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.
Table of Contents
Preface, Joseph E. Beck
Introduction, Cristóbal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan Baker
Basic Techniques, Surveys, and Tutorials
Visualization in Educational Environments, Riccardo Mazza
Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments, Judy Sheard
A Data Repository for the EDM Community: The PSLC DataShop, Kenneth R. Koedinger, Ryan Baker, Kyle Cunningham, Alida Skogsholm, Brett Leber, and John Stamper
Classifiers for EDM, Wilhelmiina Hamalainen and Mikko Vinni
Clustering Educational Data, Alfredo Vellido, Felix Castro, and Angela Nebot
Association Rule Mining in Learning Management Systems, Enrique Garcia, Cristóbal Romero, Sebastián Ventura, Carlos de Castro, and Toon Calders
Sequential Pattern Analysis of Learning Logs: Methodology and Applications, Mingming Zhou, Yabo Xu, John C. Nesbit, and Philip H. Winne
Process Mining from Educational Data, Nikola Trčka, Mykola Pechenizkiy, and Wil van der Aalst
Modeling Hierarchy and Dependence among Task Responses in EDM, Brian W. Junker
Novel Derivation and Application of Skill Matrices: The q-Matrix Method, Tiffany Barnes
EDM to Support Group Work in Software Development Projects, Judy Kay, Irena Koprinska, and Kalina Yacef
Multi-Instance Learning versus Single-Instance Learning for Predicting the Student’s Performance, Amelia Zafra, Cristóbal Romero, and Sebastián Ventura
A Response-Time Model for Bottom-Out Hints as Worked Examples, Benjamin Shih, Kenneth R. Koedinger, and Richard Scheines
Automatic Recognition of Learner Types in Exploratory Learning Environments, Saleema Amershi and Cristina Conati
Modeling Affect by Mining Students’ Interactions within Learning Environments, Manolis Mavrikis, Sidney D’Mello, Kaska Porayska-Pomsta, Mihaela Cocea, and Art Graesser
Measuring Correlation of Strong Symmetric Association Rules in Educational Data, Agathe Merceron and Kalina Yacef
Data Mining for Contextual Educational Recommendation and Evaluation Strategies, Tiffany Y. Tang and Gordon G. McCalla
Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles, Daniela Godoy and Analia Amandi
Log-Based Assessment of Motivation in Online Learning, Arnon Hershkovitz and Rafi Nachmias
Mining Student Discussions for Profiling Participation and Scaffolding Learning, Jihie Kim, Erin Shaw, and Sujith Ravi
Analysis of Log Data from a Web-Based Learning Environment: A Case Study, Judy Sheard
Bayesian Networks and Linear Regression Models of Students’ Goals, Moods, and Emotions, Ivon Arroyo, David G. Cooper, Winslow Burleson, and Beverly P. Woolf
Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources, David Masip, Julia Minguillon, and Enric Mor
Anticipating Student’s Failure as soon as Possible, Claudia Antunes
Using Decision Trees for Improving AEH Courses, Javier Bravo, Cesar Vialardi, and Alvaro Ortigosa
Validation Issues in EDM: The Case of HTML-Tutor and iHelp, Mihaela Cocea and Stephan Weibelzahl
Lessons from Project LISTEN’s Session Browser, Jack Mostow, Joseph E. Beck, Andrew Cuneo, Evandro Gouvea, Cecily Heiner, and Octavio Juarez
Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks, Zachary A. Pardos, Neil T. Heffernan, Brigham S. Anderson, and Cristina L. Heffernan
Mining for Patterns of Incorrect Response in Diagnostic Assessment Data, Tara M. Madhyastha and Earl Hunt
Machine-Learning Assessment of Students’ Behavior within Interactive Learning Environments, Manolis Mavrikis
Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator, Philippe Fournier-Viger, Roger Nkambou, and Engelbert Mephu Nguifo
Using Markov Decision Processes for Automatic Hint Generation, Tiffany Barnes, John Stamper, and Marvin Croy
Data Mining Learning Objects, Manuel E. Prieto, Alfredo Zapata, and Victor H. Menendez
An Adaptive Bayesian Student Model for Discovering the Student’s Learning Style and Preferences, Cristina Carmona, Gladys Castillo, and Eva Millán
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.
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