4. EDM 2011: Eindhoven, The Netherlands

Invited Talks (Abstracts)

• Barry Smyth:
  Social Information Discovery. 3
  
• Erik-Jan van der Linden:
  On Exploration and Mining of Data in Educational Practice. 5
  
• John C. Stamper:
  EDM and the 4th Paradigm of Scientific Discovery - Reflections on KDD Cup 2010. 7
  

Full Papers

• Nguyen Thai-Nghe, Tomás Horváth, Lars Schmidt-Thieme:
  Factorization Models for Forecasting Student Performance. 11-20
  
• Reihaneh Rabbany, Mansoureh Takaffoli, Osmar R. Zaïane:
  Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques. 21-30
  
• Nan Li, William W. Cohen, Kenneth R. Koedinger, Noboru Matsuda:
  A Machine Learning Approach for Automatic Student Model Discovery. 31-40
  
• Michel Desmarais:
  Conditions for Effectively Deriving a Q-Matrix from Data with Non-negative Matrix Factorization. Best Paper Award. 41-50
  
• Dave Barker-Plummer, Richard Cox, Robert Dale:
  Student Translations of Natural Language into Logic: The Grade Grinder Translation Corpus Release 1.0. 51-60
  
• Min Chi, Kenneth R. Koedinger, Geoffrey J. Gordon, Pamela W. Jordan, Kurt VanLehn:
  Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions. 61-70
  
• Adaeze Nwaigwe, Kenneth R. Koedinger:
  The Simple Location Heuristic is Better at Predicting Students' Changes in Error Rate Over Time Compared to the Simple Temporal Heuristic. 71-80
  
• Yue Gong, Joseph Beck:
  Items, Skills, and Transfer Models: Which Really Matters for Student Modeling? 81-90
  
• Kenneth R. Koedinger, Philip I. Pavlik Jr., John C. Stamper, Tristan Nixon, Steven Ritter:
  Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing. 91-100
  
• Bahador B. Nooraei, Zachary A. Pardos, Neil T. Heffernan, Ryan Shaun Joazeiro de Baker:
  Less is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data. 101-110
  
• Roberto Martínez Maldonado, Kalina Yacef, Judy Kay, Ahmed Sulaiman Kharrufa, Ammar Al-Qaraghuli:
  Analysing Frequent Sequential Patterns of Collaborative Learning Activity Around an Interactive Tabletop. Nominee for Best Paper Award. 111-120
  
• Kelly Wauters, Piet Desmet, Wim van den Noortgate:
  Acquiring Item Difficulty Estimates: a Collaborative Effort of Data and Judgment. Nominee for Best Paper Award. 121-128
  
• Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan:
  Spectral Clustering in Educational Data Mining. 129-138
  
• Yumeng Qiu, Yingmei Qi, Hanyuan Lu, Zachary A. Pardos, Neil T. Heffernan:
  Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing. 139-148
  
• Jack Mostow, José P. González-Brenes, Bao Hong (Lucas) Tan:
  Learning Classifiers From a Relational Database of Tutor Logs. 149-158
  
• Samad Kardan, Cristina Conati:
  A Framework for Capturing Distinguishing User Interaction Behaviors in Novel Interfaces. 159-168
  
• José P. González-Brenes, Jack Mostow, Weisi Duan:
  How to Classify Tutorial Dialogue? Comparing Feature Vectors vs. Sequences. 169-178
  
• Ryan Shaun Joazeiro de Baker, Sujith M. Gowda, Albert T. Corbett:
  Automatically Detecting a Student’s Preparation for Future Learning: Help Use is Key. 179-188
  
• Zachary A. Pardos, Sujith M. Gowda, Ryan Shaun Joazeiro de Baker, Neil T. Heffernan:
  Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System. 189-198
  
• Sujith M. Gowda, Jonathan P. Rowe, Ryan Shaun Joazeiro de Baker, Min Chi, Kenneth R. Koedinger:
  Improving Models of Slipping, Guessing, and Moment-By-Moment Learning with Estimates of Skill Difficulty. 199-208
  

