Handbook of educational data mining
- edited by Cristóbal Romero ... [et al.].
- Boca Raton, FL : CRC Press, c2011.
- Physical description
- xxii, 513 p. : ill., maps ; 26 cm.
- Chapman & Hall/CRC data mining and knowledge discovery series.
At the library
Education Library (Cubberley)
|LB1028.43 .H355 2011||Unknown|
- Romero, C. (Cristobal)
- Includes bibliographical references and index.
- Preface, Joseph E. Beck Introduction, Cristobal 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, Cristobal Romero, Sebastian 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 Trcka, Mykola Pechenizkiy, and Wil van der Aalst Modeling Hierarchy and Dependence among Task Responses in EDM, Brian W. Junker Case Studies 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, Cristobal Romero, and Sebastian 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 Millan Index.
- (source: Nielsen Book Data)
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.
(source: Nielsen Book Data)
- Publication date
- Chapman & Hall/CRC data mining and knowledge discovery series
- "A Chapman & Hall Book".
- 9781439804575 (hardcover : alk. paper)
- 1439804575 (hardcover : alk. paper)
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