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

• Published in: Applied sciences 2022-05, Vol.12 (10), p.4987
• Main Authors: Wang, Na, Ren, Ziru, Zhang, Zheng, Fu, Junsong
• Format: Article
• Language: English
• Subjects: Accuracy; analytic hierarchy process; Astronomical models; Education; Education reform; Environmental education; Evaluation; Fuzzy sets; Higher education; higher education system; Indicators; Long short-term memory; Markov analysis; neural network; Neural networks; Predictions; Steel industry; Support vector machines; Time series
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12104987

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.