Short Papers

• Annalies Vuong, Tristan Nixon, Brendon Towle:
  A Method for Finding Prerequisites Within a Curriculum. 211-216
  
• Emma Brunskill:
  Estimating Prerequisite Structure From Noisy Data. 217-222
  
• Dmitry I. Ignatov, Serafima Mamedova, Nikita Romashkin, Ivan Shamshurin:
  What Can Closed Sets of Students and Their Marks Say? 223-228
  
• Nikita Romashkin, Dmitry I. Ignatov, Elena Kolotova:
  How University Entrants are Choosing Their Department? Mining of University Admission Process with FCA Taxonomies. 229-234
  
• Marcelo Worsley, Paulo Blikstein:
  What’s an Expert? Using Learning Analytics to Identify Emergent Markers of Expertise through Automated Speech, Sentiment and Sketch Analysis. 235-240
  
• Yanbo Xu, Jack Mostow:
  Using Logistic Regression to Trace Multiple Sub-skills in a Dynamic Bayes Net. 241-246
  
• Kelly Wauters, Piet Desmet, Wim van den Noortgate:
  Monitoring Learners' Proficiency: Weight Adaptation in the Elo Rating System. 247-252
  
• Germán Cobo, David García-Solórzano, Eugènia Santamaria, Jose Antonio Morán, Javier Melenchón, Carlos Monzo:
  Modeling Students' Activity in Online Discussion Forums: A Strategy based on Time Series and Agglomerative Hierarchical Clustering. 253-258
  
• Gökhan Akçapinar, Erdal Cosgun, Arif Altun:
  Prediction of Perceived Disorientation in Online Learning Environment with Random Forest Regression. 259-264
  
• Vladimir Ivancevic, Milan Celikovic, Ivan Lukovic:
  Analyzing Student Spatial Deployment in a Computer Laboratory. 265-270
  
• Carlos Marquez-Vera, Cristóbal Romero, Sebastián Ventura:
  Predicting School Failure Using Data Mining. 271-276
  
• Philip I. Pavlik Jr., Sue-mei Wu:
  A Dynamical System Model of Microgenetic Changes in Performance, Efficacy, Strategy Use and Value during Vocabulary Learning. 277-282
  
• Jack Mostow, Yanbo Xu, Mdahaduzzaman Munna:
  Desperately Seeking Subscripts: Towards Automated Model Parameterization. 283-288
  
• Behrooz Mostafavi, Tiffany Barnes, Marvin J. Croy:
  Automatic Generation of Proof Problems in Deductive Logic. 289-294
  
• Mingyu Feng, Neil T. Heffernan, Zachary A. Pardos, Cristina Heffernan:
  Comparing of Traditional Assessment with Dynamic Testing in a Tutoring System. 295-300
  
• George Goguadze, Sergey A. Sosnovsky, Seiji Isotani, Bruce M. McLaren:
  Evaluating a Bayesian Student Model of Decimal Misconceptions. 301-306
  
• Dovan Rai, Joseph Beck:
  Exploring User Data From a Game-like Math Tutor: A Case Study in Causal Modeling. 307-313
  

Posters

• Arnon Hershkovitz, Ryan Shaun Joazeiro de Baker, Janice D. Gobert, Michael Wixon:
  Goal Orientation and Changes of Carelessness over Consecutive Trials in Science Inquiry. 315-316
  
• Antonio R. Anaya, Jesus Boticario:
  Towards Improvements on Domain-independent Measurements for Collaborative Assessment. 317-318
  
• Rafael Pedraza Perez, Cristóbal Romero, Sebastián Ventura:
  A Java Desktop Tool for Mining Moodle Data. 319-320
  
• Alfredo Zapata Gonzalez, Víctor Hugo Menéndez-Domínguez, Manuel E. Prieto, Cristóbal Romero:
  Using Data Mining in a Recommender System to Search for Learning Objects in Repositories. 321-322
  
• Diego García-Saiz, Marta E. Zorrilla:
  E-learning Web Miner: A Data Mining Application to Help Instructors Involved in Virtual Courses. 323-324
  
