A Review of Data Mining in Personalized Education: Current Trends and
Future Prospects
- URL: http://arxiv.org/abs/2402.17236v1
- Date: Tue, 27 Feb 2024 06:09:48 GMT
- Title: A Review of Data Mining in Personalized Education: Current Trends and
Future Prospects
- Authors: Zhang Xiong, Haoxuan Li, Zhuang Liu, Zhuofan Chen, Hao Zhou, Wenge
Rong, Yuanxin Ouyang
- Abstract summary: This paper focuses on four scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis.
The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process.
- Score: 30.033926908231297
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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