Data Mining And Learning Analytics: Applications In Educational Research

Data mining is an increasingly valuable tool within the institution of higher education. Educational Data Mining (EDM) has emerged as a field in its own right, focusing on data mining techniques to advance teaching, learning, and research in higher education. The Handbook of Data Mining in Educational Research addresses the impacts of data mining on education for the mutual benefit of computer science and educational practitioners and researchers.
The book is divided into three parts. Part I defines and discusses the four guiding principles of data mining, and the type and organization of data needed to carry a successful EDM study at both the macro and micro levels. Part II showcases the pedagogical applications of EDM and contains case studies from Business education, Health Sciences education, and Physical Sciences education to highlight the successful and not-so-successful application of data mining research in an educational setting.
Part III focuses exclusively on EDM as a part of advancing educational research. Applications of EDM include the identification of at-risk students, the potential to close socioeconomic gaps in achievement, teacher evaluation, and peer conferencing.

Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education.

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. He has authored several books and has contributed chapters and articles in more than 60 publications.? Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010.

Osmar R. Zaiane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.