Recommender Systems for Learning [electronic resource] / by Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval.Material type: TextLanguage: English Series: SpringerBriefs in Electrical and Computer Engineering: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XI, 76 p. 4 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781461443612Subject(s): Computer science | Information systems | Computer Science | Information Systems and Communication Service | Education (general)Additional physical formats: Printed edition:: No titleDDC classification: 005.7 LOC classification: QA75.5-76.95Online resources: Click here to access online
Introduction and Background -- TEL as a recommendation context -- Survey and Analysis of TEL Recommender Systems -- Challenges and Outlook.
Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.