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Student Enrollment and Teacher Statistics Forecasting Based on Time-Series Analysis
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-09-18 , DOI: 10.1155/2020/1246920 Stephanie Yang, Hsueh-Chih Chen, Wen-Ching Chen, Cheng-Hong Yang
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2020-09-18 , DOI: 10.1155/2020/1246920 Stephanie Yang, Hsueh-Chih Chen, Wen-Ching Chen, Cheng-Hong Yang
Education competitiveness is a key feature of national competitiveness. It is crucial for nations to develop and enhance student and teacher potential to increase national competitiveness. The decreasing population of children has caused a series of social problems in many developed countries, directly affecting education and com.petitiveness in an international environment. In Taiwan, a low birthrate has had a large impact on schools at every level because of a substantial decrease in enrollment and a surplus of teachers. Therefore, close attention must be paid to these trends. In this study, combining a whale optimization algorithm (WOA) and support vector regression (WOASVR) was proposed to determine trends of student and teacher numbers in Taiwan for higher accuracy in time-series forecasting analysis. To select the most suitable support vector kernel parameters, WOA was applied. Data collected from the Ministry of Education datasets of student and teacher numbers between 1991 and 2018 were used to examine the proposed method. Analysis revealed that the numbers of students and teachers decreased annually except in private primary schools. A comparison of the forecasting results obtained from WOASVR and other common models indicated that WOASVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) for all analyzed datasets. Forecasting performed using the WOASVR method can provide accurate data for use in developing education policies and responses.
更新日期：2020-09-20
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