Learning Analytics From Research to Practice

“This book is an edited volume concerning the current emerging topics of learning analytics (LA). … The book attempts to provide the first comprehensible reference book for LA by showcasing results, strategies, guidelines, methods, models and tools. … Larusson and White have produced an accessible and interesting introduction to LA, aimed at students, educators, researchers and educational stakeholders.” (Cristóbal Romero and Sebastián Ventura, Technology, Knowledge and Learning, Vol. 20, 2015)

“The book presents a range of excellent topics that are all interlinked with learning analytics. Each paper has a unique perspective on the topic and provides suitable examples and case studies. I would highly recommend this book to educational professionals seeking an informed view on learning analytics.” (S. M. Godwin, Computing Reviews, November, 2014)

Johann Ari Larusson is a Senior Research Scientist in the Center for Digital Data, Analytics and Adaptive Learning at Pearson. He first joined Pearson as a Senior Recommendation Engineer where he led research and development of recommendation engine technologies and adaptivity and analytics for the Alleyoop product. His research and applied work are primarily in the fields of educational technology, learning analytics, computer-mediated collaborative learning (CSCL) and software engineering. Prior to Pearson, he held various R&D positions in the technology industry, academia and banking. Even prior to educational technology, his work centered around transmission, manipulation and analysis of large volumes of (unstructured) data. Johann has authored and co-authored several peer-reviewed and award-winning publications, was a founding member and chair of the First North East Regional Learning Analytics Symposium, and has served as a reviewer for a number of journals and conferences. He holds a Ph.D. in Computer Science from Brandeis University.Brandon White is a doctoral candidate in English at the University of California, Berkeley, where his theoretical interests in pedagogical history intersect with inquiries in applied educational technology. His work and research in learning analytics primarily concerns language recasting as deployed in student writing, and as taken up by educational theory, linguistics, and psychology. He is particularly invested in developing methods for identifying, isolating, and weighing markers for students' success based on their written content. Before coming to Berkeley, he was a Teaching and Learning Fellow at Brandeis University, where he also earned his Master's degree in Cultural Production, and continued to work as a consultant in educational technology for Research and Instruction Services. He is the author of a number of peer-reviewed publications and has served on several advisory committees for teaching and learning in higher education.