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ERIC Number: ED537074
Record Type: Non-Journal
Publication Date: 2012-Jun
Pages: 257
Abstractor: ERIC
Reference Count: 0
ISBN: ISBN-978-1-7421-0276-4
Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)
International Educational Data Mining Society
The 5th International Conference on Educational Data Mining (EDM 2012) is held in picturesque Chania on the beautiful Crete island in Greece, under the auspices of the International Educational Data Mining Society (IEDMS). The EDM 2012 conference is a leading international forum for high quality research that mines large data sets of educational data to answer educational research questions. These data sets may come from learning management systems, interactive learning environments, intelligent tutoring systems, or any system used in a learning context. The following papers are presented at the conference: (1) Stream Mining in Education? Dealing with Evolution (Myra Spiliopoulou); (2) From Text to Feedback: Leveraging Data Mining to Build Educational Technologies (Danielle S. McNamara); (3) Five Aspirations for Educational Data Mining (Bob Dolan and John Behrens); (4) Assisting Instructional Assessment of Undergraduate Collaborative Wiki and SVN Activities (Jihie Kim, Erin Shaw, Hao Xu and Adarsh G V); (5) Automated Student Model Improvement (Kenneth R. Koedinger, Elizabeth A. McLaughlin and John C. Stamper); (6) Automatic Discovery of Speech Act Categories in Educational Games (Vasile Rus, Arthur Graesser, Cristian Moldovan and Nobal Niraula); (7) Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction (Shubhendu Trivedi, Zachary Pardos, Gabor Sarkozy and Neil Heffernan); (8) Comparison of methods to trace multiple subskills: Is LR-DBN best? (Yanbo Xu and Jack Mostow); (9) Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models (Jose Gonzalez-Brenes and Jack Mostow); (10) Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution (John S. Kinnebrew and Gautam Biswas); (11) Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning (Francois Bouchet, John S. Kinnebrew, Gautam Biswas and Roger Azevedo); (12) Learner Differences in Hint Processing (Ilya Goldin, Kenneth R. Koedinger and Vincent Aleven); (13) Methods to find the number of latent skills (Behzad Beheshti, Michel C. Desmarais and Rhouma Naceur); (14) Mining Student Behavior Patterns in Reading Comprehension Tasks (Terry Peckham and Gordon McCalla); (15) Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory (Yoav Bergner, Stefan Droschler, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton and David E. Pritchard); (16) Predicting drop-out from social behaviour of students (Tomas Obsivac, Lubomir Popelinsky, Jaroslav Bayer, Jan Geryk and Hana Bydzovska); (17) Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations (Martina A. Rau and Richard Scheines); (18) The Impact on Individualizing Student Models on Necessary Practice Opportunities (Jung In Lee and Emma Brunskill); (19) Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra (Ryan S.J.D. Baker, Sujith M. Gowda, Michael Wixon, Jessica Kalka, Angela Z. Wagner, Aatish Salvi, Vincent Aleven, Gail W. Kusbit, Jaclyn Ocumpaugh and Lisa Rossi); (20) Using Edit Distance to Analyse Errors in a Natural Language to Logic Translation Corpus (Dave Barker-Plummer, Robert Dale, and Richard Cox); (21) Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain (Leigh Ann Sudol, Kelly Rivers and Thomas K. Harris); (22) Classification via clustering for predicting final marks based on student participation in forums (M.I. Lopez, J.M. Luna, C. Romero, and S. Ventura); (23) Development of a Workbench to Address the Educational Data Mining Bottleneck (Ma. Mercedes T. Rodrigo, Ryan S. J. D. Baker, Bruce McLaren, Alejandra Jayme and Thomas T. Dy); (24) Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models (Jennifer L. Sabourin, Bradford W. Mott and James C. Lester); (25) Identifying Successful Learners from Interaction Behaviour (Judi McCuaig and Julia Baldwin); (26) Interaction Networks: Generating High Level Hints Based on Network Community Clusterings (Michael Eagle, Matthew Johnson and Tiffany Barnes); (27) Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques (Martina A. Rau and Zachary A. Pardos); (28) Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning (Carol Forsyth, Philip Pavlik Jr, Arthur C. Graesser, Zhiqiang Cai, Mae-Lynn Germany, Keith Millis, Heather Butler, Diane Halpern and Robert P. Dolan); (29) Leveraging First Response Time into the Knowledge Tracing Model (Yutao Wang and Neil T. Heffernan); (30) Meta-learning Approach for Automatic Parameter Tuning: A case of study with educational datasets (M.M. Molina, J.M. Luna, C. Romero, and S. Ventura); (31) Mining Concept Maps to Understand University Students' Learning (Jin Soung Yoo and Moon-Heum Cho); (32) Policy Building--An Extension To User Modeling (Michael V. Yudelson and Emma Brunskill); (33) The real world significance of performance prediction (Zachary A. Pardos, Qing Yang Wang and Shubhendu Trivedi); (34) The Rise of the Super Experiment (John C. Stamper, Derek Lomas, Dixie Ching, Steven Ritter, Kenneth R. Koedinger and Jonathan Steinhart); (35) Using Student Modeling to Estimate Student Knowledge Retention (Yutao Wang and Joseph Beck); (36) A promising classification method for predicting distance students' performance (Diego Garcia-Saiz and Marta Zorrilla); (37) Analyzing paths in a student database (Donatella Merlini, Renza Campagni and Renzo Sprugnoli); (38) Analyzing the behavior of a teacher network in a Web 2.0 environment (Eliana Scheihing, Carolina Aros and Daniel Guerra); (39) Automated Detection of Mentors and Players in an Educational Game (Fazel Keshtkar, Brent Morgan and Arthur Graesser); (40) Categorizing Students' Response Patterns using the Concept of Fractal Dimension (Rasil Warnakulasooriya and William Galen); (41) CurriM: Curriculum Mining (M. Pechenizkiy, N. Trcka, P. De Bra and Pedro A. Toledo); (42) Data mining techniques for design of ITS student models (Ritu Chaturvedi and C. I. Ezeife); (43) Deciding on Feedback Polarity and Timing (Stuart Johnson and Osmar Zaiane); (44) Finding Dependent Test Items: An Information Theory Based Approach (Xiaoxun Sun); (45) Fit-to-Model Statistics for Evaluating Quality of Bayesian Student Ability Estimation (Ling Tan); (46) Inferring learners' knowledge from observed actions (Anna N. Rafferty, Michelle M. Lamar and Thomas L. Griffiths); (47) Learning Paths in a Non-Personalizing e-Learning Environment (Agathe Merceron, Sebastian Schwarzrock, Margarita Elkina, Andreas Pursian, Liane Beuster, Albrecht Fortenbacher, Leonard Kappe, and Boris Wenzlaff); (48) Similarity Functions for Collaborative Master Recommendations (Alexandru Surpatean, Evgueni Smirnov and Nicolai Manie); (49) Social Networks Analysis for Quantifying Students' Performance in Teamwork (Pedro Crespo and Claudia Antunes); (50) Speaking (and touching) to learn: a method for mining the digital footprints of face-to-face collaboration (Roberto Martinez Maldonado, Kalina Yacef and Judy Kay); (51) Stress Analytics in Education (Rafal Kocielnik, Mykola Pechenizkiy and Natalia Sidorova); and (52) Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results (Stephen E. Fancsali). Individual papers contain figures, tables, references and footnotes. [Support for this publication was provided by Carnegie Learning, Pearson and LearnLab.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Collected Works - Proceedings
Education Level: Elementary Secondary Education; High Schools; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society