Recently, the problem of the demand for and supply of teachers in junior high schools has been paid more attention in education administration. An accurate forecast of the number of teachers needed in junior high schools may heavily affect educational policy. In this paper, we use the univariate time series analysis, state space and Neural Networks to forecast the number of teachers in the junior high school of Taiwan Area during a period from 1988 to 1993. It was found that the state space and Neural Networks exhibit a much more successful forecast than the univariate ARIMA model.
| Keywords: | Taiwan; Junior High School Teachers; Teacher Supply and Demand; Forecasting Model |
|---|
[ Chinese version | Index | Journal of Education and Psychology | Other Journals | Faculty Home Page | Enquiry ]
Mail any comments and suggestions to
en@fed.cuhk.edu.hk.