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9 Data Mining and Learning Analytics Applications in Educational Research Samira ElAtia, Donald Ipperciel & Osmar Zaïane Series: Wiley Series on Methods and Applications in Data Mining ISBN: 978-1-118-99823-6 | Nov 2016 | 320PP This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. The initial series of chapters offers a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles: prediction, clustering, rule association, and outlier detection. The next series of chapters showcases the pedagogical applications of Educational Data Mining (EDM) and features case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research, from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. About the Authors Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. Osmar R. Zaïane is Professor of Computing Science at the University of Alberta, Canada. To request review copies [email protected] Back to contents Education


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