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

Baican Yang,Shan Zhong,Yunfu Zhang,Yiwen Yang,Yong Jin

doi:10.1155/2021/4936873

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.

Highlights

With the rapid development of higher education [1,2,3,4], a school’s reputation has become the most important criterion for students when choosing a school, and a school’s reputation is largely determined by the quality of its education [5,6,7]
The teaching quality of colleges and universities is reflected in the teaching quality of each specialty or department, the teaching quality of each department is reflected in the quality of each course, and the course quality is reflected in the teaching quality of each teacher who teaches the course [8, 9]
The development of a scientific and reasonable teaching quality evaluation system [14] that can accurately and equitably assess teaching quality is a significant challenge. Because it is a complex and abstract nonlinear problem, it is difficult to express the evaluation of teaching quality at colleges and universities using a mathematical model or analytical formula

Summary

Introduction

With the rapid development of higher education [1,2,3,4], a school’s reputation has become the most important criterion for students when choosing a school, and a school’s reputation is largely determined by the quality of its education [5,6,7]. As a result of the large-scale enrollment increase of schools and universities for several years in a row, a number of related issues have emerged, including a teacher shortage, a decline in the quality of students, and a shortage of educational and teaching equipment and logistics facilities. The development of a scientific and reasonable teaching quality evaluation system [14] that can accurately and equitably assess teaching quality is a significant challenge Because it is a complex and abstract nonlinear problem, it is difficult to express the evaluation of teaching quality at colleges and universities using a mathematical model or analytical formula. The model contains an adjustable learning rate and momentum term to improve the gradient descent method of the BP neural network’s convergence speed and optimize the network topology to ensure the model’s stability

Background

Methodology

Experiments and Results

Conclusion