期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2015
卷号：10
期号：8
页码：16-20
语种：English
出版社：Kassel University Press
摘要：The scale of higher education is an essential link in the process of the formulation of education planning and reasonable allocation of teaching resources. At the same time, it also provides the required basis and support for the government to formulate educational planning and policy. The scale of higher education development is influenced not only by the level of economic development and industrial structure, but also by the total population and the living standards of residents. We take these elements as the influence factors, which contain noise information. Because the scale of higher education and its impact factors have complex nonlinear relationship, the traditional forecasting method cannot describe their changing trends, which leads to the low accuracy of prediction. In order to solve the above problems, this paper bases on the traditional GM (1,1) model to judge the number of students in the future, and uses the weakening buffer operator to amend the historical data. Secondly, this paper analyzes the structure of the system cloud gray forecasting model, and demonstrates its integral generation principle. We propose a new method for the cosine gray forecasting model which is based on the system cloud SCOS-GM (1, 1), and prove the effectiveness of SCOS-GM (1, 1) model by the residual test. Finally, the SCOS-GM (1, 1) model is utilized to predict the scale of higher education in China during the period of 2012-2014. The results show that the scale of higher education will demonstrate a gradual upward trend in the next few years.
其他摘要：The scale of higher education is an essential link in the process of the formulation of education planning and reasonable allocation of teaching resources. At the same time, it also provides the required basis and support for the government to formulate educational planning and policy. The scale of higher education development is influenced not only by the level of economic development and industrial structure, but also by the total population and the living standards of residents. We take these elements as the influence factors, which contain noise information. Because the scale of higher education and its impact factors have complex nonlinear relationship, the traditional forecasting method cannot describe their changing trends, which leads to the low accuracy of prediction. In order to solve the above problems, this paper bases on the traditional GM (1,1) model to judge the number of students in the future, and uses the weakening buffer operator to amend the historical data. Secondly, this paper analyzes the structure of the system cloud gray forecasting model, and demonstrates its integral generation principle. We propose a new method for the cosine gray forecasting model which is based on the system cloud SCOS-GM (1, 1), and prove the effectiveness of SCOS-GM (1, 1) model by the residual test.
Finally, the SCOS-GM (1, 1) model is utilized to predict the scale of higher education in China during the period of 2012-2014. The results show that the scale of higher education will demonstrate a gradual upward trend in the next few years.
关键词：system cloud;higher education scale;gray model
