Friday, July 15, 2011

Call: Datasets and Data Supported Learning in Technology-Enhanced Learning



Special Issue on dataTEL
“Datasets and Data Supported Learning in Technology-Enhanced Learning”

International Journal of Technology Enhanced Learning (IJTEL)
ISSN (Online): 1753-5263 - ISSN (Print): 1753-5255

Deadline of submissions: 25 October 2011



The prospect of great growth of open and linked data in the knowledge society creates opportunities for new insights through advanced analysis methods based on e.g., information extraction, filtering, and retrieval technologies. Educational institutions also create and own large datasets on their students’ and course activities. The analytic use of such data, however, is very limited, when considering new educational services, recommending suitable peers or content or processes or goals, and improving the personalization of learning. Nevertheless, personalized learning is expected to have the potential to create more effective learning experiences, and accelerate learners’ time-to-competence. In the educational world, the literature is sparse on how to build upon today’s very limited public datasets and how to accommodate the lack of agreed quality standards on the personalization of learning.

The special issue on dataTEL in IJTEL aims to address this issue by collecting high value research papers to develop a body of knowledge about data-based personalization of learning. So far, there is no consensus on algorithms that can be successfully applied to make reliable analyses of data in a specific learning setting. Having an initial collection of datasets, coupled with case studies of their use in TEL, could be a first major step towards a theory of personalisation within TEL that can be based on empirical experiments with verifiable and valid results.

However, data driven research confronts researchers with a new set of challenges, for instance, a lack of common dataset formats or policies to share educational datasets, a huge variety of different evaluation methods for comparing diverse personalization techniques, and new ethical and privacy issues that arise from the ability to link and mine information.

Therefore, the objective of this special issue is to explore suitable datasets for TEL – with a specific focus on recommender and information filtering systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required pre-processing procedures and rules to create sharable data sets, common evaluation criteria for recommender systems in TEL and how a data set driven future in TEL could look like.


Relevant topics include, but are not limited to:

- descriptions of datasets that can be used for experimentation

- descriptions of data experiments (methods or results of experiments)

- experiences with those datasets

- dealing with legal protection rights towards datasets on a European level

- privacy preservation for educational datasets

- methods of effective anonymisation of educational datasets

- management and pre-processing procedures for educational datasets

- future scenarios for educational datasets

- impact of educational datasets for learners, teachers, and parents

- mash-ups based on educational datasets

- recommender approaches that are based on educational data

- evaluation methodologies and metrics for educational recommender systems


Hendrik Drachsler, Open University, The Netherlands

Katrien Verbert, K.U. Leuven, Belgium

Miguel-Angel Sicilia, University of Alcalá, Spain

Nikos Manouselis, Agro-Know Technologies, Greece

Stefanie Lindstaedt, KnowCenter, Austria

Martin Wolpers, Fraunhofer Institute for Applied Information Technology, Germany

Riina Vuorikari, European Schoolnet, Belgium


Authors are invited to submit original unpublished research as papers. All submitted papers will be peer-reviewed by at least two members of the program committee for originality, significance, clarity, and quality. In addition, the authors are asked to contribute short abstracts of their submissions to the dataTEL group space at TELeurope.

Submission will be available through the EasyChair submission system:

Details of the journal, manuscript preparation are available on the here:

Any questions and submissions should be sent to:

REVIEW COMMITTEE (to be confirmed)

Erik Duval, K.U. Leuven, Belgium

Seda Gurses, K.U. Leuven, Belgium

Abelardo Pardo, University Carlos III of Madrid, Spain

Julià Minguillón, Open University of Catalonia, Spain

Olga Santos, aDeNu, Spanish National University for Distance Education, Spain

Julien Broisin, Université Paul Sabatier, France

Christoph Rensing, TU Darmstadt, Germany

Shlomo Berkovsky, CSIRO, Australia

John Stamper, Datashop, Pittsburgh Science of Learning Center, USA

Eelco Herder, Forschungszentrum L3S, Germany

Martin Memmel, DFKI, Germany

Xavier Ochoa, Escuela Superior Politécnica del Litoral, Ecuador

Fridolin Wild, KMI, Open University, UK

Wolfgang Reinhardt, University of Paderborn, Germany

Wolfgang Greller, Open Universiteit, The Netherlands

Marco Kalz, Open Universiteit, The Netherlands

Adriana Berlanga, Open Universiteit, The Netherlands

Peter Sloep, Open Universiteit, The Netherlands

Ralf Klamma, RWTH Aachen, Germany

Pythagoras Karampiperis, NCSR Demokritos, Greece

Giannis Stoitsis, IEEE, Greece


Submission of manuscripts: 25 October 2011

Completion of first review: 30 November 2011

Submission of revised manuscripts: 15 January 2011

Final decision notification: 10 February 2012

Publication date (tentative): February 2012


The manuscripts should be original, unpublished, and not in consideration for publication elsewhere at the time of submission to the International Journal on Technology-Enhanced Learning and during the review process.

Please carefully follow the author guidelines at while preparing your manuscript. To get familiarity with the style of the journal, please see a previous issue at

All manuscripts will be subject to the usual high standards of peer review. Each paper will undergo double blind review.