Recommender Systems for Technology Enhanced Learning: Research Trends and Applications

Manouselis, Nikos (Editor)/ Drachsler, Hendrik (Editor)/ Verbert, Katrien (Editor)/ Santos, Olga C. (Editor)

ISBN 10: 1493946560 / ISBN 13: 9781493946563

Edité par Springer-Verlag New York Inc, 2016

A propos de cet article

reprint edition. 320 pages. 9.25x6.10x0.71 inches. In Stock.

N° de réf. du vendeur x-1493946560

Détails bibliographiques

• Titre: Recommender Systems for Technology Enhanced Learning: Research Trends and Applications
• Éditeur: Springer-Verlag New York Inc
• Date de parution: 2016
• Langue: anglais
• ISBN à 10 chiffres: 1493946560
• ISBN à 13 chiffres: 9781493946563
• Reliure: Paperback
• État de l'article: Brand New
• Poids: 0,45 kilogramme

A propos de ce titre

Synopsis

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.