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.
"Learning Analytics Goes to School provides a clear and practical overview of how to harness excitement over big data and learning analytics in education for educational improvement at scale. The approach outlined by the authors provides concrete guidance for how research-practice partnerships can use large data sets, new analytic techniques, and methods of improvement science to design and test solutions to problems of practice. It is a must read and great reference book for those new to educational data science, as well as those seeking to embrace a more collaborative approach to education research."
—William R. Penuel, Professor of Learning Sciences and Human Development, University of Colorado, USA
"Learning Analytics Goes to School is for anyone interested in understanding the growing use of data pertaining to students and their digitally-mediated learning activities. This book provides a thorough and thoughtful discussion of the primary issues related to educational data, and a step-by-step guide to addressing these issues by implementing a process called ‘Collaborative Data-intensive Improvement’ (CDI). The authors demystify jargon, lay out the basic concepts of data science for education, and provide a roadmap for creating research-practice partnerships aimed at producing reliably positive outcomes for all students. Written in a style that is both professional and accessible, this will be a valuable resource for teachers and administrators as well as researchers."
—Stephanie D. Teasley, Research Professor in the School of Information at the University of Michigan, and President of the Society for Leaning Analytics Research (SoLAR), USA
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