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Organized by the International Working Group on Educational Data Mining.

Sponsors


Université du Québec à Montréal (UQAM), Canada


Machine Learning Department at the School of Computer Science, Carnegie Melon University

Proceedings

This page holds the proceedings for the 1st International Conference on Educational Data Mining (EDM08). The conference was held on June 20-21, 2008, in Montreal, Canada. Citation information:

‎Baker, R.S.J.d., Barnes, T., Beck, J.E. (Eds.) Educational Data Mining 2008: 1‎st International Conference on Educational Data Mining, ‎Proceedings. Montreal, Quebec, Canada. June 20-21, 2008.

ISBN - 10: 0615306292
ISBN - 13: 9780615306292 

All the files linked in this page are in PDF format.

Full Proceedings

Download entire conference proceedings from here (~6.5MB).

Best Paper

Shih, B., Koedinger, K., and Scheines, R. A Response Time Model for Bottom-Out Hints as Worked Examples. Pages 117-126. [pdf]

Other Best Paper Nominees

Merceron, A., and Yacef, K. Interestingness Measures for Association Rules in Educational Data. Pages 57-66. [pdf]

Mavrikis, M. Data-driven modelling of students' interactions in an ILE. Pages 87-96. [pdf]

Best Poster

Chi, M., Jordan, P., VanLehn, K., and Hall, M. Reinforcement Learningbased Feature Selection For Developing Pedagogically Effective Tutorial Dialogue Tactics. Pages 258-265. [pdf]

All Conference Papers

Abbas, S and Sawamura, H. Towards Argument Mining from Relational DataBase. Pages 202-209. [pdf]

Allevato, A., Thornton, M., Edwards S., and Perez-Quinones, M. Mining Data from an Automated Grading and Testing System by Adding Rich Reporting Capabilities. Pages 167-176. [pdf]

Antunes, C. Acquiring Background Knowledge for Intelligent Tutoring Systems. Pages 18-27. [pdf]

Ayers, E., Nugent, R., and Dean, N. Skill Set Profile Clustering Based on Weighted Student Responses. Pages 210-217. [pdf]

Baker, R. and Carvalho, A. Labeling Student Behavior Faster and More Precisely with Text Replays. Pages 38-47. [pdf]

Baker, R., Corbett, A., and Aleven, V. Improving Contextual Models of Guessing and Slipping with a Truncated Training Set. Pages 67-76. [pdf]

Barnes, T., Stamper, J., Lehman, L., and Croy, M. A pilot study on logic proof tutoring using hints generated from historical student data. Pages 197-201. [pdf]

Chi, M., Jordan, P., VanLehn, K., and Hall, M. Reinforcement Learningbased Feature Selection For Developing Pedagogically Effective Tutorial Dialogue Tactics. Pages 258-265. [pdf]

Cho, K. Machine Classification of Peer Comments in Physics. Pages 192-196. [pdf]

Desmarais, M., Villarreal, A., and Gagnon, M. Adaptive Test Design with a Naive Bayes Framework. Pages 48-56. [pdf]

Feng, M., Beck, J., Heffernan N., and Koedinger, K. Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test? Pages 107-116. [pdf]

Feng, M., Heffernan, N., Beck, J., and Koedinger, K. Can we predict which groups of questions students will learn from? Pages 218-225. [pdf]

Heiner, C. A Preliminary Analysis of the Logged Questions that Students Ask in Introductory Computer Science. Pages 250-257. [pdf]

Hershkovitz, A. and Nachmias, R. Developing a Log-based Motivation Measuring Tool. Pages 226-233. [pdf]

Hubscher R. and Puntambekar S. Integrating Knowledge Gained From Data Mining With Pedagogical Knowledge. Pages 97-106. [pdf]

Jeong, H. and Biswas, G. Mining Student Behavior Models in Learning-by-Teaching Environments. Pages 127-136. [pdf]

Koedinger, K., Cunningham, K., Skogsholm A., and Leber, B. An open repository and analysis tools for fine-grained, longitudinal learner data. Pages 157-166. [pdf]

Lynch, C., Ashley, K., Pinkwart N., and Aleven, V. Argument graph classification with Genetic Programming and C4.5. Pages 137-146. [pdf]

Mathews, M. and Mitrovic, T. Do Students Who See More Concepts in an ITS Learn More? Pages 266-273. [pdf]

Mavrikis, M. Data-driven modelling of students' interactions in an ILE. Pages 87-96. [pdf]

Merceron, A and Yacef, K. Interestingness Measures for Association Rules in Educational Data. Pages 57-66. [pdf]

Mostow, J. and Zhang, X. Analytic Comparison of Three Methods to Evaluate Tutorial Behaviors. Pages 28-37. [pdf]

Pardos, Z., Heffernan, N., Ruiz, C., and Beck, J. The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS. Pages 147-156. [pdf]

Pavlik, P., Cen, H., Wu, L., and Koedinger, L. Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. Pages 77-86. [pdf]

Pechenizkiy, M., Calders, T., Vasilyeva, E., and De Bra, P. Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study. Pages 187-191. [pdf]

Romero, C., Gutiérrez, S., Freire, M., and Ventura, S. Mining and Visualizing Visited Trails in Web-Based Educational Systems. Pages 182-186. [pdf]

Romero, C., Ventura, S., Espejo, P.G., and Hervas, C. Data Mining Algorithms to Classify Students. Pages 8-17. [pdf]

Shapiro, R.B., Petry, H., and Gomez, L.M. Computational Infrastructures for School Improvement: A Way to Move Forward. Pages 242-249. [pdf]

Shih, B., Koedinger, K., and Scheines, R. A Response Time Model for Bottom-Out Hints as Worked Examples. Pages 117-126. [pdf]

Ventura, S., Romero, C., and Hervas, C. Analyzing Rule Evaluation Measures with Educational Datasets: A Framework to Help the Teacher. Pages 177-181. [pdf]

Zhang, X., Mostow, J., Duke, N., Trotochaud, C., Valeri, J., and Corbett, A. Mining Free-form Spoken Responses to Tutor Prompts. Pages 234-241. [pdf]