Neural Network-Based Approach for Evaluating College English Teaching Methodology

Abstract

A fair and scientific method of evaluating collegiate English instruction is currently lacking. Traditional methods are commonly used to evaluate the quality of college English education in terms of the standard statistical analysis evaluation model. As the evaluation of English teaching is a nonlinear issue, the above strategies have achieved some good results but have certain limitations. They are not scientific and objective in the selection of evaluation indicators, or in the setting of evaluation index weights, and there is a certain degree of subjectivity. Artificial neural network (ANN) is widely used in massive fields due to its characteristics of nonlinear processing, adaptive learning, and high fault tolerance. As a kind of neural network, BP neural network has strong nonlinear mapping ability, so it is feasible and scientific to solve the nonlinear relationship of college English teaching evaluation (ETE). Therefore, in this work, we first designed an ETE system index. Then, a strategy of college ETE with BP network is designed, which can carry out high-performance modeling for the designed teaching evaluation index. In order to alleviate the issue of slow convergence speed for BP network and fall into local optimum, this work also combines particle swarm algorithm with BP network to further improve network performance. Massive experiments have proved the reliability and effectiveness of this work.