Overview
Presents cutting edge research from leading experts in the growing field of Recommender Systems for Technology Enhanced Learning (RecSys TEL)
International contributions are included to demonstrate the merging of various efforts and communities
Topics include: Linked Data and the Social Web as Facilitators for TEL Recommender Systems in Research and Practice, Personalised Learning-Plan Recommendations in Game-Based Learning and Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem
Includes supplementary material: sn.pub/extras
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Table of contents (14 chapters)
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Front Matter
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User and Item Data
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Front Matter
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Innovative Methods and Techniques
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Front Matter
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Platforms and Tools
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Front Matter
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Keywords
About this book
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.
Reviews
From the book reviews:
“Book represents a collection of state-of-the-art contributions devoted to RSs for TEL and explores contemporary research achievements in the area. … This very interesting, well-timed volume will provide great opportunities for PhD students and newcomers to this field to continue with high-quality research efforts. The book is also interesting for master’s students who would like to acquire adequate knowledge and emergent research achievements in this field. Secondary school teachers and experienced researchers could also find this book useful and interesting.” (M. Ivanović, Computing Reviews, October, 2014)Editors and Affiliations
Bibliographic Information
Book Title: Recommender Systems for Technology Enhanced Learning
Book Subtitle: Research Trends and Applications
Editors: Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos
DOI: https://doi.org/10.1007/978-1-4939-0530-0
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media New York 2014
Hardcover ISBN: 978-1-4939-0529-4Published: 12 April 2014
Softcover ISBN: 978-1-4939-4656-3Published: 03 September 2016
eBook ISBN: 978-1-4939-0530-0Published: 12 April 2014
Edition Number: 1
Number of Pages: XIV, 306
Number of Illustrations: 67 b/w illustrations
Topics: Artificial Intelligence, Education, general, Information Systems and Communication Service