Recommender Systems for Technology Enhanced Learning: Research Trends & Applications
an Edited Volume to be published by Springer in 2013
http://adenu.ia.uned.es/publications/recsystel/springer/
Important Dates:
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Manuscript submission: 1 November 2012
Notification of acceptance: 1 December 2012
Final manuscript due: 20 December 2012
Tentative publication date: ~Spring 2013
Introduction
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Technology Enhanced Learning (TEL) aims to design, develop and test
socio-technical innovations that will support and enhance
learning practices of both individuals and organisations.
It is an application domain that generally addresses all types of
technology research & development aiming to support of teaching and
learning activities, and considers meta-cognitive and reflective skills
such as self-management, self-motivation, and effective informal and
self-regulated learning. Information retrieval is a pivotal activity
in TEL, and the deployment of recommender systems has attracted
increased interest during the past years as it addresses the
information overload problem in TEL scenarios with a low cost approach.
Recommendation methods, techniques and systems open an interesting
new approach to facilitate and support learning and teaching.
There are plenty of resources available on the Web, both in terms of
digital learning content and people resources (e.g. other learners,
experts, tutors) that can be used to facilitate teaching and
learning tasks. The challenge is to develop, deploy and evaluate
systems that provide learners and teachers with meaningful guidance
in order to help identify suitable learning resources from a
potentially overwhelming variety of choices.
Aim & Scope
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Recommender Systems for Technology Enhanced Learning: Research Trends & Applications
aims to collect contributions that will range from research papers
to system presentations and that will address the following topics on the application
of recommender systems in the domain of education:
i) user needs, tasks and activities to be supported
ii) requirements for the design and deployment of recommender systems
iii) novel recommendation algorithms and systems
iv) evaluation criteria, methods and studies
Topics of Interest
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(list indicative and not exclusive)
* User tasks to be supported by recommender systems in TEL
* Explanation and visualizations of recommendations
* Open user models for TEL recommender systems
* Recommendation approaches based on social (meta) data
* Requirements for the deployment of TEL recommender systems
* Data sets for TEL recommender systems
* Recommendation algorithms and systems for TEL
* Transfer of successful algorithms and systems from other application areas
* Evaluation criteria and methods for TEL recommender systems
* Experiences from actual implementation in teaching, learning and/or community building
Submission Procedure
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All submitted manuscripts will be peer reviewed. Contributions should be original
and not simultaneously submitted to other venues. Manuscripts that have been included
in previous workshop/conference proceedings should include at least 30% of new material
in order to be considered for publication.
Detailed manuscript instructions are available from Springer Book Author Guidelines:
http://www.springer.com/authors/book+authors/helpdesk?SGWID=0-1723113-12-971304-0
Submissions will be handled through EasyChair:
https://www.easychair.org/conferences/?conf=recsystelspringer2013
The volume is scheduled to be published during 2013 by Springer.
Editors:
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Nikos Manouselis, Agro-Know Technologies & ARIADNE Foundation (Greece)
Katrien Verbert, Katholieke Universiteit Leuven (Belgium)
Hendrik Drachsler, Open Universiteit Nederlands (The Netherlands)
Olga C. Santos, aDeNu - Spanish National University for Distance Education (Spain)
Enquiries
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For any questions, please contact the editors at recsystelspringer2013@easychair.org
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