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Nikos Manouselis <[log in to unmask]>
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Mon, 29 Oct 2012 19:12:40 +0200
text/plain (117 lines)
*apologies for cross-posting*

Recommender Systems for Technology Enhanced Learning: Research Trends & 
an Edited Volume to be published by Springer in 2013

Important Dates:
Manuscript submission: 20 November 2012   **** EXTENDED ****
Notification of acceptance: 15 December 2012
Final manuscript due: 5 January 2013
Tentative publication date: ~Spring 2013


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
Recommender Systems for Technology Enhanced Learning: Research Trends & 
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
(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

All submitted manuscripts will be peer reviewed. Contributions should be 
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 

Submissions will be handled through EasyChair:

The volume is scheduled to be published during 2013 by Springer.

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)

For any questions, please contact the editors at 
[log in to unmask]


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