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]