1. EDM 2008:
Montreal,
Québec,
Canada
Ryan Shaun Joazeiro de Baker, Tiffany Barnes, Joseph E. Beck (Eds.):
Educational Data Mining 2008, The 1st International Conference on Educational Data Mining, Montreal, Québec, Canada, June 20-21, 2008. Proceedings.
www.educationaldatamining.org 2008 
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
Last update Wed Feb 15 04:04:17 2012
CET by the DBLP Team — 
Data released under the ODC-BY 1.0 license — See also our legal information page
