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Open-set learning context recognizing in mobile learning: Problem and methodology
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DOI：10.1016/j.icte.2024.04.006.png)
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摘要
En 中文
Mobile learning allows for an interactive way of learning through devices like smartphones. However, current methods usually rely on pre-set situations and struggle to recognize new contexts when they come up during testing. To solve this, we suggest the Open-set Learning Context Recognition Model (OLCRM). This model uses data extracted from smartphone sensors to identify whether a learning context is known or unknown. It also uses a Dual Discriminator Generative Adversarial Network (DDGAN) to create high-quality fake examples, which helps improve the accuracy of recognizing contexts. Experimental results demonstrate the effectiveness of OLCRM in open-set learning context recognition problems. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Learning contexts
Mobile learning
Generative adversarial network
Out of distribution
Open-set recognition
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