Wu, Xiang and Wang, Huanhuan and Zhang, Yongting and Zou, Baowen and Hong, Huaqing (2024) A Tutorial-Generating Method for Autonomous Online Learning. IEEE Transactions on Learning Technologies, 17. pp. 1558-1567. ISSN 1939-1382, DOI https://doi.org/10.1109/TLT.2024.3390593.
Full text not available from this repository.Abstract
Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate personalized tutorials according to learners' preferences, nor can they adjust tutorial content as moods or levels of knowledge change. Therefore, this study develops an intelligent tutorial-generating system (Self-GT) for self-aid learning, integrating cognitive computing and generative learning to capture learners' dynamic preferences. The critical components of Self-GT are the tutorial-generating model based on cyclic deep reinforcement learning (RL) and the multimodal knowledge graph containing complex relationships. Specifically, the proposed RL model dynamically explores learners' preferences from the temporal dimension, enabling RL agents to express learning behavior characteristics accurately and generate personalized tutorials. Then, relying on the internal self-developed education base and external Internet sources, a multimodal knowledge graph with multiple self-defined relationships is designed to enhance the precision of tutorial generation. Finally, the experimental results indicate that the Self-GT performs well in generating tutorials and has been successfully applied in the generating tutorial for ``Hospital Network Architecture Planning and Design.''
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) |
| Uncontrolled Keywords: | Tutorials; Knowledge graphs; Hospitals; Visualization; Task analysis; Sociology; Scalability; Cognitive computing; course tutorial generating; deep reinforcement learning (RL); generative learning; multimodal knowledge graph |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Nov 2024 04:19 |
| Last Modified: | 14 Nov 2024 04:19 |
| URI: | http://eprints.um.edu.my/id/eprint/45903 |
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