CALL FOR JOURNAL PAPERS: 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
SCOPE
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.
TOPICS
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
SPECIAL ISSUE CO-EDITORS
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
SUBMISSIONS
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.
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
IMPORTANT DATES
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
SUBMISSION GUIDELINES
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.
All manuscripts will be subject to the usual high standards of peer
review. Each paper will undergo double blind review.
Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm for more
information.
log in
