<apologies for cross-postings>
Call for papers for the Educational Data Mining track, at the Workshop
on Data Mining for User Modeling, taking place on June 25, 2007 at the
eleventh international conference on User Modeling
(http://www.iit.demokritos.gr/um2007/index.php) in Corfu, Greece.
EDUCATIONAL DATA MINING
Educational data mining is the process of extracting useful
information from educational processes to better understand and assess
students and the contexts which they learn in, improve the performance
of educational systems, and inform teachers and researchers.
Data mining activities include both large scale data analyzed
traditionally, as well as more recent advances in machine learning.
Educational data mining (EDM) is of particular interest now due to the
scaling up of the number of students using interactive learning
environments such as intelligent computer tutors. When the field of
computer-based education was new, the main challenge of student
modeling was to build a basic model of the student's competencies.
Few students used such systems, and controlled studies were of brief
duration and had relatively few users. In recent years, studies
involving computer tutors have scaled up in scope both longitudinally
and in the number of users. This increase in scale has created a
problem: what to do with the data? For the first time we have the
ability to answer educational questions about how individual students
will react to instruction, or whether a particular student is not
learning material in an expected manner. The missing ingredient is
the computational toolkit to organize, visualize, and learn from the
The aims of this portion of the workshop are:
1. Provide a forum for researchers in computer science, psychometrics,
psychology, and education who are investigating educational data
2. Help form collaborations among researchers investigating similar
problems but who are perhaps using different approaches.
Topics of interest include, but are not limited to:
* What new types of analyses does data mining enable? Are we now able
to attack new problems?
* How can we integrate computational approaches, prior knowledge, and
existing learning theories?
* How should data mining estimate how users represent the domain? Do
subpopulations have different representations?
* How can data external to learning systems, such as test scores,
behavioral observations, and demographic data, be most usefully
integrated with usage data?
* What approaches and features are useful for assessing students?
* What tools exist that we should be using? What tools don't exist
that we should be developing?
We are especially interested in papers with a strong evaluation
component, particularly those that compare approaches in common use
among EDM practitioners. Determining limitations or weaknesses of
existing techniques is also on-point.
The session on Educational Data Mining is a half day and takes place
in the context of a broader workshop on Data Mining for User Modeling
(http://www.iit.demokritos.gr/um2007/workshops.php). This full-day
workshop covers a variety of topics in data mining as it relates to
user modeling issues in ubiquitous computing and education, and is
composed of three sessions.
* The morning session is on Educational Data Mining (see
* At mid-day there will be a shared session on data mining for UM for
education in ubiquitous contexts. This session will consist of an
invited talk by Gord McCalla, and presentations of papers at the
intersection of these two areas.
* The afternoon session will be on Ubiquitous Knowledge Discovery for
User Modeling (see http://vasarely.wiwi.hu-berlin.de/K-DUUM07)
The workshop aims to bring together researchers and practitioners from
a variety of backgrounds. We expect that participants will come from
a variety of research areas, including: user modelling, ubiquitous
computing, student modeling, personalization, Web mining, machine
learning, intelligent tutoring systems, and assessment.
We are considering both papers describing original, unpublished
research (10 pages max) as well as position papers and work at the
formative stage (5 pages max). Submissions should be in LNCS format
Submissions should be emailed to [log in to unmask] , and indicate
the target session (EDM, K-DUUM, or shared session).
All submissions will be reviewed by three (or more) reviewers.
All submissions should be emailed by 11:59pm Hawaii time, on February 7, 2007.
Ryan S.J.d. Baker – University of Nottingham
Joseph E. Beck – Carnegie Mellon University
Bettina Berendt – Humboldt University Berlin
Alexander Kröner – German Research Center for Artificial Intelligence
Ernestina Menasalvas – Universidad Politécnica de Madrid
Stephan Weibelzahl – National College of Ireland, Dublin
Ricardo Baeza-Yates, Director of Yahoo! Research Barcelona, Spain and
Yahoo! Research Latin America at Santiago, Chile
Jörg Baus, German Research Center for Artificial Intelligence,
Saarland Univ., Germany
Shlomo Berkovsky, University of Haifa, Israel
Christophe Choquet, University of Maine, France
Michel Desmarais, Ecole polytechnique Montreal
Marko Grobelnik, Jozef Stefan Institute, Ljubljana, Slovenia
Dominik Heckman, German Research Center for Artificial Intelligence, Germany
Pilar Herrero, Universidad Politécnica de Madrid, Spain
Anthony Jameson, German Research Center for Artificial Intelligence, Germany
Judy Kay, University of Sydney
Christian Kray, Informatics Research Institute. University of Newcastle, UK
Bruce McLaren, DFKI
Tanja Mitrovic, University of Canterbury
Dunja Mladenic, Jozef Stefan Institute, Ljubljana, Slovenia
Bamshad Mobasher, DePaul University Chicago, Chicago / IL, USA
Junichiro Mori, University of Tokio, Japan
Katharina Morik, University of Dortmund, Germany
Helen Pain, University of Edinburgh
Kaska Porayska-Pomsta, University of London
Thorsten Prante, Fraunhofer IPSI, Germany
Valerie Shute, ETS
Silvia Viola, Universita' Politecnica delle Marche, Italy
Titus Winters, DirecTV
Kalina Yacef, University of Sydney
Panayiotis Zaphiris,City University London, UK
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