Welcome to Francis Academic Press

Frontiers in Educational Research, 2021, 4(3); doi: 10.25236/FER.2021.040318.

Evaluation of the Health Status of the National Higher Education System Based on Mathematical Model


Chenyang Sun, Xueqi Wang, Xinxin Xu

Corresponding Author:
Chenyang Sun

School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233000, China


In order to measure and evaluate the health status of the higher education system at the national level, first,select index factors that can reflect the level of higher education in the country, and establish an entropy weight-weighted Topsis comprehensive evaluation model, which is measured by the comprehensive evaluation index and the index factor weight ratio The level of higher education in the country. At the same time, in order to measure the sustainable state of the national higher education system, taking China as an example,by constructing a prediction model based on MLP multilayer perceptron, combined with an evaluation model, predicting the country's education level in the next few years. Finally, policy recommendations are made on the health and sustainability of the national higher education system.


entropy weight-weighted Topsis, MLP multilayer perceptron, higher education system, health status

Cite This Paper

Chenyang Sun, Xueqi Wang, Xinxin Xu. Evaluation of the Health Status of the National Higher Education System Based on Mathematical Model. Frontiers in Educational Research (2021) Vol. 4, Issue 3: 89-97. https://doi.org/10.25236/FER.2021.040318.


[1] Xu Limin, Qian Jianping. Enlightenment of American Higher Education Quality Assurance System to Our Country [J].]; 2 China Construction Education, 2006(08):9-12.

[2] Huang Huijuan. A Preliminary Study on the Quality Assurance System of American Higher Education [D].]; and Fujian Normal University, 2005.

[3] Wang Lili, Chen Kezheng, Ren Suzhen. A Study and Practice on the Construction of Self-Assessment System of Teaching Quality in Colleges and Universities [J]. Journal of Qingdao University of Science and Technology (Social Sciences Edition) 28(04):101-104.

[4] Xu Weiwei, Wu Jiancheng, Zeng Dewei. Construction of Evaluation System of Teaching Quality in Colleges and Universities [J].] A Discussion Journal of Jiangsu Institute of Technology (Social Sciences Edition), 2007(02):59-61.

[5] Liu Junshan, Meng Wanjin. Discussion on the Quality of Higher Education Evaluation Index System [J]. Jiangsu Higher Education, 1999(06): 11-13.

[6] Usher Natalie. Developing evaluative judgement in higher education. Assessment for knowing and producing quality work [J]. Assessment in Education: Principles, Policy & Practice, 2020, 27(6).

[7] Rosaria Lumino, Dora Gambardella. Re-framing accountability and learning through evaluation: Insights from the Italian higher education evaluation system [J]. Evaluation, 2020, 26(2).

[8] Liu Hui, Hong Rui, Lang Zhihui, Yao Jieyu, Ye Dong, Shan Jiangliang, Liu Xin. Evaluation of the spontaneous combustion tendency of corrosion products in oil tanks based on TOPSIS methodologies [J]. Journal of Loss Prevention in the Process Industries, 2021, 71.

[9] Hou Rui, Zhang Bixi. Regional logistics demand forecasting method based on MLP neural network and its application [J]. System Engineering Theory and Practice, 2005(12): 43-47.

[10] Rodríguez Fermín, Genn Michael, Fontán Luis, Galarza Ainhoa. Very short-term temperature forecaster using MLP and N-nearest stations for calculating key control parameters in solar photovoltaic generation [J]. Sustainable Energy Technologies and Assessments, 2021, 45.

[11] Bolourian Haghighi Behrouz, Taherinia Amir Hossein, Harati Ahad, Rouhani Modjtaba. WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II [J]. Applied Soft Computing Journal, 2021, 101.