Recommender Systems for Technology Enhanced Learning

. Ed(s): Manouselis, Nikos; Drachsler, Hendrik; Verbert, Katrien; Santos, Olga C.

ISBN 10: 1493946560 / ISBN 13: 9781493946563

Editore: Springer-Verlag New York Inc., 2016

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Editor(s): Manouselis, Nikos; Drachsler, Hendrik; Verbert, Katrien; Santos, Olga C. Num Pages: 320 pages, 67 black & white illustrations, 30 black & white tables, biography. BIC Classification: JN; UT; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 17. Weight in Grams: 492. . 2016. Paperback. . . . .

Codice articolo V9781493946563

Dati bibliografici

• Titolo: Recommender Systems for Technology Enhanced Learning
• Editore: Springer-Verlag New York Inc.
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 1493946560
• ISBN 13: 9781493946563
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.

Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.

Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.