Handbook of educational data mining. (©2011)

• Record Type: Book
• Further Information: Note: Edited by Cristóbal Romero [and others].
• Other Names: Romero, C (Cristobal)
• 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 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, 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 Index
• Publisher Details: Boca Raton, FL : CRC Press
• Publication Date: 2011
• Copyright Date: 2011
• Extent: 1 online resource (xxii, 513 pages), illustrations, map
• Subjects: 370.285/6312; Education -- Data processing; Computer-assisted instruction; Data mining -- Handbooks, manuals, etc; Education; EDUCATION -- Organizations & Institutions; Computer-assisted instruction; Data mining; Education -- Data processing; Electronic books; Handbooks and manuals
• Languages: English
• ISBNs: 9781439804582; 1439804583
• Related ISBNs: 9781439804575; 1439804575
• Notes: Note: Includes bibliographical references and index.; Note: Print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.