Construction Quality Evaluation of Teacher Development Schools Based on Improved Artificial Neural Network

Abstract

There are many problems with the convention teacher education model, such as the short internship time during pre-job training, and the limited experience, pertinence, and effectiveness of on-the-job training. Fortunately, the teacher development school mechanism provides a viable solution to these problems. Therefore, the construction quality of such schools is of great significance to the teaching level and professional development of school teachers, as well as the overall development of students. As a result, this paper proposes a method to evaluate the construction quality of teacher development schools based on an improved artificial neural network. Firstly, an evaluation index system was established for the construction quality of teacher development schools, which consists of 5 core evaluation indices, and the periodical scoring criteria were detailed. Then, the feasibility of the proposed evaluation index system was verified through reliability, validity, and difference analyses. Finally, a combined neural network was constructed to evaluate the construction quality of teacher development schools. The experimental results show that our model can effectively predict the construction quality of teacher development schools, providing a reference for project quality evaluation in other fields.