An Evaluation Model of Online Autonomous English Learning Efficiency Using an Artificial Neural Network

Abstract


 Traditional classroom teaching model is constantly impacted by online education model. To enhance the online English learning efficiency of college students, one of the primary paths is to cultivate their autonomous learning ability. However, the existing research is limited to quantitative research. Therefore, this paper devises an evaluation model of online autonomous English learning efficiency based on artificial neural network. Firstly, the students participating in online English learning were classified, the evaluation indices of online autonomous English learning efficiency were determined and weighed, and the classification method was introduced for the evaluation results. Next, the improved particle swarm optimization (PSO) was adopted to optimize the traditional backpropagation neural network (BPNN). The improved BPNN was employed to evaluate the online autonomous English learning efficiency. The superiority of the improved model was demonstrated through experiments.

Current Situation Dissection and Ability Cultivation Strategies of Online Autonomous Learning for College Students

“…Therefore, cultivating and improving students' knowledge, abilities, emotional attitudes and values are of great significance. The learning ability of college students not only includes knowledge, skills and attitudes, but also encompasses values [13][14]. Cultivating students' awareness of autonomous learning during their college years is essential.…”