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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/5734
Title: Recommender Systems for Technology Enhanced Learning: Research Trends & Applications
Authors: Manouselis, Nikos
Verbert, Katrien
Drachsler, Hendrik
Santos, Olga
Keywords: Adaptive web systems
e-learning
experimental simulations
information retrieval
personalization
eal-world implementations
recommender systems
state-of-the-art
technology enhanced learning
web-based systems
Issue Date: 16-Dec-2014
Citation: Manouselis, N., Verbert, K., Drachsler, H., & Santos, O. C. (Eds.) (2014). Recommender Systems for Technology Enhanced Learning: Research Trends & Applications. Springer.
Abstract: 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.
URI: http://hdl.handle.net/1820/5734
ISBN: 978-1-4939-0530-0
Appears in Collections:1. TELI Publications, books and conference papers

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