• Qiwei He, Bernard P. Veldkamp, Gerben Westerhof:
  Computerized Coding System for Life Narratives to Assess Students' Personality Adaption. 325-326
  
• Emma Brunskill, Stuart Russell:
  Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System. 327-328
  
• Joana Barracosa, Cláudia Antunes:
  Mining Teaching Behaviors from Pedagogical Surveys. 329-330
  
• Stephen Fancsali:
  Variable Construction and Causal Modeling of Online Education Messaging Data: Initial Results. 331-332
  
• Carina S. González, Pedro A. Toledo:
  The Hospital Classrooms Environments Challenge. 333-334
  
• Osamu Yamakawa:
  Combining Study of Complex Network and Text Mining Analysis to Understand Growth Mechanism of Communities on SNS. 335-336
  
• Yanbo Xu, Jack Mostow:
  Logistic Regression in a Dynamic Bayes Net Models Multiple Subskills Better! 337-338
  
• Edgar Cambranes-Martinez, Judith Good:
  Studying the Problem-solving Strategies in the Early Stages of Learning Programming. 339-340
  
• Anjo Anjewierden, Hannie Gijlers, Nadira Saab, Robert de Hoog:
  Brick: Mining Pedagogically Interesting Sequential Patterns. 341-342
  
• Lorenzo Moreno, Carina S. González, Román Estévez, Béatrice Pesquet-Popescu:
  Intelligent Evaluation of Social Knowledge Building Using Conceptual Maps with MLN. 343-344
  
• Florian Lemmerich, Marianus Ifland, Frank Puppe:
  Identifying Influence Factors on Students Success by Subgroup Discovery. 345-346
  
• Dorina Kabakchieva, Kamelia Stefanova, Valentin S. Kisimov:
  Analyzing University Data for Determining Student Profiles and Predicting Performance. 347-348
  
• Matt Johnson, Michael Eagle, Leena Joseph, Tiffany Barnes:
  The EDM Vis Tool. 349-350
  
• Yutao Wang, Neil T. Heffernan:
  Towards Modeling Forgetting and Relearning in ITS: Preliminary Analysis of ARRS Data. 352
  
• Alina Von Davier:
  Quality Control and Data Mining Techniques Applied to Monitoring Scaled Scores. 353-354
  
• Anna Lea Dyckhoff, Dennis Zielke, Mohamed Amine Chatti, Ulrik Schroeder:
  eLAT: An Exploratory Learning Analytics Tool for Reflection and Iterative Improvement of Technology Enhanced Learning. 355-356
  
• Judith Zimmermann, Kay Henning Brodersen, Jean-Philippe Pellet, Elias August, Joachim M. Buhmann:
  Predicting Graduate-level Performance from Undergraduate Achievements. 357-358
  
• Irena Koprinska:
  Mining Assessment and Teaching Evaluation Data of Regular, Advanced Stream Students. 359-360
  
• Agathe Merceron:
  Investigating Usage of Resources in LMS with Specific Association Rules. 361-362
  
• Marta E. Zorrilla, Diego García-Saiz, José L. Balcázar:
  Towards Parameter-free Data Mining: Mining Educational Data with Yacaree. 363-364
  
• Leigh Ann Sudol-DeLyser, Jonathan Steinhart:
  Factors Impacting Novice Code Comprehension in a Tutor for Introductory Computer Science. 365-366
  
• Paul Salvador Inventado, Roberto S. Legaspi, Merlin Suarez, Masayuki Numao:
  Investigating the Transitions between Learning and Non-learning Activities as Students Learn Online. 367-368
  
• Sébastien Lallé, Vanda Luengo:
  Learning Parameters for a Knowledge Diagnostic Tools in Orthopedic Surgery. 369-370
  
• Petr Jarusek, Radek Pelánek:
  Problem Response Theory and its Application for Tutoring. 371-372
  
• Michael Yudelson, Philip I. Pavlik, Kenneth R. Koedinger:
  Towards Better Understanding of Transfer in Cognitive Models of Practice. 373-374
  

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