A study of English learning anxiety regulation strategies based on the deep learning model

Abstract

Abstract Foreign language learning anxiety is prevalent in students’ foreign language learning process. To better alleviate students’ anxiety situation, this paper constructs a self-coding training model for anxiety regulation strategies based on recurrent neural networks in deep learning models. Anxiety-related factors affecting English learning are input into the recurrent neural network as the input layer. The data are corrected by balancing the data in the input layer through encoding and decoding in the implicit layer. The corrected data is reconstructed and transformed as the input layer of the next level of the recurrent neural network. The above steps were repeated continuously for layer-by-layer training until the same output layer parameters as the pre-trained model were reached. This resulted in a learning anxiety regulation strategy of changing the student learning environment and self-regulation. To verify that the above strategies can reduce the anxiety value, a simulation was conducted, and the results showed that the number of superior students in the multimedia environment was 2% higher than that in the traditional teaching model. Students’ anxiety was reduced from 18% to 7% after active and effective self-regulation. From these results, it is clear that the anxiety regulation strategy derived from the deep learning model is feasible and ensures the healthy development of students.