Abstract
Relying on network technology, the integration of personalized learning and Internet technology has become another trending industry. This paper explores a new strategy for education management, that is, a personalized information push system based on recommendation algorithms. The system can push personalized learning resources for teachers and students and help them quickly locate interest points and learning directions by analyzing their usage history and tag attribute characteristics. The personalized information push algorithm achieves data fidelity by pre-cleaning or pre-processing the data. In addition, after the clustering algorithm is integrated into the system, its computing efficiency and mining depth are greatly improved than before. At the same time, based on collaborative filtering technology, this paper introduces information entropy and standard deviation to optimize the core algorithm, so as to distinguish the similarity between users, and further push recommendation accuracy and precision to a higher level. Finally, the existing problems in the current development of big data education management are analyzed, and future development strategies are proposed. To sum up, the personalized information recommendation system proposed in this study has a lower MAE value, so this has forward-looking significance for enhancing the depth of interactive learning and changing the inherent learning mode.
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Funding
The research is supported by: (1) University-Industry Collaborative Education Program: Research on Management and Quality Assurance Mechanism of Undergraduate Thesis in College Based on PDCA Model (202102654038); (2) University-Industry Collaborative Education Program: Construction and practice of full chain innovation and entrepreneurship training platform in Colleges (201801200027); (3) The ICP of Zhejiang Normal University: Personalized information transmission system.
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Zhu, Z., Sun, Y. Personalized information push system for education management based on big data mode and collaborative filtering algorithm. Soft Comput 27, 10057–10067 (2023). https://doi.org/10.1007/s00500-023-08213-w
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DOI: https://doi.org/10.1007/s00500-023-08213-w