An Analysis Model of College English Classroom Patterns Using LSTM Neural Networks

Abstract

With the advent of the information age, IT represented by mobile computing and neural network has been widely used in various fields of education, and IT has become an indispensable part of university classrooms. Mobile devices can be used to promote teacher-student interaction, and neural networks can be used to update teaching methods. The goal of CET is to help students develop their listening, speaking, reading, and writing skills. Although some studies have discovered a certain level of poor learning adaptability in the practice of AI-supported College English Teaching (CET), the majority of the studies only describe the phenomenon without delving deeper into the causes. The deep integration of AI and education is promoting the growth of a new college English education ecosystem. The research is aimed at understanding the current situation of college students’ English learning adaptability supported by AI, clarifying the influence effect of various influencing factors on learning adaptability and the relationship between learning adaptability and various factors, and proposing strategies to improve students’ learning adaptability through investigation and analysis of college students’ English learning adaptability supported by AI.