Table of Contents

• Stream Mining in Education? Dealing with EvolutionMyra Spiliopoulou. 3-4 [doi]
• From Text to Feedback: Leveraging Data Mining to Build Educational TechnologiesDanielle S. McNamara. 5-6 [doi]
• Five Aspirations for Educational Data MiningBob Dolan, John Behrens. 7-8 [doi]
• Assisting Instructor Assessment of Undergraduate Collaborative Wiki and SVN ActivitiesJihie Kim, Erin Shaw, Hao Xu, Adarsh G. V.. 10-16 [doi]
• Automated Student Model ImprovementKenneth R. Koedinger, Elizabeth A. McLaughlin, John C. Stamper. 17-24 [doi]
• Automatic Discovery of Speech Act Categories in Educational GamesVasile Rus, Arthur C. Graesser, Cristian Moldovan, Nobal B. Niraula. 25-32 [doi]
• Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor PredictionShubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan. 33-40 [doi]
• Comparison of methods to trace multiple subskills: Is LR-DBN best?Yanbo Xu, Jack Mostow. 41-48 [doi]
• Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive ModelsJosé P. González-Brenes, Jack Mostow. 49-56 [doi]
• Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance EvolutionJohn S. Kinnebrew, Gautam Biswas. 57-64 [doi]
• Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated LearningFrançois Bouchet, John S. Kinnebrew, Gautam Biswas, Roger Azevedo. 65-72 [doi]
• Learner Differences in Hint ProcessingIlya M. Goldin, Kenneth R. Koedinger, Vincent Aleven. 73-80 [doi]
• Methods to find the number of latent skillsBehzad Beheshti, Michel Desmarais, Rhouma Naceur. 81-86 [doi]
• Mining Student Behavior Patterns in Reading Comprehension TasksTerry Peckham, Gordon McCalla. 87-94 [doi]
• Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response TheoryYoav Bergner, Stefan Droschler, Gerd Kortemeyer, Saif Rayyan, Daniel T. Seaton, David E. Pritchard. 95-102 [doi]
• Predicting drop-out from social behaviour of studentsTomas Obsivac, Lubos Popelinsky, Jaroslav Bayer, Jan Geryk, Hana Bydzovska. 103-109 [doi]
• Searching for Variables and Models to Investigate Mediators of Learning from Multiple RepresentationsMartina Rau, Richard Scheines. 110-117 [doi]
• The Impact on Individualizing Student Models on Necessary Practice OpportunitiesJung In Lee, Emma Brunskill. 118-125 [doi]
• Sensor-free automated detection of affect in a Cognitive Tutor for AlgebraRyan Shaun Joazeiro de Baker, Sujith M. Gowda, Michael Wixon, Jessica Kalka, Angela Z. Wagner, Aatish Salvi, Vincent Aleven, Gail Kusbit, Jaclyn Ocumpaugh, Lisa M. Rossi. 126-133 [doi]
• Using Edit Distance to Mine for Errors in a Natural Language to Logic Translation CorpusDave Barker-Plummer, Robert Dale, Richard Cox, Alex Romanczuk. 134-141 [doi]
• Calculating Probabilistic Distance to Solution in a Complex Problem Solving DomainLeigh Ann Sudol, Kelly Rivers, Thomas K. Harris. 144-147 [doi]
• Classification via clustering for predicting final marks starting from the student participation in ForumsManuel Ignacio López, Cristóbal Romero, Sebastián Ventura, José María Luna. 148-151 [doi]
• Development of a Workbench to Address the Educational Data Mining BottleneckMa. Mercedes T. Rodrigo, Ryan Shaun Joazeiro de Baker, Bruce M. McLaren, Alejandra Jayme, Thomas Dy. 152-155 [doi]
• Early Prediction of Student Self-Regulation Strategies by Combining Multiple ModelsJennifer Sabourin, Bradford W. Mott, James C. Lester. 156-159 [doi]
• Identifying Experts from Interaction BehaviourJudi McCuaig, Julia Baldwin. 160-163 [doi]
