Research on Education Teaching Quality Analysis Based on the Neural Network Model

Abstract

Universities emphasize quality review and monitoring. To effectively evaluate classroom teaching effectiveness, a trustworthy model of teaching quality evaluation is required. Due to the fact that teaching is a dynamic process, numerous elements influence teaching quality, and the link between assessment index and teaching effect is complex and nonlinear. There are numerous methods for measuring the quality of classroom instruction, but the vast majority of it relies on a single machine learning algorithm, making it difficult to construct an accurate and reliable mathematical model. In this paper, we employ the AdaBoost’s multicore neural network learning algorithm to learn several weak classifiers and combine them into a single strong classifier. We also transfer the classification probabilities into teaching quality outcomes to obtain the final teaching quality results. Our model offers a new, effective way for evaluating the quality of classroom instruction, and it can serve as a solid theoretical resource for reforming classroom instruction.