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ORCID1st EDM 2008: Montreal, Québec, Canada
- Ryan Shaun Joazeiro de Baker, Tiffany Barnes, Joseph E. Beck:
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 F. 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 Lehmann, 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
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