1. EDM 2008: Montreal, Québec, Canada

Full Papers

• Cristóbal Romero, Sebastián Ventura, Pedro G. Espejo, César Hervás:
  Data Mining Algorithms to Classify Students. 8-17
  
• Cláudia Antunes:
  Acquiring Background Knowledge for Intelligent Tutoring Systems. 18-27
  
• Jack Mostow, Xiaonan Zhang:
  Analytic Comparison of Three Methods to Evaluate Tutorial Behaviors. 28-37
  
• Ryan Shaun Joazeiro de Baker, Adriana M. J. B. de Carvalho:
  Labeling Student Behavior Faster and More Precisely with Text Replays. 38-47
  
• Michel C. Desmarais, Alejandro Villarreal, Michel Gagnon:
  Adaptive Test Design with a Naive Bayes Framework. 48-56
  
• Agathe Merceron, Kalina Yacef:
  Interestingness Measures for Associations Rules in Educational Data. 57-66
  
• Ryan Shaun Joazeiro de Baker, Albert T. Corbett, Vincent Aleven:
  Improving Contextual Models of Guessing and Slipping with a Trucated Training Set. 67-76
  
• Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koedinger:
  Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. 77-86
  
• Manolis Mavrikis:
  Data-driven modelling of students' interactions in an ILE. 87-96
  
• Roland Hübscher, Sadhana Puntambekar:
  Integrating Knowledge Gained From Data Mining With Pedagogical Knowledge. 97-106
  
• Mingyu Feng, Joseph E. Beck, Neil T. Heffernan, Kenneth R. Koedinger:
  Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standarized Test?. 107-116
  
• Benjamin Shih, Kenneth R. Koedinger, Richard Scheines:
  A Response Time Model For Bottom-Out Hints as Worked Examples. 117-126
  
• Hogyeong Jeong, Gautam Biswas:
  Mining Student Behavior Models in Learning-by-Teaching Environments. 127-136
  
• Collin Lynch, Kevin D. Ashley, Niels Pinkwart, Vincent Aleven:
  Argument graph classification with Genetic Programming and C4.5. 137-146
  
• Zachary A. Pardos, Neil T. Heffernan, Carolina Ruiz, Joseph E. Beck:
  The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS. 147-156
  
• Kenneth R. Koedinger, Kyle Cunningham, Alida Skogsholm, Brett Leber:
  An Open Repository and analysis tools for fine-grained, longitudinal learner data. 157-166
  
• Anthony Allevato, Matthew Thornton, Stephen H. Edwards, Manuel A. Pérez-Quiñones:
  Mining Data from an Automated Grading and Testing System by Adding Rich Reporting Capabilities. 167-176
  

Posters

• Sebastián Ventura, Cristóbal Romero, César Hervás:
  Analyzing Rule Evaluation Measures with Educational Datasets: A Framework to Help the Teacher. 177-181
  
• Cristóbal Romero, Sergio Gutiérrez Santos, Manuel Freire, Sebastián Ventura:
  Mining and Visualizing Visited Trails in Web-Based Educational Systems. 182-186
  
• Mykola Pechenizkiy, Toon Calders, Ekaterina Vasilyeva, Paul De Bra:
  Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study. 187-191
  
• Kwangsu Cho:
  Machine Classification of Peer Comments in Physics. 192-196
  
• Tiffany Barnes, John C. Stamper, Lorrie Lehman, Marvin J. Croy:
  A pilot study on logic proof tutoring using hints generated from historical student data. 197-201
  

Young Researchers' Track (YRT) Posters

• Safia Abbas, Hajime Sawamura:
  Towards Argument Mining from Relational DataBase. 202-209
  
• Elizabeth Ayers, Rebecca Nugent, Nema Dean:
  Skill Set Profile Clustering Based on Weighted Student Responses. 210-217
  
• Mingyu Feng, Neil T. Heffernan, Joseph E. Beck, Kenneth R. Koedinger:
  Can we predict which groups of questions students will learn from?. 218-225
  
• Arnon Hershkovitz, Rafi Nachmias:
  Developing a Log-based Motivation Measuring Tool. 226-233
  
• Xiaonang Zhang, Jack Mostow, Nell Duke, Christina Trotochaud, Joseph Valeri, Albert T. Corbett:
  Mining Free-form Spoken Responses to Tutor Prompts. 234-241
  
• R. Benjamin Shapiro, Hisham Petry, Louis M. Gomez:
  Computational Infrastructures for School Improvement: A Way to Move Forward. 242-249
  
• Cecily Heiner:
  A Preliminary Analysis of the Logged Questions that Students Ask in Introductory Computer Science. 250-257
  
• Min Chi, Pamela W. Jordan, Kurt VanLehn, Moses Hall:
  Reinforcement Learning-based Feature Seleciton For Developing Pedagogically Effective Tutorial Dialogue Tactics. 258-265
  
• Moffat Mathews, Tanja Mitrovic:
  Do Students Who See More Concepts in an ITS Learn More?. 266-273