Research on Efficiency Improvement and Empirical Study of Educational Resource Allocation Based on Improved Machine Learning Algorithm
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Abstract
Balanced and precise educational resource allocation is a core issue in advancing educational equity and improving overall quality. To address this problem, this paper proposes an optimized educational resource allocation model based on an improved random forest algorithm. First, theories related to educational resource allocation and machine learning are reviewed, and a core indicator system affecting resource allocation efficiency is clarified. Second, to address bias in feature weight allocation in traditional random forest algorithms, an adaptive weighting mechanism and cross-validation optimization strategy are introduced to construct an improved random forest algorithm. Third, basic educational resource data are used as samples, and the improved algorithm is applied to predict and optimize resource allocation efficiency. Comparative experiments are conducted to verify the model’s superiority. Finally, targeted suggestions for improving allocation efficiency are proposed based on empirical results. The results show that the R2 value of the improved random forest algorithm is 5.3 percentage points higher than that of the traditional algorithm, increasing from 0.873 to 0.926, with a 12.3% reduction in RMSE. The method effectively identifies key bottlenecks and supports precise resource optimization.
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References
G. Zhou and X. Zhou, “Education Policy and Reform in China,” Singapore: Springer Nature Singapore, 2019, ch. 3, pp. 31-44, doi: 10.1007/978-981-13-6492-1_3.
J. Xiao and Z. Liu, “Inequalities in the financing of compulsory education in China: A comparative study of Gansu and Jiangsu Provinces with spatial analysis,” International Journal of Educational Development, vol. 39, pp. 250-263, 2014, doi: 10.1016/j.ijedudev.2014.05.004.
F. Çelik and M. H. Baturay, “Technology and innovation in shaping the future of education,” Smart Learning Environments, vol. 11, no. 1, pp. 54, 2024, doi: 10.1186/s40561-024-00339-0.
Y. Wang, H. Lin, L. Sun, D. Zhang, J. Wang, and L. She, “Research on the Balanced Development of High Quality Compulsory Education,” Proceedings of the 2020 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE); 17-19 December 2020; Frankfurt, Germany. Piscataway, NJ, USA: IEEE; 2020. pp. 262-265, doi: 10.1109/ISAIEE51769.2020.00065.
Z. Ye, R. Khanal, and Z. Cao, “Studying on the efficiency of higher education resource allocation and its influencing factors in the western China using DEA-Malmquist and Tobit models,” PLoS One, vol. 20, no. 10, Art. no. e0334090, 2025, doi: 10.1371/journal.pone.0334090.
Q. Zhang, “Study on "hollowing out" rural governance from the perspective of precise poverty alleviation,” Population, Resources & Environmental Economics, vol. 4, pp. 140-151, 2023, doi: 10.23977/pree.2023.040117.
F. Li and I. Hardy, “Acculturating for Change: An Institutional Logics Perspective on Postgraduate Policy Implementation in China,” Higher Education Policy, pp. 1-25, 2025, doi: 10.1057/s41307-025-00410-6.
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295-316, 2020, doi: 10.1016/j.neucom.2020.07.061.
A. Semwal, X. Yue, Y. Shen, and M. Aibin, “Cloud Resource Allocation Recommendation Based on Machine Learning,” Proceedings of the 24th International Conference on Transparent Optical Networks (ICTON 2024); 14-18 July 2024; Bari, Italy. Piscataway, NJ, USA: IEEE; 2024. pp. 1-4, doi: 10.1109/ICTON62926.2024.10647993.
M. Ren, L. Yang, Y. Zhou, L. Lv, J. Chen, P. Xiao, et al., “Resource Allocation in Cell-Free MEC Networks: A Hierarchical MADRL-Based Algorithm,” IEEE Transactions on Cognitive Communications and Networking, vol. 12, pp. 4593-4609, 2025, doi: 10.1109/TCCN.2025.3645433.
A. Rathee and S. Dalal, “A systematic literature review of machine learning-based resource allocation techniques in cloud computing,” Computing, vol. 107, no. 9, pp. 179, 2025, doi: 10.1007/s00607-025-01526-8.
D. Deng, “A Performance Evaluation Model Based on AHP and Its Application,” Proceedings of the 2021 International Conference of Social Computing and Digital Economy (ICSCDE); 28–29 August 2021; Chongqing, China. Piscataway, NJ, USA: IEEE; 2021. pp. 26-29, doi: 10.1109/ICSCDE54196.2021.00015.
J. F. Chen, H. N. Hsieh, and Q. H. Do, “Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach,” Applied Soft Computing, vol. 28, pp. 100-108, 2015, doi: 10.1016/j.asoc.2014.11.050.
J. Zheng, J. Yu, L. Jin, and Z. Zhu, “An Empirical Study on the evaluation of primary compulsory education resource allocation based on TOPSIS Model with entropy weight method,” Proceedings of the 2022 10th International Conference on Orange Technology (ICOT); 10-11 November 2022; Shanghai, China. Piscataway, NJ, USA: IEEE; 2022. pp. 1-4, doi: 10.1109/ICOT56925.2022.10008179.
X. Zhao and Z. J. Hu, “The application of DEA model in the evaluation of higher education resource allocation— Taking Guizhou Province as an example,” Educational Theory and Practice: Subject Edition, vol. 29, no. 30, pp. 15-17, 2009.
Y. H. Guo and H. P. Xue, “An analysis of the evaluation of the allocation efficiency of primary and secondary school educational resources based on DEA method—Taking Hubei and Jiangsu provinces in central and eastern China as examples,” Proceedings of the 2009 Academic Annual Conference of China Educational Economics Association; 5th December 2009; Guangzhou, Guangdong, China. Guangzhou, Guangdong, China: Educational Economics Branch of the Chinese Society of Education; 2009. pp. 235-255.
S. Y. Zou, F. M. Li, A. P. Xie, and T. Zhou, “A resource allocation method based on random forest algorithm: China, 2020113133348.8,” 26th February 2021.
X. Z. Qian, J. Qin, and W. Song, “Improved parallel random forest algorithm and its out-of-bag estimation,” Application Research of Computers, vol. 35, no. 6, pp. 4, 2018, doi: 10.3969/j.issn.1001-3695.2018.06.011.
K. N. Fang, J. B. Wu, J. P. Zhu, and B. C. Xie, “A review of random forest method research,” Statistics & Information Forum, vol. 26, no. 3, pp. 7, 2011, doi: 10.3969/j.issn.1007-3116.2011.03.006.
K. Jurczuk, M. Czajkowski, and M. Kretowski, “Understanding evolutionary induction of decision trees,” Proceedings of the Genetic and Evolutionary Computation Conference Companion; 10-14 July 2021; Lille, France. New York, USA: Association for Computing Machinery; 2021. pp. 155-156, doi: 10.1145/3449726.3459422.
M. Krzywinski and N. Altman, “Classification and regression trees,” Nature Methods, vol. 14, no. 8, pp. 757-758, 2017, doi: 10.1038/nmeth.4370.