Year: 2025
Type: article
Abstract: As intelligent education grows quickly, the amount of data about managing education is also expanding quickly. Finding useful information quickly while keeping data private is the key to making education management smarter. This work presents an intelligent education management data mining technique grounded in federated learning (FL) and formulates a multi-level system architecture. The system can safely share and intelligently analyse dispersed education data thanks to improvements to the FL algorithm and the addition of the DP protection mechanism. Experimental validation using the PISA dataset indicates that the suggested approach markedly enhances accuracy, F1 Score, and AUC index of the model, while safeguarding data privacy and security, hence exhibiting better performance and robust generalisation capability. The study's findings furnish a theoretical foundation and technological assistance for advancing the evolution of informatisation and intelligence within the realm of educational management.
Author: Xiaoyin Liang
Institution: Guangxi Vocational Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4414437349
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