Handbook of Educational Data Mining
1st Edition
Published on 25. October 2010
Book
Hardback
535 pages
978-1-4398-0457-5 (ISBN)
Description
Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. 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.
Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances
With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making.
Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.
Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances
With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making.
Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.
Reviews / Votes
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
More details
Series
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Researchers, statisticians, psychologists, engineers, and computer scientists in educational data mining.
Illustrations
140 s/w Abbildungen, 90 s/w Tabellen
90 Tables, black and white; 140 Illustrations, black and white
Dimensions
Height: 254 mm
Width: 178 mm
Weight
1088 gr
ISBN-13
978-1-4398-0457-5 (9781439804575)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Cristobal Romero | Sebastian Ventura | Mykola Pechenizkiy
Handbook of Educational Data Mining
E-Book
10/2010
1st Edition
CRC Press
€225.99
Available for download
Cristobal Romero | Sebastian Ventura | Mykola Pechenizkiy
Handbook of Educational Data Mining
E-Book
10/2010
1st Edition
CRC Press
€225.99
Available for download
Persons
Cristobal Romero is an associate professor in the Department of Computer Science at the University of Cordoba 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.
Sebastian Ventura is an associate professor in the Department of Computer Science at the University of Cordoba 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.
Sebastian Ventura is an associate professor in the Department of Computer Science at the University of Cordoba 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.
Content
Preface. Introduction. Basic Techniques, Surveys, and Tutorials. Case Studies. Index.