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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/2402.17236 |
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| _version_ | 1866910348760580096 |
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| author | Xiong, Zhang Li, Haoxuan Liu, Zhuang Chen, Zhuofan Zhou, Hao Rong, Wenge Ouyang, Yuanxin |
| author_facet | Xiong, Zhang Li, Haoxuan Liu, Zhuang Chen, Zhuofan Zhou, Hao Rong, Wenge Ouyang, Yuanxin |
| contents | Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review of Data Mining in Personalized Education: Current Trends and Future Prospects Xiong, Zhang Li, Haoxuan Liu, Zhuang Chen, Zhuofan Zhou, Hao Rong, Wenge Ouyang, Yuanxin Computers and Society Machine Learning Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation. |
| title | A Review of Data Mining in Personalized Education: Current Trends and Future Prospects |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2402.17236 |