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.
| ISBN: | 9781461443605 |
| Publication date: | 28th August 2012 |
| Author: | Nikos Manouselis |
| Publisher: | Springer an imprint of Springer New York |
| Format: | Paperback |
| Pagination: | 76 pages |
| Series: | SpringerBriefs in Electrical and Computer Engineering |
| Genres: |
Network hardware Education |
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.
Recommender Systems for Learning features in the following genres: Network hardware, Education
Paperback. Not Available.
Recommender Systems for Learning was written by Nikos Manouselis and published by Springer an imprint of Springer New York
Recommender Systems for Learning has 76 pages
Yes it is part of SpringerBriefs in Electrical and Computer Engineering series