Recommender systems for technology enhanced learning
research trends and applications
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Publication
2014 - Springer, New York, New York (State)
Language
English
Word Count
76,500 words, Guess
Page Count
306 pages
Identifiers
- ISBN-101493905295
- ISBN-139781493905294
- Library of Congress Control Number2014935878
- OCLC Control Number867615360
- Better World Books9781493905294
and 1 more
- Open LibraryOL30388595M
Classifications
- DDC371.33
- LCCLB1028.3 .R418 2014
- LCCQ334-342L1-991QA75.5
and 2 more
- LCCQ334-342
- LCCTJ210.2-211.495
Description
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.
Subjects
Topics
Other Editions
- Recommender systems for technology enhanced learning: research trends and applications
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