Application of Flipped Classroom Model Driven by Big Data and Neural Network in Oral English Teaching

Abstract

With the advancement of big data and neural network technology, flipped classroom informatization has shifted the traditional order of knowledge transfer and internalization, emphasizing students’ autonomous learning before class, knowledge absorption, and knowledge completion in class with the assistance of teachers. Students’ internalization and consolidation create the conditions for individualized learning. In foreign teaching, the benefits and feasibility of the flipped classroom have been demonstrated, and it is a promising new teaching model. Although recent research on oral English teaching in Chinese universities has yielded promising results, students’ classroom activity and participation remain low, learning initiative is lacking, and opportunities and time for oral training are insufficient. This article uses flipped classroom, big data, and neural network technology to teach college oral English classes, with the goal of determining whether the flipped classroom model can help students improve their oral English proficiency and self-learning ability, as well as exploring students’ attitudes toward the flipped classroom model. This paper first proposes a big data and deep neural network-based algorithm for detecting oral English pronunciation errors, which can be used for self-correction of students in the flipped classroom mode to improve the quality of oral English teaching. Finally, we also conducted simulation experiments, and the experimental results show that our algorithm is 4.12% better than SVM.