Research on Intelligent Courses in English Education based on Neural Networks

Abstract

Accurately predicting students’ performance plays a crucial role in achieving the intellectualization of courses. This paper studied intelligent courses in English education based on neural networks and designed a firefly algorithm-back propagation neural network (FA-BPNN) method. The correlation between various features and final grades was calculated using the students’ online learning data. Features with higher correlation were selected as the input for the FA-BPNN algorithm to estimate the final score that students achieved in the “College English” course. It was found that the training time of the FA-BPNN algorithm was 3.42 s, the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of the FA-BPNN algorithm were 0.986, 0.622, and 0.205, respectively. They were lower than those of the BPNN, genetic algorithm (GA)-BPNN, and particle swarm optimization (PSO)-BPNN algorithms, as well as the adaptive neuro-fuzzy inference system approach. The results indicated the efficacy of the FA for optimizing the parameters of the BPNN algorithm. The comparison between the predicted results and actual values suggested that the average error of the FA-BPNN algorithm was only 0.5, which was the smallest. The experimental results demonstrate the reliability of the FA-BPNN algorithm for performance prediction and its practical application feasibility.