Romero C. et al. (Eds.) Handbook of Educational Data Mining

CRC Press, 2010. — 535 p. — (Data Mining and Knowledge Discovery Series). — ISBN: 1439804575, 9781439804575.Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy and Ryan S.J.d. Baker, "Handbook of Educational Data Mining" . 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.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 Modeling 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.Cristóbal Romero, Sebastián Ventura, Mykola Pechenizkiy, and Ryan S. J. d. BakerBasic Techniques, Surveys and Tutorials
Riccardo Mazza
Visualization in Educational Environments
Judy Sheard
Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments
Kenneth R. Koedinger, Ryan S. J. d. Baker, Kyle Cunningham, Alida Skogsholm, Brett Leber, and John Stamper
A Data Repository for the EDM Community: The PSLC DataShop
Wilhelmiina Hämäläinen and Mikko Vinni
Classifiers for Educational Data Mining
Alfredo Vellido, Félix Castro, and Àngela Nebot
Clustering Educational Data
Enrique García, Cristóbal Romero, Sebastián Ventura, Carlos de Castro, and Toon Calders
Association Rule Mining in Learning Management Systems
Mingming Zhou, Yabo Xu, John C. Nesbit, and Philip H. Winne
Sequential Pattern Analysis of Learning Logs: Methodology and Applications
Nikola Trčka, Mykola Pechenizkiy, and Wil van der Aalst
Process Mining from Educational Data
Brian W. Junker
Modeling Hierarchy and Dependence among Task Responses in Educational Data Mining
Case Studies
Tiffany Barnes
Novel Derivation and Application of Skill Matrices: The q-Matrix Method
Judy Kay, Irena Koprinska, and Kalina Yacef
Educational Data Mining to Support Group Work in Software Development Projects
Amelia Zafra, Cristóbal Romero, and Sebastián Ventura
Multi-Instance Learning versus Single-Instance Learning for Predicting the Student’s Performance
Benjamin Shih, Kenneth R. Koedinger, and Richard Scheines
A Response-Time Model for Bottom-Out Hints as Worked Examples
Saleema Amershi and Cristina Conati
Automatic Recognition of Learner Types in Exploratory Learning Environments
Manolis Mavrikis, Sidney D’Mello, Kaska Porayska-Pomsta, Mihaela Cocea, and Art Graesser
Modeling Affect by Mining Students’ Interactions within Learning Environments
Agathe Merceron and Kalina Yacef
Measuring Correlation of Strong Symmetric Association Rules in Educational Data
Tiffany Y. Tang and Gordon G. McCalla
Data Mining for Contextual Educational Recommendation and Evaluation Strategies
Daniela Godoy and Analía Amandi
Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles
Arnon Hershkovitz and Rafi Nachmias
Log-Based Assessment of Motivation in Online Learning
Jihie Kim, Erin Shaw, and Sujith Ravi
Mining Student Discussions for Profiling Participation and Scaffolding Learning
Judy Sheard
Analysis of Log Data from a Web-Based Learning Environment: A Case Study
Ivon Arroyo, David G. Cooper, Winslow Burleson, and Beverly P. Woolf
Bayesian Networks and Linear Regression Models of Students’ Goals, Moods, and Emotions
David Masip, Julià Minguillón, and Enric Mor
Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources
Cláudia Antunes
Anticipating Students’ Failure As Soon As Possible
Javier Bravo, César Vialardi, and Alvaro Ortigosa
Using Decision Trees for Improving AEH Courses
Mihaela Cocea and Stephan Weibelzahl
Validation Issues in Educational Data Mining: The Case of HTML-Tutor and iHelp
Jack Mostow, Joseph E. Beck, Andrew Cuneo, Evandro Gouvea, Cecily Heiner, and Octavio Juarez
Lessons from Project LISTEN’s Session Browser
Zachary A. Pardos, Neil T. Heffernan, Brigham S. Anderson, and Cristina L. Heffernan
Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks
Tara M. Madhyastha and Earl Hunt
Mining for Patterns of Incorrect Response in Diagnostic Assessment Data
Manolis Mavrikis
Machine-Learning Assessment of Students’ Behavior within Interactive Learning Environments
Philippe Fournier-Viger, Roger Nkambou, and Engelbert Mephu Nguifo
Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator
Tiffany Barnes, John Stamper, and Marvin Croy
Using Markov Decision Processes for Automatic Hint Generation
Manuel E. Prieto, Alfredo Zapata, and Victor H. Menendez
Data Mining Learning Objects
Cristina Carmona, Gladys Castillo, and Eva Millán
An Adaptive Bayesian Student Model for Discovering the Student’s Learning Style and Preferences