International Workshop on Educational Data Mining (EDM @ ICALT'07)
as part of the 7th IEEE International Conference on Advanced Learning Technologies (IEEE ICALT 2007), Niigata, Japan, July 18-20, 2007
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.
CALL FOR PAPERS
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 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.
|March 14, 2007 (Expired)||Submission of paper (IEEE 2-column, 10-pages maximum)|
|March 30, 2007||Notification of acceptance|
|April 6, 2007||Final 2-pages summary for publication in main ICALT proceedings camera-ready due|
|April 16, 2007||Author registration deadline|
|April 30, 2007||Final camera-ready due|
|July 18-20, 2007||ICALT Conference|
All submissions will be handled electronically. Please submit your contribution (up to 10 pages) before the submission deadline (March 14, 2007 - Extended deadline) to the EDM workshop chairs by e-mail: firstname.lastname@example.org.
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.
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.
|NEW!! Workshop Contributions|
|A Outliers Analysis of Learner's data based on User Interface Behaviors by Yong Se Kim, Tae Bok Yoon, Hyun Jin Cha, Young Mo Jung, Eric Wang and Jee Hyong Lee|
A framework for using web usage mining to personalize e-learning
by Hafidh Ba-Omar, Ilias Petrounias, and Fahad Anwar
User session Models for Educational Systems based on Multiple Knowledge Structures
by Judit Jasso', and Alfredo Milani
Analyzing the data collected by Programming Tutors that Provide Post-Practice Reflection
by Amruth Kumar, and Peter Rutigliano
Joseph E. Beck
see also here
|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|
|Rahel Bekele||Addis Ababa University, Ethiopia|
|Mária Bieliková||Slovak University of Technology, Slovakia|
|Hao Cen||Carnegie Mellon University, USA|
|Raquel M. Crespo Garcia||Carlos III University of Madrid, Spain|
|Christophe Choquet||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|
|Alexey Tsymbal||Siemens AG, Germany|
|Marie-Helene Ng Cheong Vee||Birkbeck University of London, UK|