Call for Papers
The Third International Conference on Educational Data Mining brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software, as well as state databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions.
The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This Conference emerges from preceding EDM workshops at the AAAI, AIED, ICALT, ITS, and UM conferences.
Topics of Interest
We welcome papers describing original work. Areas of interest include but are not limited to:
* Improving educational software. Many large educational data sets are generated by computer software. Can we use our discoveries to improve the [WINDOWS-1252?]software's effectiveness?
* Domain representation. How do learners represent the domain? Does this representation shift as a result of instruction? Do different subpopulations represent the domain differently?
* Evaluating teaching interventions. Student learning data provides a powerful mechanism for determining which teaching actions are successful. How can we best use such data?
* Emotion, affect, and choice. The student's level of interest and willingness to be a partner in the educational process is critical. Can we detect when students are bored and uninterested? What other affective states or student choices should we track?
* Integrating data mining and pedagogical theory. Data mining typically involves searching a large space of models. Can we use existing educational and psychological knowledge to better focus our search?
* Improving teacher support. What types of assessment information would help teachers? What types of instructional suggestions are both feasible to generate and would be welcomed by teachers?
* Replication studies. We are especially interested in papers that apply a previously used technique to a new domain, or that reanalyze an existing data set with a new technique.
* Best practices for adaptation of data mining techniques to EDM. We are especially interested in papers that present best practices or methods for the adaptation of techniques from data mining and other relevant literatures to the specific needs of analysis of educational data.
* Paper submission: March 10, 2010 (23:59:59 EST), no extension
* Acceptance notification: April 21, 2010
* Poster abstract submission: April 28, 2010 (23:59:59 EST)
* Poster notification: May 3, 2010
* Camera ready papers, posters: May 19, 2010
* Conference: June 11-13, 2010
All submissions should follow the formatting guidelines (MS Word, PDF). There are three types of submission:
* Full papers: Maximum of 10 pages. Should describe substantial, unpublished work
* Young researchers: Maximum of 8 pages. Designed for graduate students and undergraduates
* Poster abstracts: Maximum of 2 pages
* Conference Chair: Ryan S.J.d. Baker, Worcester Polytechnic Institute
* Program Chairs: Agathe Merceron, Beuth University of Applied Sciences Berlin
Philip I. Pavlik Jr., Carnegie Mellon University
* Local Organizing Chair: John Stamper, Carnegie Mellon University
* Web Chair: Arnon Hershkovitz, Tel Aviv University
Esma Aimeur, University of Montreal, Canada
Ivon Arroyo, University of Massachusetts Amherst, USA
Beth Ayers, Carnegie Mellon University, USA
Ryan Baker, Worcester Polytechnic Institute, USA
Tiffany Barnes, University of North Carolina at Charlotte, USA
Joseph Beck, Worcester Polytechnic Institute, USA
Bettina Berendt, Katholieke Universiteit Leuven , Belgium
Gautam Biswas, Vanderbilt University, USA
Cristophe Choquet, Université du Maine, France
Cristina Conati, University of British Columbia, Canada
Richard Cox, University of Sussex, UK
Michel Desmarais, Ecole Polytechnique de Montreal, Canada
Aude Dufresne, University of Montreal, Canada
Mingyu Feng, Worcester Polytechnic Institute, USA
Art Graesser, Universisty of Memphis, USA
Andreas Harrer, Katholische Universität Eichstätt-Ingolstadt, Germany
Neil Heffernan, Worcester Polytechnic Institute, USA
Arnon Hershkovitz, Tel Aviv University, Israel
Cecily Hiener, University of Utah, USA
Roland Hubscher, Bentley University, USA
Sebastian Iksal, Université du Maine, France
Kenneth Koedinger, Carnegie Mellon University, USA
Vanda Luengo, Université Joseph Fourier Grenoble, France
Tara Madhyastha, University of Washington, USA
Brent Martin, Canterbury University, New Zealand
Noboru Matsuda, Carnegie Mellon University, USA
Manolis Mavrikis, The University of Edinburgh, UK
Gordon McCalla, Univerisity of Saskatchewan, Canada
Bruce McLaren, Deutsches Forschungszentrum für Künstliche Intelligenz, Germany
Julia Mingullon Alfonso, Universitat Oberta de Catalunya, Spain
Tanja Mitrovic, Canterbury University, New Zealand
Jack Mostow, Carnegie Mellon University, USA
Rafi Nachmias, Tel Aviv University, Israel
Roger Nkambou, Université du Québec à Montréal (UQAM), Canada
Mykola Pechenizkiy, Eindhoven University of Technology, Netherlands
Steve Ritter, Carnegie Learning, USA
Cristobal Romero, Cordoba University, Spain
Carolyn Rose, Carnegie Mellon University, USA
Steven Tanimoto, University of Washington, USA
Sebastian Ventura, Cordoba University, Spain
Kalina Yacef, University of Sydney, Australia
Osmar Zaiane, University of Alberta, Canada