-------- Original-Nachricht -------- Betreff: [computational.science] CFP: Int. Workshop on Educational Data Mining (EDM@ICALT'07) Datum: Wed, 31 Jan 2007 20:22:20 +0200 (EET) Von: Mykola Pechenizkiy mpechen@cs.jyu.fi Antwort an: mpechen@cs.jyu.fi Organisation: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com
**************************************************************** International Workshop on Educational Data Mining (EDM@ICALT'07) (http://www.win.tue.nl/~mpechen/conf/edm2007/) as part of the 7th IEEE International Conference on Advanced Learning Technologies (IEEE ICALT 2007) (http://www.ask.iti.gr/icalt/2007/) Niigata, Japan, July 18-20, 2007 ****************************************************************
*** CALL FOR PAPERS ***
Recently, the increase in dissemination of interactive learning environments has allowed the collection of huge amounts of data. An 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: determining what are common types of learning behavior (e.g. in online systems), predicting the knowledge and interests of a user based on past behavior, partitioning 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 a 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 workshop at 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 workshop invites submissions addressing all aspects of educational data mining with applications for adaptation and personalization in e-learning systems.
The topics of special interest include, but are not restricted to:
* 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 * Data mining for predicting user (potentially changing) interests * Mining differences in user's learning behavior * Mining data from A/B tests * Application of discovered patterns for personalization and adaptation * Description of applications * 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.
* IMPORTANT DATES *
February 28, 2007 Submission of paper (IEEE 2-column, 10-pages maximum) March 16, 2007 Notification of acceptance April 6, 2007 Final camera-ready paper due April 16, 2007 Author registration deadline July 18-20, 2007 ICALT Conference
* SUBMISSION PROCEDURES *
Please submit your contribution (up to 10 pages) before the submission deadline (Feb 28, 2007) to the EDM workshop chairs by e-mail: edm.icalt07@gmail.com. Each submission will be reviewed by at least three members of the workshop programme committee members. All accepted workshop 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.
* TRACK CHAIRS *
Joseph E. Beck Carnegie Mellon University, USA Mykola Pechenizkiy Eindhoven University of Technology, the Netherlands Toon Calders Eindhoven University of Technology, the Netherlands Silvia Rita Viola U. Politecnica delle Marche and U. for Foreigners, Perugia, Italy
* TRACK PROGRAM COMMITTEE *
Ivon Arroyo University of Massachusetts Amherst, USA Ari Bader-Natal Carnegie Mellon University, USA Ryan Baker University of Nottingham, UK Mária Bieliková Slovak University of Technology, Slovakia Hao Cen Carnegie Mellon University, USA Raquel M. Crespo GarciaCarlos III University of Madrid, Spain Christophe Croquet Université du Maine, France Rebecca Crowley University of Pittsburgh, USA Paul De Bra Eindhoven University of Technology, the Netherlands Mingyu Feng Worcester Polytechnic Institute, USA Elena Gaudioso Universidad Nacional de Educación a Distanzia, Spain Sabine Graf Vienna University of Technology, Austria Wilhelmiina Hämälainen University of Joensuu, Finland Judy Kay University of Sydney, Australia Manolis Mavrikis University of Edinburgh, UK Agathe Merceron University of Applied Sciences Berlin, Germany Maria Milosavljevic Macquarie University, Sydney, Australia Kaska Porayska-Pomsta London Knowledge Lab , UK Genaro Rebolledo-Mendez University of Sussex, UK Cristobal Romero Universidad de Córdoba, Spain Amy Soller Institute for Defense Analyses, USA Alexey Tsymbal Siemens AG, Germany Marie-Helene Ng Cheong Vee Birkbeck University of London, UK
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