Construction of Primary and Secondary School Teachers’ Competency Model Based on Improved Machine Learning Algorithm

Abstract

In order to quantitatively evaluate the competence of primary and secondary school teachers, a competency model of primary and secondary school teachers based on an improved machine learning algorithm is proposed. The fitting parameter analysis model of primary and secondary school teachers’ competency is constructed, and the fitting benefit degree parameter of primary and secondary school teachers’ competency is extracted based on the analysis results of reliability index parameters. The improved machine learning algorithm is used to carry out quantitative analysis and characteristic element analysis in the process of primary and secondary school teachers’ competency evaluation and determine the competency elements of the model. According to the machine learning model, the competency elements are conceptualized and classified, and the theoretical parameter analysis model of online teaching competency of primary and secondary school teachers is constructed to realize the assessment and quantitative analysis of primary and secondary school teachers’ competency. Factor analysis and reliability tests were performed using the KMO test and Bartlett test. The empirical simulation analysis results show that the reliability and accuracy of the evaluation of primary and secondary school teachers’ competence by this method are good, and the level of credibility is high.