical Issues in Using Educational Data 5. Foundations of Collaborative Applications of Educational Data Mining and Learning Analytics 6. Supporting Conditions for Collaborative Data-Intensive Improvement 7. Five Phases of Collaborative Data-Intensive Improvement 8. Lessons Learned and Prospects for the Future
