Optimized Design of English Learning Environment Based on Deep Neural Network

Shaoli Yan and Zaoli Yang

Mathematical Problems in Engineering, 2022, vol. 2022, 1-10

Abstract: The learning environment is an important support condition for learning and an important variable affecting learning, so it is an important research content of learning theory, and the understanding of the learning environment is also developing and changing as the educational theory continues to develop. The new curriculum standards for college English demand teachers alter the original conventional way of pedagogy and optimize students’ mode of learning. In teaching practice, the learning atmosphere exerts an extremely influential influence on the smooth implementation of teaching activities and the healthy development of students’ minds and bodies. Deep learning is a hot research topic among machine learning areas in recent years, and deep belief networks as a pioneer in constructing such deep structures. Also, deep neural network (DNN) has been a hot research topic in the field of artificial intelligence and big data analysis in recent years. A DNN-based English learning environment optimization design is put forward in this paper, focusing on the problems of the English learning environment in colleges and the causes of the problems, and exploring strategies to optimize the English learning environment in colleges in order to promote the normal development of English teaching in colleges. The experimental results show that the DNN can improve the overall recognition rate of fault identification and fault location by 13% and 25% on average compared with the other two algorithms, so the deep learning can extract features directly from the original samples and overcome the defect that the neural network is easy to fall into local optimum, and obtain better results. The optimization of the learning method will help to realize the education concept of “human-oriented and comprehensive development,†and help to stimulate students’ enthusiasm, initiative, and exploration in learning.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2502259

DOI: 10.1155/2022/2502259

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