Multimodal English Teaching Classroom Interaction Based on Artificial Neural Network

Abstract

In recent years, with the rapid development of science and technology, traditional teaching methods and concepts have been frequently impacted. Artificial neural network shows excellent intelligence because of its powerful nonlinear processing ability and efficient associative function. It is increasingly becoming an emerging object in the field of artificial intelligence. At the same time, in the field of education and teaching, the integration of English teaching and multimodality not only condenses the characteristics of the times but also expands new teaching models, bringing opportunities for the emergence of new teaching models. Based on this, this study proposes an interactive method for multimodal English teaching based on artificial neural networks. It aims to study how to use the autonomous learning of artificial neural networks to accelerate the fusion of different modalities and at the same time make suggestions for different teaching interaction modes. This paper firstly analyzes the interaction of English teaching under the traditional mode. It then proposes a multimodal fusion interaction method based on artificial neural networks. It finally explores the feasibility of the new interaction theory by setting up an experimental group and a control group. Through the analysis of the experimental data, the final data results show that the multimodal fusion interaction based on artificial neural network has a very significant effect, and the students' interest in the English classroom is as high as 81.9%. This fully demonstrates the great value of the new fusion method, and it has certain enlightening significance for the establishment of English teaching modes and curriculum reform.