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Zusatztext Computer scientists review the current state in using large-scale educational data sets to understand learning better and to provide information about the learning process. ?-SciTech Book News! February 2011 Informationen zum Autor Cristóbal Romero is an associate professor in the Department of Computer Science at the University of Córdoba in Spain. Dr. Romero is a member of the International Working Group on Educational Data Mining and was conference co-chair of the Second International Conference on Educational Data Mining. His research interests include the application of artificial intelligence and data mining techniques to education and e-learning systems. Sebastián Ventura is an associate professor in the Department of Computer Science at the University of Córdoba in Spain. Dr. Ventura has been a reviewer for User Modelling and User Adapted Interaction, Information Sciences , and Soft Computing and was conference co-chair of the Second International Conference on Educational Data Mining. His research interests encompass machine learning, data mining, and their applications as well as the application of KDD techniques to e-learning. Mykola Pechenizkiy is an assistant professor in the Department of Computer Science at Eindhoven University of Technology in the Netherlands. Dr. Pechenizkiy has been involved in the organization of workshops, special tracks, and conferences on applications of data mining in medicine, industry, and education. He is conference co-chair of the Fourth International Conference on Educational Data Mining. His research is focused on knowledge discovery, data mining, machine learning, and their applications. Ryan Baker is an assistant professor of psychology and the learning sciences in the Department of Social Science and Policy Studies, with a collaborative appointment in computer science, at Worcester Polytechnic Institute in Massachusetts. An associate editor of the Journal of Educational Data Mining , Dr. Baker was program co-chair of the First International Conference on Educational Data Mining and conference chair of the Third International Conference on Educational Data Mining. His research is at the intersection of educational data mining, machine learning, human–computer interaction, and educational psychology. Klappentext This handbook helps education experts understand what types of questions EDM can address and helps data miners understand what types of questions are important to educational design and educational decision making. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. Zusammenfassung Educational data mining (EDM) is an emerging discipline concerned with developing methods for exploring the different types of data that come from an educational context. This book presents the applications of data mining techniques in education. Inhaltsverzeichnis Preface. Introduction. Basic Techniques, Surveys, and Tutorials. Case Studies. Index. ...
