Sequence Model and Prediction for Sustainable Enrollments in Chinese Universities

Higher education has made significant contributions to the sustainable development of global society in terms of improving the level of science and technology and optimizing the moral system of citizens. The number of students enrolled in higher education and the proportion of its types are important indicators that reflect the development level of a country’s higher education. As a country with a large population base, China’s development sequence of the number of college students and the proportion of its types in recent years is an important reflection of the sustainable development of global education. Therefore, according to the time series data of the number and types of enrollment in Chinese colleges and universities from 2010 to 2020, this study uses methods such as polynomial regression and Holt’s exponential smoothing prediction to establish a statistical model and predict the number of college enrollment, its chain growth rate, and the proportion of types in recent years. It also examines the differences in the overall level and degree of fluctuation for the number of people in different regions and the chain growth rate. The results show that the number of students enrolled in Chinese colleges and universities is expanding, and their chain growth rate is also increasing. There are significant differences in the degree of growth in different regions, and the increases in the west and south are greater than that in the east and north. The prediction results show that the predicted value of China’s enrollment and its chain growth rate will continue to increase in the next few years. The proportion of undergraduates in college enrollment dropped significantly since 2019, and most provinces have experienced similar situations. Finally, this study also proposes some policy recommendations that can promote the sustainable development of education in view of the above sequence trends. The novelties of this paper are reflected in the materials, methods, and perspectives because it adopts the latest dynamic enrollment data, applies a variety of predicting methods to the analysis of enrollment in universities, and locks the perspective on China, specifically in China’s provinces and regions.