3. EDM 2010: Pittsburgh, PA, USA

• Ivon Arroyo, Hasmik Meheranian, Beverly Park Woolf:
  Effort-based Tutoring: An Empirical Approach to Intelligent Tutoring. 1-10
  
• Ryan Shaun Joazeiro de Baker, Sujith M. Gowda:
  An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools. 11-20
  
• Michel C. Desmarais, Ildikó Pelczer:
  On the Faithfulness of Simulated Student Performance Data. 21-30
  
• Sidney K. D'Mello, Arthur C. Graesser:
  Mining Bodily Patterns of Affective Experience during Learning. 31-40
  
• Mingyu Feng, Neil T. Heffernan:
  Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? 41-50
  
• Jon M. Fincham, John R. Anderson, Shawn Betts, Jennifer L. Ferris:
  Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System. 51-60
  
• Yue Gong, Joseph E. Beck, Neil T. Heffernan:
  Using multiple Dirichlet distributions to improve parameter plausibility. 61-70
  
• Larry Howard, Julie Johnson, Carin Neitzel:
  Examining Learner Control in a Structured Inquiry Cycle Using Process Mining. 71-80
  
• Hogyeong Jeong, Gautam Biswas, Julie Johnson, Larry Howard:
  Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models. 81-90
  
• Anna Katrina Dominguez, Kalina Yacef, James R. Curran:
  Data Mining for Generating Hints in a Python Tutor. 91-100
  
• Blair Lehman, Whitney L. Cade, Andrew Olney:
  Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity. 101-110
  
• Sunghwan Mac Kim, Rafael A. Calvo:
  Sentiment Analysis in Student Experiences of Learning. 111-120
  
• Keith E. Maull, Manuel Gerardo Saldivar, Tamara Sumner:
  Online Curriculum Planning Behavior of Teachers. 121-130
  
• André Krüger, Agathe Merceron, Benjamin Wolf:
  A Data Model to Ease Analysis and Mining of Educational Data. 131-140
  
• Orlando Montalvo, Ryan Shaun Joazeiro de Baker, Michael A. Sao Pedro, Adam Nakama, Janice D. Gobert:
  Identifying Students' Inquiry Planning Using Machine Learning. 141-150
  
• Rebecca Nugent, Nema Dean, Elizabeth Ayers:
  Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers. 151-160
  
• Zachary A. Pardos, Neil T. Heffernan:
  Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm. 161-170
  
• Cristóbal Romero, José Raúl Romero, José María Luna, Sebastián Ventura:
  Mining Rare Association Rules from e-Learning Data. 171-180
  
• Michael Sao Pedro, Ryan Shaun Joazeiro de Baker, Orlando Montalvo, Adam Nakama, Janice D. Gobert:
  Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns. 181-190
  
• David H. Shanabrook, David G. Cooper, Beverly Park Woolf, Ivon Arroyo:
  Identifying High-Level Student Behavior Using Sequence-based Motif Discovery. 191-200
  
• Benjamin Shih, Kenneth R. Koedinger, Richard Scheines:
  Unsupervised Discovery of Student Strategies. 201-210
  
• Wenting Xiong, Diane J. Litman, Christian D. Schunn:
  Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data. 211-220
  
• Michael Yudelson, Peter Brusilovsky, Antonija Mitrovic, Moffat Mathews:
  Using Numeric Optimization To Refine Semantic User Model Integration Of Adaptive Educational Systems. 221-230
  
• John Champaign, Robin Cohen:
  An Annotations Approach to Peer Tutoring. 231-240
  
• Mohammad Hassan Falakmasir, Jafar Habibi:
  Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning. 241-248
  
• Rajibussalim:
  Mining Students' Interaction Data from a System that Support Learning by Reflection. 249-256
  
• Vilaythong Southavilay, Kalina Yacef, Rafael A. Calvo:
  Process Mining to Support Students' Collaborative Writing. 257-266
  
• Turadg Aleahmad, Vincent Aleven, Robert Kraut:
  Automatic Rating of User-Generated Math Solutions. 267-268
  
• Matt Bachmann, Janice Gobert, Joseph Beck:
  Tracking Students' Inquiry Paths through Student Transition Analysis. 269-270
  
• Lee Becker, Wayne H. Ward, Sarel van Vuuren:
  DISCUSS: Enabling Detailed Characterization of Tutorial Interactions Through Dialogue Annotation. 271-272
  
• James Bernauer, Jay Powell:
  Data Mining of both Right and Wrong Answers from a Mathematics and a Science M/C Test given Collectively to 11, 228 Students from India in years 4, 6 and 8. 273-274
  
• Suchismita Srinivas, Muntaquim Bagadia, Anupriya Gupta:
  Mining information from tutor data to improve pedagogical content knowledge. 275-276
  
• Haiyun Bian:
  Clustering Student Learning Activity Data. 277-278
  
• Nabila Bousbia, Jean-Marc Labat, Amar Balla, Issam Rebaï:
  Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments. 279-280
  
