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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...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
Springer
2013
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-1-4614-4361-2 http://cds.cern.ch/record/1500221 |
_version_ | 1780926864189030400 |
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author | Manouselis, Nikos Drachsler, Hendrik Verbert, Katrien Duval, Erik |
author_facet | Manouselis, Nikos Drachsler, Hendrik Verbert, Katrien Duval, Erik |
author_sort | Manouselis, Nikos |
collection | CERN |
description | 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. |
id | cern-1500221 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2013 |
publisher | Springer |
record_format | invenio |
spelling | cern-15002212021-04-22T00:02:20Zdoi:10.1007/978-1-4614-4361-2http://cds.cern.ch/record/1500221engManouselis, NikosDrachsler, HendrikVerbert, KatrienDuval, ErikRecommender Systems for LearningEngineeringTechnology 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.Springeroai:cds.cern.ch:15002212013 |
spellingShingle | Engineering Manouselis, Nikos Drachsler, Hendrik Verbert, Katrien Duval, Erik Recommender Systems for Learning |
title | Recommender Systems for Learning |
title_full | Recommender Systems for Learning |
title_fullStr | Recommender Systems for Learning |
title_full_unstemmed | Recommender Systems for Learning |
title_short | Recommender Systems for Learning |
title_sort | recommender systems for learning |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-1-4614-4361-2 http://cds.cern.ch/record/1500221 |
work_keys_str_mv | AT manouselisnikos recommendersystemsforlearning AT drachslerhendrik recommendersystemsforlearning AT verbertkatrien recommendersystemsforlearning AT duvalerik recommendersystemsforlearning |