An Adaptive BP Neural Network Model for Teaching Quality Evaluation in Colleges and Universities

Abstract

There is currently no fair, rational, or scientific approach for evaluating college teachers' teaching abilities. Mathematical methods are frequently used to measure the teaching capacity of college instructors in order to make it more scientific. Traditional statistical analysis evaluation models, fuzzy evaluation methods, grey decision methods, and the analytic hierarchy process (AHP) are only a few examples. Because teacher assessment is a nonlinear problem, even though the preceding methods have produced some positive results, they are vulnerable to some subjectivity. In this paper, the neural network model is incorporated into the adaptive vector and momentum of the modified BP neural network of a gradient descent method to boost the model's convergence speed, and the model is thoroughly researched to evaluate university teaching quality, and the network structure is omitted to address the complex nonlinear problem of college and university teaching quality assessment. The model's comprehensive evaluation of teaching activities is then bolstered by the addition of new evaluation indexes to the existing ones.