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Cited by 13Higher Education Governance and DevelopmentMulti-Criteria Decision MakingEvaluation and Optimization Models

Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network

Open access
Wang, Na, Ren, Ziru, Zhang, Zheng, Fu, Junsong·2022·Applied Sciences
DOI 10.3390/app12104987
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Abstract

A healthy and sustainable higher education system plays an important role in social development. The evaluation and prediction of such a system are vital for higher education. Existing models are usually constructed based on fewer indicators and original data are incomplete; thus, evaluation may be inefficient. In addition, these models are generally suitable for specific countries, rather than the whole universe. To tackle these issues, we proceed as follows: Firstly, we select a series of evaluation indicators that cover most aspects of higher education to establish a basic evaluation system. Then, we choose several representative countries to illustrate the system. Next, we use the analytic hierarchy process (AHP) to calculate a weight matrix of the indicators according to their importance. Furthermore, we obtain authoritative data from these countries. Then, we apply the indicators to the technique for order preference by similarity to an ideal solution (TOPSIS) algorithm to ascertain their relative levels. Finally, we combine the weight matrix with the relative levels to achieve a comprehensive evaluation of higher education. So far, a theoretical establishment of a higher education evaluation model has been generally completed. For better practical application, we add a predictive function to our evaluation model. Starting with China, we predict the development of national higher education for the next 20 years. We adopt a long short-term memory (LSTM) neural network as a method of prediction. Considering the significant influences of national policies on higher education, we address the issues under two circumstances: with or without policy influences. At last, we compare our model with existing models. Experimental results show that our model better reflects national higher education levels and provides more reasonable and robust prediction results.

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  • The evaluation of LLM-generated educational content can be formulated as a multi-criteria decision-making problem.

    “Therefore, the evaluation of LLM-generated educational content can be formulated as a multi-criteria decision-making problem, where multiple criteria must be considered simultaneously [5][6][7].”

    From An Integrated AHP–Fuzzy AHP Evaluation Framework for Large Language Models in Software Engineering Education (2026) · cites this paper as [7]

    The Introduction describes evaluating higher-education systems using multiple weighted indicators, and Section 3.3 identifies AHP as a multicriteria decision-making method. The paper does not address LLM-generated educational content, so it supports the multi-criteria approach but not its application to that specific content. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 10

  1. Yi, M.C. Popularization process of higher education in China and its influencing factors-Prediction based on time series trend extrapolation model. China High. Educ. Res. 2016, 3, 47-55.
  2. Yong, Q.; Ou, L.Y. Research on the Quality Evaluation of Higher Education Development Based on CIPP Model. Sci. Educ. Lit. Collect. 2020, 3, 1-3.
  3. Saaty, T.L.; Vargas, L.G. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process (book). In International Series in Operations Research & Management Science; Springer: Berlin/Heidelberg, Germany, 2012.
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  5. Hu, Y.M.; Tang, Y.P. Prediction of the scale of students and financial investment in Higher Education during the 14th five year plan. Chongqing High. Educ. Res. 2019, 7, 10-22.
  6. Mi, H.; Wen, X.L.; Zhou, Z.G. An empirical analysis of population factors and the change of China's higher education scale in the next 20 years. Popul. Res. 2003, 6, 76-81.
  7. Bie, D.R.; Yi, M.C. Development standard, process prediction and path selection of higher education popularization. Educ. Res. 2021, 42, 63-79.
  8. Zheng, F.X. Comparative study of ARIMA and exponential smoothing in regional higher education scale prediction. J. Sichuan Univ. Sci. Technol. 2013, 26, 83-85.
  9. Li, S.H.; Li, W.P. Research on the scale development of China's higher education from 2013 to 2030-Based on the analysis of the age population and economic level. Open Educ. Res. 2013, 19, 73-80.
  10. Hu, D.X.; Wang, M. Trend prediction of China's higher education scale from 2016 to 2032. Educ. Acad. Mon. 2016, 6, 3-7.

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Authors
Wang, N., Ren, Z., Zhang, Z., & Fu, J.
Affiliations
  • Beihang University (CN)
  • Beijing University of Posts and Telecommunications (CN)
Corresponding author
Fu, Junsong
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2022-05-15
Volume
12
Issue
10
Pages
4987
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app12104987
OpenAlex
W4280523842
Cited by
13
References
34
Citations by year
2026: 4 · 2025: 4 · 2024: 3 · 2023: 1 · 2022: 1
FWCI
7.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (62102017, 62001055)
Topics
  • Social Sciences › Political Science and International Relations › Higher Education Governance and Development
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Evaluation and Optimization Models
Keywords
higher education assessment, indicator weighting, education policy impact, cross-national comparison

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