• Whitney L. Cade, Andrew Olney:
  Using Topic Models to Bridge Coding Schemes of Differing Granularity. 281-282
  
• John Champaign, Robin Cohen:
  A Distillation Approach to Refining Learning Objects. 283-284
  
• Kristy Elizabeth Boyer, Robert Phillips, Eunyoung Ha, Michael D. Wallis, Mladen A. Vouk, James C. Lester:
  A Preliminary Investigation of Hierarchical Hidden Markov Models for Tutorial Planning. 285-286
  
• Carol Forsyth, Heather Butler, Arthur C. Graesser, Diane F. Halpern:
  Higher Contributions Correlate with Higher Learning Gains. 287-288
  
• Adam B. Goldstein, Ryan Shaun Joazeiro de Baker, Neil T. Heffernan:
  Pinpointing Learning Moments; A finer grain P(J) model. 289-290
  
• José P. González-Brenes, Jack Mostow:
  Predicting Task Completion from Rich but Scarce Data. 291-292
  
• Sharon Hardof-Jaffe, Arnon Hershkovitz, Ronit Azran, Rafi Nachmias:
  Hierarchical Structures of Content Items in LMS. 293-294
  
• Arnon Hershkovitz, Rafi Nachmias:
  Is Students' Activity in LMS Persistent?. 295-296
  
• Matt Johnson, Tiffany Barnes:
  EDM Visualization Tool: Watching Students Learn. 297-298
  
• Nabila Khodeir, Nayer M. Wanas, Nevin Mahmoud Darwish, Nadia Hegazy:
  Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks. 299-300
  
• Leah Macfadyen, Peter Sorenson:
  Using LiMS (the Learner Interaction Monitoring System) to Track Online Learner Engagement and Evaluate Course Design. 301-302
  
• Keith E. Maull, Manuel Gerardo Saldivar, Tamara Sumner:
  Observing Online Curriculum Planning Behavior of Teachers. 303-304
  
• André Krüger, Agathe Merceron, Benjamin Wolf:
  When Data Exploration and Data Mining meet while Analysing Usage Data of a Course. 305-306
  
• Jack Mostow, Bao Hong (Lucas) Tan:
  AutoJoin: Generalizing an Example into an EDM query. 307-308
  
• Martin Mozina, Matej Guid, Aleksander Sadikov, Vida Groznik, Jana Krivec, Ivan Bratko:
  Conceptualizing Procedural Knowledge Targeted at Students with Different Skill Levels. 309-310
  
• Philip I. Pavlik Jr.:
  Data Reduction Methods Applied to Understanding Complex Learning Hypotheses. 311-312
  
• Dovan Rai, Joseph E. Beck:
  Analysis of a causal modeling approach: a case study with an educational intervention. 313-314
  
• Beijie Xu, Mimi Recker:
  Peer Production of Online Learning Resources: A Social Network Analysis. 315-316
  
• Cristóbal Romero, Sebastián Ventura, Ekaterina Vasilyeva, Mykola Pechenizkiy:
  Class Association Rules Mining from Students' Test Data. 317-318
  
• Andre A. Rupp, Shauna J. Sweet, Younyoung Choi:
  Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for Epistemic Games. 319-320
  
• Pokpong Songmuang, Maomi Ueno:
  Multiple Test Forms Construction based on Bees Algorithm. 321-322
  
• Hema Soundranayagam, Kalina Yacef:
  Can Order of Access to Learning Resources Predict Success?. 323-324
  
• Kenneth R. Koedinger, John C. Stamper:
  A Data Driven Approach to the Discovery of Better Cognitive Models. 325-326
  
• John C. Stamper, Tiffany Barnes, Marvin J. Croy:
  Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems. 327-328
  
• Jaan Ubi, Innar Liiv:
  A Review of Student Churn in the Light of Theories on Business Relationships. 329-330
  
• Ekaterina Vasilyeva, Mykola Pechenizkiy, Aleksandra Tesanovic, Evgeny Knutov, Sicco Verwer, Paul De Bra:
  Towards EDM Framework for Personalization of Information Services in RPM Systems. 331-332
  
• César Vialardi Sacín, Jorge Chue, Alfredo Barrientos, Daniel Victoria, Jhonny Estrella, Juan Pablo Peche, Alvaro Ortigosa:
  A Case Study: Data Mining Applied to Student Enrollment. 333-334
  
• Yutao Wang, Neil T. Heffernan, Joseph E. Beck:
  Representing Student Performance with Partial Credit. 335-336
  
• Mimi Recker, Beijie Xu, Sherry Hsi, Christine Garrard:
  Where in the World? Demographic Patterns in Access Data. 337-338
  
• Ankit Ranka, Faisal Anwar, Hui Soo Chae:
  Pundit: Intelligent Recommender of Courses. 339-340
  

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