Construction and Application of a Task-Based Teaching Model of College English Based on Random Matrix Theory

Abstract

Society is in urgent need of English talents with high comprehensive English application ability and strong independent learning ability. Task-based language teaching has always been an effective way to improve English talents, but the shortcomings of traditional task-based language teaching in teaching practice are beginning to emerge, such as insufficient classroom time, lack of real-time monitoring, and scientific evaluation mechanisms. Therefore, the traditional task-based language teaching cannot meet the new requirements of society for the cultivation of English talents. Random matrix theory uses the principles of statistical mechanics to model the interaction of complex systems in multiple mathematical domains to improve the teaching effectiveness. Random matrix theory has been widely used in theoretical neuroscience and optimal control. This paper is based on teaching task-based language and analyzing the data from the teaching process with big data so as to give timely feedback and suggestions to teachers. This paper constructs a model of college English teaching based on random matrix theory using a task-based teaching model. Since the teaching model of college English is a typical high-dimensional nonlinear function problem, a network model of deep confidence network is used to determine the implicit relationship between input and output. To verify the effectiveness of the model, this paper conducts a practical study of the constructed model. The study shows that the task-based teaching model of college English based on random matrix theory improves college students’ English expertise and English language usage ability and also greatly enhances students’ enthusiasm for learning English.