The Potentials of Recommender Systems Challenges for Student Learning

Hopfgartner, F. , Lommatzsch, A., Kille, B., Larson, M., Brodt, T., Cremonesi, P. and Karatzoglou, A. (2016) The Potentials of Recommender Systems Challenges for Student Learning. Proceedings of CiML'16: Challenges in Machine Learning: Gaming and Education, Barcelona, Spain, 9 Dec 2016.

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Abstract

Increasingly, educators make use of learning-by-doing approaches to teach students of STEM programmes the skills that they need to become successful in careers in research and development. However, we argue that the technical challenges addressed in these programmes are often too limited and therefore do not support the students in gaining the more advanced skill sets required to thrive in our technology-oriented economy. We therefore suggest to incorporate realistic and complex challenges that model real-world problems faced in industrial settings. Focusing on the domain of recommender systems, we see potentials in embedding recommender systems challenges to enhance student learning to teach students the skills required by modern data scientists.

Item Type:Conference or Workshop Item
Additional Information:The EC-project CrowdRec (No. 610594) funded some of the authors in part.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hopfgartner, Dr Frank
Authors: Hopfgartner, F., Lommatzsch, A., Kille, B., Larson, M., Brodt, T., Cremonesi, P., and Karatzoglou, A.
Subjects:Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
College/School:College of Arts > School of Humanities > Humanities Advanced Technology and Information Institute (HATII)
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Proceedings of CiML'16: Challenges in Machine Learning: Gaming and Education 2016
Publisher Policy:Reproduced with the permission of the Publisher
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