Teaching Quality Evaluation Model of University Teachers Based on Neural Network

Abstract

Considering the structural characteristics and the adaptive and selflearning functions of neural networks, a teaching quality evaluation system for high-level teachers was developed using the BP neural algorithm. The system's mathematical model was established, with each evaluation index serving as input and teaching effectiveness as output. The model demonstrated feasibility and applicability in terms of convergence speed and network adaptability. Experimental results indicated that the mathematical model effectively addressed the complexity and interference of human factors in traditional analysis and evaluation of teaching processes. The model exhibited features such as convenience, accuracy, reliability, and speed, with high identification accuracy.