College English Flipped Classroom Teaching Model Based on Big Data and Deep Neural Networks

Abstract

With the rapid development of information technology, flipped classroom as a new type of mixed teaching mode relying on computer technology has changed the traditional teaching mode and formed a teaching process of “learning first and teaching later,” and it has been used in many fields of teaching. Flipped classroom reverses the sequence of traditional teaching knowledge transfer and knowledge internalization and improves students’ autonomy. However, it is still in the exploratory stage of the specific impact of the flipped classroom teaching model on college students’ English autonomous learning ability. Therefore, this article proposes a novel college English flipped classroom teaching model based on big data and deep neural networks. The study has selected a total of 230 students in two classes of the second-year English major of a university as the research objects. Data are utilized to investigate the changes of the two groups of students’ English autonomous learning ability and English academic performance, to explore the specific changes of college students’ English autonomous learning ability and its influencing factors through interviews, and to predict and effectively analyze the weight of influencing factors through the deep neural network. This research enriches the theoretical research results of college students’ English autonomous learning ability under the flipped classroom teaching model, provides reference for the cultivation of college students’ English autonomous learning ability, and has certain reference significance for the optimization of the flipped classroom teaching model. The proposed research will support researchers and practitioners at college and university level.