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<p>To effectively manage college students, it is necessary to address issues related to academic achievements, personal developments, and resource allocations. This is typically done in situations where there is uncertainty and insufficient information. Even though traditional statistical and machine learning approaches have improved decision-making, they struggle to adapt to the inherent ambiguity in real-world educational settings. This research suggests the Fuzzy Decision-Driven Optimization for Student Management (FDO-SM) framework as a means of overcoming the limitations previously mentioned. With the help of fuzzy logic, the framework can handle imprecise data and provide more flexible and accurate decision support. Determining fuzzy sets, constructing membership functions, and developing rule-based systems are all components of this technique. These components are used to evaluate student performances, distribute resources, and forecast appropriate behaviors. The results of the experiments demonstrate that FDO-SM outperforms traditional approaches, particularly in situations where uncertainty or data scarcity prevails. The purpose of this research is to provide a realistic and adaptable method for enhancing decision-making in educational administrations. The consequences of this research include greater student support and increased institutional efficiency.</p>

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Optimization of College Student Management Based on Fuzzy Decision Algorithm

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Yin Ma

摘要

To effectively manage college students, it is necessary to address issues related to academic achievements, personal developments, and resource allocations. This is typically done in situations where there is uncertainty and insufficient information. Even though traditional statistical and machine learning approaches have improved decision-making, they struggle to adapt to the inherent ambiguity in real-world educational settings. This research suggests the Fuzzy Decision-Driven Optimization for Student Management (FDO-SM) framework as a means of overcoming the limitations previously mentioned. With the help of fuzzy logic, the framework can handle imprecise data and provide more flexible and accurate decision support. Determining fuzzy sets, constructing membership functions, and developing rule-based systems are all components of this technique. These components are used to evaluate student performances, distribute resources, and forecast appropriate behaviors. The results of the experiments demonstrate that FDO-SM outperforms traditional approaches, particularly in situations where uncertainty or data scarcity prevails. The purpose of this research is to provide a realistic and adaptable method for enhancing decision-making in educational administrations. The consequences of this research include greater student support and increased institutional efficiency.