The Forecast Of Student Population Tendency In Enrollment And The Research On Enrollment Policy

The competition for student source among colleges is becoming more and more fierce nowadays. Therefore it's rewarding to forecast the tendency of student population in enrollment and the main influential factors, according to which the college could develop corresponding policies. In this way, the college could promote the propaganda, the consultation and the enrollment, and hence improve the quality of student source and expand the influence and reputation of colleges to attract more outstanding senior high school graduates.The focus of the article is to explore the two aspects that have decisive effect on the development of enrollment policy: the forecast of the tendency of student population in enrollment and the analysis of the factors that affect the students'decision-making when filling in the voluntary table of college entrance examination, on the basis of which an enrollment policy with practical value is put forward.Firstly, the thesis forecasts the tendency of student population in enrollment with Grey-Time Series Combined Forecasting Model, including the introduction of some basic knowledge on Grey-Time Series Combined Forecasting Model, the method to develop the model, the perfection and successful application of the model in the forecast of the tendency of student population in enrollment, analyzing the advantages and drawbacks of the original and perfected models. Secondly, the thesis analyzes the factors that that affect the students'decision-making, including a questionnaire and riddling of the main factors that could affect students'decision making by using Stepwise Regression Model. And then the author puts forward three policies in college enrollment policy and looks into the further research in this area. The main task of the thesis is about the perfection of the Grey-Time Series Combined Forecasting Model, which has the advantage in fitting data with quality of both trends and fluctuations. The error in result, however is comparatively larger. After the perfection, the model performs better in fitting the error and forecasting, which is successful in its application in analyzing the tendency of student population in enrollment.The limit of the thesis lies in the fact that the analysis is confined within only certain colleges for the lack of data and others. The analysis would be very eaningful for the college enrollment, students'decision-making and the policy-making if more data could be obtained to analyze the enrollment ratio among the qualified students and the tendency of students'decision by using the model.