Research on Balanced Allocation of English Educational Resources Based on Multi-objective Optimisation and NSGA-II

Dongyang Liu ()
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Dongyang Liu: School of Foreign Languages, Henan University of Animal Husbandry and Economy, Zhengzhou 450046, P. R. China

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 02, 1-22

Abstract: With the in-depth development of globalisation, English, as an international common language, is of great importance to the reasonable allocation of its educational resources for the improvement of national educational equity and overall educational quality. However, there has long been a serious imbalance in the allocation of English education resources in China. In terms of resource allocation between urban and rural areas and different schools, differences in the number of teachers, teaching facilities, teaching materials and other aspects lead to gaps in the opportunities and quality of English education for students. This imbalance in resource allocation not only affects educational equity, but also limits students’ English learning outcomes. Therefore, this paper proposes an optimal allocation method of English educational resources based on multi-objective optimisation (MOO) and non-dominated sorting genetic algorithm (NSGA-II). This method aims to maximise the utilisation rate of educational resources, narrow the gap between urban and rural areas and schools, and promote educational equity and efficient use of resources by optimising multiple objectives at the same time, such as teacher allocation, balanced use of teaching facilities and fair distribution of teaching materials resources. The experimental results show that the proposed method has a high degree of fit on two different datasets, reaching 94.58% and 96.87%, respectively, and the resource balance is greatly improved in the process of resource allocation. In addition, the algorithm has high operating efficiency under large-scale data, and the training time can be stabilised at 57.84 s when the sample size reaches 24,200. The experiment also shows that the application of this method in districts and counties significantly reduces the fluctuation of educational resources and optimises the allocation level of educational resources in each district and county. The research provides a new way to solve the problem of unfair distribution of educational resources, and shows a good application potential in the field of large-scale resource optimisation.

Keywords: Balanced distribution; education resources; English; multi-objective optimisation; non-dominated sorting genetic algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219649225500157

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