Recommender Systems for Technology Enhanced Learning : Research Trends and Applications /

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Bibliographic Details
Imprint:New York, NY : Springer New York : Imprint : Springer, 2014.
Description:1 online resource (XIV, 306 pages 67 illustrations) : online resource
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11094901
Hidden Bibliographic Details
Other authors / contributors:Manouselis, Nikos, editor.
Drachsler, Hendrik, editor.
Verbert, Katrien, editor.
Santos, Olga C., editor.
ISBN:9781493905300
1493905309
1306699886
9781306699884
1493905295
9781493905294
Digital file characteristics:text file PDF
Notes:English.
Summary: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.
Other form:Printed edition: 9781493905294
Standard no.:10.1007/978-1-4939-0530-0