Matching Prediction of Teacher Demand and Training Based on SARIMA Model Based on Neural Network

Matching Prediction of Teacher Demand and Training Based on SARIMA Model Based on Neural Network

Jianliu Zhu
DOI: 10.4018/IJITWE.333637
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Abstract

This study introduces the ‘SARIMA Improved Model + Pearson Correlation Coefficient' approach to predict the demand for big data jobs in Jiangsu Province schools from January 2016 to December 2019. It also explores the matching between demand and supply in universities. The model is fault-tolerant, offers fast predictions, and addresses the disconnect between college talent training and teacher demand. The SARIMA-BP model predicts the trend of big data teacher demand in Jiangsu Province. The model, though untested in recruitment data prediction, with a large database, achieves root mean square error of 7.66, indicating high precision and reliability. Based on matching research and the local big data education industry in Jiangsu Province, countermeasures and suggestions are presented under the “one body, two wings, and one tail” framework. This concise summary highlights the research's core components and objectives.
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In “Education and Job Match: The Relatedness of College Major and Work,” foreign scholars such as Robst and domestic scholars such as Wang Xiaoqin, Guo Qingling, Xu Ren, etc., have discussed the mismatch between the employment direction of graduates and their majors (Huang et al., 2019).Through the level of scores to judge the degree of fit between job seekers and positions (Ghadimi et al., 2018).

Zhou have take a a questionnaire on matches between college graduates’ majors and initial employment served to examine the employment of college students in the past five years from multiple dimensions. The article proposed that students who have a higher level of matching between their majors in colleges and their initial employment positions will reduce the probability of changing their employment direction, and the two are negatively correlated. It helps to improve work efficiency and reduce work pressure (Zhou et al., 2022).

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