Hopfgartner, F. orcid.org/0000-0003-0380-6088, Lommatzsch, A., Kille, B. et al. (4 more authors) (2016) The potentials of recommender systems challenges for student learning. In: Lee, D.D., (ed.) NIPS 2016 : 30th Conference on Neural Information Processing Systems. Workshop Challenges in Machine Learning: Gaming and Education. Challenges in Machine Learning: Gaming and Education, 09 Dec 2016, Barcelona, Spain.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Jun 2021 07:02 |
Last Modified: | 24 Jun 2021 07:02 |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175095 |