• Interaction Networks: Generating High Level Hints Based on Network Community ClusteringsMichael Eagle, Matthew Johnson, Tiffany Barnes. 164-167 [doi]
• Investigating Practice Schedules of Multiple Fraction Representations Using Knowledge Tracing Based Learning Analysis TechniquesMartina Rau, Zachary A. Pardos. 168-171 [doi]
• Learning Gains for Core Concepts in a Serious Game on Scientific ReasoningCarol Forsyth, Philip I. Pavlik Jr., Arthur C. Graesser, Zhiqiang Cai, Mae-Lynn Germany, Keith K. Millis, Heather Butler, Diane F. Halpern, Robert Dolan. 172-175 [doi]
• Leveraging First Response Time into the Knowledge Tracing ModelYutao Wang, Neil T. Heffernan. 176-179 [doi]
• Meta-learning Approach for Automatic Parameter Tuning: A case of study with educational datasetsMaría De Mar Molina, Cristóbal Romero, Sebastián Ventura, José María Luna. 180-183 [doi]
• Mining Concept Maps to Understand Students' LearningJin Soung Yoo, Moon-Heum Cho. 184-187 [doi]
• Policy Building - An Extension To User ModelingMichael Yudelson, Emma Brunskill. 188-191 [doi]
• The real world significance of performance predictionZachary A. Pardos, Qing Yang Wang, Shubhendu Trivedi. 192-195 [doi]
• The Rise of the Super ExperimentJohn C. Stamper, Derek Lomas, Dixie Ching, Steven Ritter, Kenneth R. Koedinger, Jonathan Steinhart. 196-200 [doi]
• Incorporating Factors Influencing Knowledge Retention into a Student ModelYutao Wang, Joseph Beck. 201-203 [doi]
• A promising classification method for predicting distance students' performanceDiego García-Saiz, Marta E. Zorrilla. 206-207 [doi]
• Analyzing paths in a student databaseDonatella Merlini, Renza Campagni, Renzo Sprugnoli. 208-209 [doi]
• Analyzing the behavior of a teacher network in a Web 2.0 environmentEliana Scheihing, Carolina Aros, Daniel Guerra. 210-211 [doi]
• Automated Detection of Mentors and Players in an Educational GameFazel Keshtkar, Brent Morgan, Arthur Graesser. 212-213 [doi]
• Categorizing Students' Response Patterns using the Concept of Fractal DimensionRasil Warnakulasooriya, William Galen. 214-215 [doi]
• CurriM: Curriculum MiningMykola Pechenizkiy, Nikola Trcka, Paul De Bra, Pedro Toledo. 216-217 [doi]
• Data mining techniques for design of ITS student modelsRitu Chaturvedi, Christie I. Ezeife. 218-219 [doi]
• Deciding on Feedback Polarity and TimingStuart Johnson, Osmar R. Zaïane. 220-221 [doi]
• Finding Dependent Test Items: An Information Theory Based ApproachXiaoxun Sun. 222-223 [doi]
• Fit-to-Model Statistics for Evaluating Quality of Bayesian Student Ability EstimationLing Tan. 224-225 [doi]
• Inferring learners' knowledge from observed actionsAnna N. Rafferty, Michelle LaMar, Thomas L. Griffiths. 226-227 [doi]
• Learning Paths in a Non-Personalizing e-Learning EnvironmentAgathe Merceron, Liane Beuster, Margarita Elkina, Albrecht Fortenbacher, Leonard Kappe, Andreas Pursian, Sebastian Schwarzrock, Boris Wenzlaff. 228-229 [doi]
• Similarity Functions for Collaborative Master RecommendationsAlexandru Surpatean, Evgueni N. Smirnov, Nicolai Manie. 230-231 [doi]
• Speaking (and touching) to learn: a method for mining the digital footprints of face-to-face collaborationRoberto Martínez Maldonado, Kalina Yacef, Judy Kay. 232-233 [doi]
• Social Networks Analysis for Quantifying Students' Performance in TeamworkPedro Crespo, Claudia Antunes. 234-235 [doi]
• Stress Analytics in EducationRafal Kocielnik, Mykola Pechenizkiy, Natalia Sidorova. 236-237 [doi]
• Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial ResultsStephen Fancsali. 238-239 [doi]