In keeping with my recent theme about data, I thought I'd post a copy of a call for papers for an interdisciplinary conference on topic of educational data mining, which will be held in UQÀM - Université du Québec à Montréal, Montreal, QC, Canada, immediately following the International Conference on Intelligent Tutoring Systems.University researchers fields such as school psychology, education, special education, and related disciplines might have some important insights to share at this conference. Practitioners and graduate interns might also have something important to contribute to this new are of study. The topics that I think are most relevant to RTI and broader K-12 school improvement efforts are highlighted below in green.
Call For Papers
The First 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, EC-TEL, ICALT, ITS, and UM conferences.
Topics Of InterestWe 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 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.
Important DatesPaper submission: March 31, 2008
Acceptance notification: April 30, 2008
Camera ready paper: May 16, 2008
Conference: June 20-21, 2008
Submission TypesAll submissions should follow the formatting guidelins (MS Word, PDF). There are two types of submission:
Full papers: Maximum of 10 pages. Should describe substantial, unpublished work
Young researcher: Maximum of 8 pages. Designed for graduate students and undergraduates
Conference OrganizationConference Chair: Tiffany Barnes, University of North Carolina Charlotte, USA
Program Chairs: Ryan S. J. de Baker, Carnegie Mellon University, USA; Joseph E. Beck, Worcester Polytechnic Institute, USA
Local Arrangements Chair: Michel Desmáris, Ecole Polytechnique de Montreal, Canada
Web Chair: Arnon Hershkovitz, Tel Aviv University, Israel
(Click here for a PDF version)