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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.10658 |
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| _version_ | 1866916438272376832 |
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| author | Luo, Zijin Wang, Xu Wang, Yiquan Zhang, Haotian Li, Zhuangzhuang |
| author_facet | Luo, Zijin Wang, Xu Wang, Yiquan Zhang, Haotian Li, Zhuangzhuang |
| contents | In order to enhance students' initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students' course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10658 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Personalized MOOC Learning Group and Course Recommendation Method Based on Graph Neural Network and Social Network Analysis Luo, Zijin Wang, Xu Wang, Yiquan Zhang, Haotian Li, Zhuangzhuang Social and Information Networks Computers and Society In order to enhance students' initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students' course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes. |
| title | A Personalized MOOC Learning Group and Course Recommendation Method Based on Graph Neural Network and Social Network Analysis |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2410.10658 |