Recommender Systems for Learning

• Κύριοι συγγραφείς: Manouselis, Nikos (Συγγραφέας), Drachsler, Hendrik (Συγγραφέας), Verbert, Katrien (Συγγραφέας), Duval, Erik (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2013.
• Σειρά: SpringerBriefs in Electrical and Computer Engineering,
• Θέματα: Computer science.; Computers.; Education.; Computer Science.; Information Systems and Communication Service.; Education, general.
• Φυσική περιγραφή: XI, 76 p. 4 illus. online resource.
• ISBN: 9781461443612
• ISSN: 2191-8112

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.