Preschool Teachers Teaching Quality Evaluation Based on Neural Network Algorithms

Abstract

Based on the current teaching situation of preschool teachers, in order to comprehensively evaluate the effectiveness of early childhood teaching, research have constructed a teaching quality evaluation model using fuzzy synthesis method and expert method. This model can handle the fuzzy relationship between evaluation indicators and achieve the evaluation of teaching quality. Considering that the teaching evaluation is influenced by many factors, the Genetic Algorithm back propagation (GA-BP) neural network algorithm is chosen for the solution model construction of the preschool teacher teaching quality. The entropy method chosen for the data calculation is to complete the preschool teaching quality evaluation. In the mean square error test for solving the model, the improved GA-BP model converged after 40 iterations with the model convergence speed increased by 34.65%. In the evaluation and prediction of preschool teacher indicators, the improved GA-BP model accurately evaluated the teacher classroom teaching indicators. In sample 3, sample 6, and sample 9, the improved GA-BP model scored 91, 89, and 88 points, respectively, close to the true scoring results. The improved model’s accuracy was high as 90% in teacher skills, personality charm, and academic research evaluation. The improved model also effected better in the application of the preschool teaching quality evaluation. The application effect of this model in teaching effectiveness evaluation and teacher quality evaluation is good, providing valuable reference for the establishment of early childhood education evaluation system.