Towards AI-powered personalization in MOOC learning
Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques includ...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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2021
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| Online Access: | https://hdl.handle.net/10356/151947 |
| _version_ | 1811684700605906944 |
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| author | Yu, Han Miao, Chunyan Leung, Cyril White, Timothy John |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Yu, Han Miao, Chunyan Leung, Cyril White, Timothy John |
| author_sort | Yu, Han |
| collection | NTU |
| description | Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students’ learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed. |
| first_indexed | 2024-10-01T04:32:48Z |
| format | Journal Article |
| id | ntu-10356/151947 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:32:48Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1519472021-10-22T02:52:22Z Towards AI-powered personalization in MOOC learning Yu, Han Miao, Chunyan Leung, Cyril White, Timothy John School of Computer Science and Engineering School of Materials Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Engineering::Computer science and engineering Education Research Management Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students’ learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed. Nanyang Technological University National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IDM Futures Funding Initiative; the Lee Kuan Yew Post-Doctoral Fellowship Grant; and the NTU-PKU Joint Research Institute, a collaboration between Nanyang Technological University and Peking University that is sponsored by a donation from the Ng Teng Fong Charitable Foundation. 2021-10-22T02:52:22Z 2021-10-22T02:52:22Z 2017 Journal Article Yu, H., Miao, C., Leung, C. & White, T. J. (2017). Towards AI-powered personalization in MOOC learning. Npj Science of Learning, 2(1), 15-. https://dx.doi.org/10.1038/s41539-017-0016-3 2056-7936 https://hdl.handle.net/10356/151947 10.1038/s41539-017-0016-3 30631461 2-s2.0-85105745635 1 2 15 en npj Science of Learning © 2017 The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Education Research Management Yu, Han Miao, Chunyan Leung, Cyril White, Timothy John Towards AI-powered personalization in MOOC learning |
| title | Towards AI-powered personalization in MOOC learning |
| title_full | Towards AI-powered personalization in MOOC learning |
| title_fullStr | Towards AI-powered personalization in MOOC learning |
| title_full_unstemmed | Towards AI-powered personalization in MOOC learning |
| title_short | Towards AI-powered personalization in MOOC learning |
| title_sort | towards ai powered personalization in mooc learning |
| topic | Engineering::Computer science and engineering Education Research Management |
| url | https://hdl.handle.net/10356/151947 |
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