Full proceedings are available
Conference Schedule
All sessions held in SH-2420, Pavillon Sherbrooke, 200, rue Sherbrooke Ouest (on the UQAM campus)
Friday, June 20, 2008
| Time | Session |
| 9.00‑9.30 | Opening ceremony |
| 9.30‑10.30 | Keynote address: Prof. Brian Junker, Assessment Modeling in Educational Data Mining |
| 10.30‑10.45 | Coffee break |
| 10.45‑12.15 | Session: EDM for Assessment (30 mins each) |
| Argument graph classification with Genetic Programming and C4.5 Collin Lynch, Kevin Ashley, Niels Pinkwart and Vincent Aleven | |
Mining Data from an Automated Grading and Testing System by Adding Rich Reporting Capabilities | |
| Adaptive Test Design with a Naive Bayes Framework Michel Desmarais, Alejandro Villarreal and Michel Gagnon | |
| 12.15‑13.35 | Lunch |
| 13.35‑15.35 | Session: EDM for Improving Skill and Domain Models (30 mins each) |
| Labeling Student Behavior Faster and More Precisely with Text Replays Ryan Baker and Adriana de Carvalho | |
| Acquiring Background Knowledge for Intelligent Tutoring Systems Claudia Antunes | |
| Mining Student Behavior Models in Learning‑by‑Teaching Environments Hogyeong Jeong and Gautam Biswas | |
| Integrating Knowledge Gained From Data Mining With Pedagogical Knowledge Roland Hubscher and Sadhana Puntambekar | |
| 15.35‑15.50 | Coffee break |
| 15.50‑17.20 | Session: Best Paper Nominees (30 mins each) |
| Data‑driven modelling of students' interactions in an ILE Manolis Mavrikis | |
| A Response Time Model for Bottom‑Out Hints as Worked Examples Benjamin Shih, Kenneth Koedinger and Richard Scheines | |
| Interestingness Measures for Association Rules in Educational Data Agathe Merceron and Kalina Yacef | |
19.00-22.00 |
|
Saturday, June 21, 2008
| Time | Session |
| 9.00‑10.00 | Keynote address: Prof. David Pritchard, Assessing Learning |
| 10.00‑10.15 | Coffee break |
| 10.15‑11.00 | Young Researchers Track (5 mins each) |
| Skill Set Profile Clustering Based on Weighted Student Responses Elizabeth Ayers, Rebecca Nugent and Nema Dean | |
| Developing a Log-based Motivation Measuring Tool Arnon Hershkovitz and Rafi Nachmias | |
| Can we predict which groups of questions students will learn from? Mingyu Feng, Neil Heffernan, Joseph Beck and Ken Koedinger | |
| Reinforcement Learning-based Feature Selection For Developing Pedagogically Effective Tutorial Dialogue Tactics Min Chi, Pamela Jordan, Kurt VanLehn and Moses Hall | |
| Computational Infrastructures for School Improvement: A Way to Move Forward Ben Shapiro, Hisham Petry and Louis Gomez | |
| Do Students Who See More Concepts in an ITS Learn More? Moffat Mathews and Tanja Mitrovic | |
| Skill Mining Free-form Spoken Responses to Tutor Prompts Xiaonan Zhang, Jack Mostow, Nell Duke, Christina Trotochaud, Joseph Valeri and Albert Corbett | |
| A Preliminary Analysis of the Logged Questions that Students Ask in Introductory Computer Science Cecily Heiner | |
| Argument Mining Using Highly Structured Argument Repertoire Safia Abbas and Hajime Sawamura | |
| 11.00‑12.00 | Poster session |
| 12.00‑13.15 | Lunch |
| 13.15‑15.15 | Session: Improving Understanding of Student and Tutor Behaviors Through EDM (30 mins each) |
| Analytic Comparison of Three Methods to Evaluate Tutorial Behaviors Jack Mostow and Xiaonan Zhang | |
| Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor Philip Pavlik, Hao Cen, Lili Wu and Ken Koedinger | |
| The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS Zachary Pardos, Joseph Beck, Neil Heffernan and Carolina Ruiz | |
| Improving Contextual Models of Guessing and Slipping with a Truncated Training Set Ryan Baker, Albert Corbett and Vincent Aleven | |
| 15.15‑15.30 | Coffee break |
| 15.30‑17.00 | Session: Tools to Support EDM (30 mins each) |
| Data Mining Algorithms to Classify Students Cristobal Romero, Sebastián Ventura, Pedro G. Espejo and Cesar Hervas | |
| Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test? Mingyu Feng, Joseph Beck, Neil Heffernan, and Ken Koedinger | |
| An open repository and analysis tools for fine-grained, longitudinal learner data Ken Koedinger, Kyle Cunningham, Alida Skogsholm and Brett Leber | |
| 17.00‑17.30 | Closing Ceremony, Award |