Recommender Systems for Learning

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learn...

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Bibliographic Details
Main Authors: Manouselis, Nikos. (Author), Drachsler, Hendrik. (Author), Verbert, Katrien. (Author), Duval, Erik. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Series:SpringerBriefs in Electrical and Computer Engineering,
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-4361-2
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505 0 # |a Introduction and Background -- TEL as a recommendation context -- Survey and Analysis of TEL Recommender Systems -- Challenges and Outlook. 
520 # # |a Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains. 
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