[edm-announce] Fwd: CFP: Workshop on Educational Data Mining @ ICALT 07
- From: "Silvia Viola" <sr.viola@xxxxxxxxx>
- To: edm-discuss@xxxxxxxxxxxxx, edm-announce@xxxxxxxxxxxxx
- Date: Fri, 2 Feb 2007 13:40:02 +0100
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International Workshop on Educational Data Mining (EDM@ICALT'07
<mailto:EDM@ICALT'07> )
http://www.educationaldatamining.org/ICALT2007.html
(http://www.win.tue.nl/~mpechen/conf/edm2007/
<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/ <http://www.ask.iti.gr/icalt/2007/>
) Niigata, Japan, July 18-20, 2007
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APOLOGIES FOR MULTIPLE POSTINGS
*** 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 (e.g. between
two systems)
* 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@xxxxxxxxx
<mailto:edm.icalt07@xxxxxxxxx> . 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 Brandeis University, USA
Ryan Baker University of Nottingham, UK
Mária Bieliková Slovak University of Technology, Slovakia
Hao Cen Carnegie Mellon University, USA
Raquel M. Crespo Garcia Carlos 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
--
Silvia Rita Viola
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