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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...

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Detalles Bibliográficos
Autores principales: Manouselis, Nikos, Drachsler, Hendrik, Verbert, Katrien, Duval, Erik
Lenguaje:eng
Publicado: Springer 2013
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-1-4614-4361-2
http://cds.cern.ch/record/1500221
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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.
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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