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2nd Call for Datasets for TEL : dataTEL 2010

June 22, 2010 by Hendrik Drachsler   Comments (0)

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dataTEL Challenge on Sharing datasets for Technology-Enhanced Learning
Barcelona, Spain, 30 September 2010

Organised jointly by:
- dataTEL Theme Team of STELLAR Network of Excellence
- Joint Workshop on Recommender Systems for Technology-Enhanced Learning (RecSysTEL)
- 5th European Conference on Technology Enhanced Learning (EC-TEL 2010)


In the world of recommender systems, it is a common practice to use public available
datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie)
in order to evaluate recommendation algorithms. These datasets are used as benchmarks
to develop new recommendation algorithms and compare them to other algorithms in given settings.
In such datasets, a representation of implicit or explicit feedback from the users
regarding the candidate items is stored, in order to allow the recommender system
to produce a recommendation. This feedback can be in several forms.
For example, in the case of collaborative filtering systems it can be ratings or votes
(i.e. if an item has been viewed or bookmarked). In the case of content-based recommenders,
it can be product reviews or simple tags (keywords) that users provide for items.
Additional information is also required such a unique way to identify who provides
this feedback (user ID) and upon which item (item ID). The user-rating matrix used
in collaborative filtering systems is a well-known example.

Although recommender systems are increasingly applied in Technology Enhanced Learning (TEL),
it is still an application area that lacks such publicly available and interoperable datasets.
So although there is a lot of research conducted on recommender systems in TEL, they lack
datasets that would allow the experimental evaluation of the performance of different
recommendation algorithms using comparable, interoperable, and reusable datasets. This
leads to awkward experimentation and testing such as using datasets from movies in order
to evaluate educational recommendation algorithms.

To this end, the dataTEL Theme Team of the STELLAR Network of Excellence
( has launched the first
dataTEL Challenge: a call for TEL datasets that invites research groups to submit
existing datasets from TEL applications that can be used as input for TEL recommender systems.

A special dataTEL Cafe event will take place during the RecSysTEL Workshop 2010 in Barcelona,
to discuss about such issues and facilitate dataset sharing in this community. A best dataTEL
award will also be given to a TEL dataset that will be selected from a specially appointed
Scientific Committee.


* 1 July 2010: Submissions
* 16 July 2010: Notifications
* 1 August 2010: Camera-ready
* 30 September 2010: dataTEL Cafe event during RecSysTEL workshop and before the ECTEL conference dinner


The dataTEL Challenge submissions should include:
* TEL dataset file(s): preferably in CSV or XML format
* TEL dataset description: 2 pages formatted according to Springer LNCS

The dataset files(s) should be made available through some URL (e.g. FTP location).
The 2-page description should be submitted through the EasyChair submission system:

If you haven't an EasyChair account yet, you'll be asked to create it before you can access the dataTEL page.

For more information on content and formatting of the dataset and the 2-page description, see:


* Lora Aroyo, Free University of Amsterdam, The Netherlands
* Toine Bogers, Royal School of Library Information Science, Denmark
* Ernesto De Luca, Technical University of Berlin, Germany
* Jon Dron, Athabasca University, Canada
* Hannes Ebner, Royal Institute of Technology (KTH), Sweden
* Sandy El Helou, EPFL, Lausanne, Switzerland
* Alexander Ferlfernig, Technical University of Graz, Austria
* Kai Hoever, TU Darmstadt, Germany
* Joris Klerkx, KU Leuven, Belgium
* Stephanie Lindstaedt, Know Center, Austria
* Nikos Manouselis, Greek Research & Technology Network, Greece
* Jad Najjar, WU Vienna, Austria
* Nikos Palavitsinis, Greek Research & Technology Network, Greece
* Alan Said, Technical University of Berlin, Germany
* Elena Shulman, European Schoolnet, Belgium
* Stefan Ternier, Open University of the Netherlands, The Netherlands
* Michael Totschnig, WU Vienna, Austria
* Frans Van Assche, ARIADNE Foundation, Belgium
* Katrien Verbert, Katholieke Universiteit Leuven, Belgium
* Riina Vuorikari, European Schoolnet, Belgium
* Martin Wolpers, FIT Fraunhofer, Germany


* Hendrik Drachsler, Open Universiteit Nederlands, The Netherlands
* David Massart, European Schoolnet, Belgium

Feel free to spread the word using the 'dataTEL' tag!


The RecSysTEL Workshop aims to bring together researchers and
practitioners that are working on topics related to the design,
development and testing of recommender systems in educational
settings as well as present the current status of research in this
area. Overall, it aims to outline the rich potential
of TEL as an application area for recommender systems, as well as
expose participants to the challenges of developing such systems
in a TEL context.

The 5th European Conference on Technology Enhanced Learning (EC-TEL 2010)
brings together technological developments, learning models, and implementations
of new and innovative approaches to training and education. The conference
traditionally explores how the synergy of multiple disciplines
can provide new, more effective and more especially more sustainable,
technology-enhanced learning solutions to learning problems.


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