An Evaluation Method for the Construction of Double-Qualified Teachers in Vocational Schools Based on ID3 Algorithm and Neural Networking

Abstract

In vocational schools, the traditional evaluation approach of forming a double-qualified teaching team has the issue of an imperfect evaluation standard, resulting in low accuracy. Based on the ID3 algorithm, this research develops an evaluation technique for double-qualified teacher team construction in vocational schools. The characteristics of dual-teacher education are gathered, customized training plans are produced, and the selection mechanism for vocational school teachers is maximised based on the requirements of students’ vocational ability training. By calculating the information gain of each attribute, the ID3 algorithm is used to formulate teacher evaluation standards, and dimensionless post-data are comprehensively processed to design the evaluation mode of teacher team construction. Experimental results: the average accuracy of the evaluation method for the construction of double-qualified teachers in vocational schools based on association rules, as well as the other two evaluation methods, was 69.959%, 58.851 percent, and 58.744 percent, respectively, indicating that the evaluation method for the construction of double-qualified teachers in vocational schools integrated with the ID3 algorithm was more accurate.