Niigata, Japan
July 18-20, 2007

International Workshop on Educational Data Mining (EDM@ICALT'07)

as part of the 7th IEEE International Conference on Advanced Learning Technologies (IEEE ICALT 2007)

Paper submission deadline extended: March 14, 2007
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Recently, the increase in dissemination of interactive learning environments has allowed the collection of huge amounts of data. A popular and effective way of discovering new knowledge from large and complex data sets is data mining. As such, the EDM workshop invites papers that study how to apply data mining to analyze data generated by learning systems or experiments, as well as how discovered information can be used to improve adaptation and personalization. Interesting problems data mining can help to solve are: what are common types of learning behavior (e.g. in online systems), predict the knowledge and interests of a user based on past behavior, partition a heterogeneous group of users into homogeneous clusters, etc.

Typically, educational data sources are quite heterogeneous (e.g., web log files, interaction logs, source code, text and dialogue data, etc.), and have variety of different scales, grain-sizes, and spatial and temporal resolution. Though the many types of educational data often differ considerably from one another, they provide multiple types of insight on a single domain or context and, above all, share the potential to reveal unexpected and useful knowledge concerning learners and/or the process of learning - if correctly and coherently analyzed. Applying methods to mine the complex data that we can collect on educational situations requires the development of new approaches that build upon techniques from a combination of areas, including statistics, psychometrics, machine learning, and scientific computing.

The EDM'07 workshop as part of ICALT'07 aims at providing a focused international forum for researchers to present, discuss and explore the state of the art of mining educational data and evaluating usefulness of discovered patterns for adaptation and personalization, as well as to outline promising future research directions. The EDM'07 workshop invites submissions addressing all aspects of educational data mining with applications for adaptation and personalization in e-learning systems. A non-exhaustive list of topics of interest includes:
  • Methods and approached for EDM
  • Characteristics of educational data and how to deal with them
  • Learning browsing behavior; e.g., searching for patterns in log-data from (time-series) experiments
  • Data mining for user modeling
  • Data mining for predicting user (potentially changing) interests
  • Mining differences in user's learning behavior (e.g. between two systems)
  • Mining data from A/B tests
  • Application of discovered patterns for personalization and adaptation
  • Description of applications
  • Description of case studies and experiences

The workshop invites papers reporting experiences, case studies, surveys, reflections and comparisons. The submission format is: either a full paper of up to 10 pages, a short paper of up to 5 pages, or an abstract of up to 3 pages for a poster.


February 28, 2007 March 14, 2007Submission of paper (IEEE 2-column, 10-pages maximum)
March 30, 2007Notification of acceptance
April 6, 2007Final 2-pages summary for publication in main ICALT proceedings camera-ready due
April 16, 2007Author registration deadline
July 18-20, 2007ICALT days


All submissions will be handled electronically. Please submit your contribution (up to 10 pages) before the submission deadline (February 28 March 14, 2007) to the EDM'07 workshop chairs by e-mail: Each submission will be reviewed by at least three members of the workshop programme committee members.

All accepted workshop papers (up to 10 pages for full papers) will be published in the online workshop proceedings edited by the general workshop chairs. Beside this a short version of each accepted paper (2 pages long, IEEE 2-column format) will be published in the main IEEE proceedings. Therefore, authors of accepted papers will be asked to prepare an additional short-version camera-ready paper to be included in the main IEEE proceedings.


Joseph E. BeckCarnegie Mellon University, USA
Mykola Pechenizkiy Eindhoven University of Technology, the Netherlands
Toon Calders Eindhoven University of Technology, the Netherlands
Silvia R. Viola U. Politecnica delle Marche and U. for Foreigners, Italy


Ivon ArroyoUniversity of Massachusetts Amherst, USA
Ryan BakerUniversity of Nottingham, UK
Ari Bader-NatalBrandeis University, USA
Mária BielikováSlovak University of Technology, Slovakia
Hao CenCarnegie Mellon University, USA
Raquel M. Crespo GarciaCarlos III University of Madrid, Spain
Christophe CroquetUniversité du Maine, France
Rebecca CrowleyUniversity of Pittsburgh, USA
Paul De BraEindhoven University of Technology, the Netherlands
Mingyu FengWorcester Polytechnic Institute, USA
Elena GaudiosoUniversidad Nacional de Educación a Distanzia, Spain
Sabine GrafVienna University of Technology, Austria
Wilhelmiina HämälainenUniversity of Joensuu, Finland
Judy KayUniversity of Sydney, Australia
Manolis MavrikisUniversity of Edinburgh, UK
Agathe MerceronUniversity of Applied Sciences Berlin, Germany
Maria MilosavljevicMacquarie University, Sydney, Australia
Kaska Porayska-PomstaUniversity of Edinburgh, UK
Genaro Rebolledo-MendezUniversity of Sussex, UK
Cristobal RomeroUniversidad de Córdoba, Spain
Amy SollerUSA
Alexey TsymbalSiemens AG, Germany
Marie-Helene Ng Cheong VeeBirkbeck University of London, UK

For further questions, please contact