Application of Convolutional Neural Network-Based Hierarchical Teaching Method in College English Teaching and Examination Reform

Abstract

Along with the pace of educational reform in colleges and universities, a variety of new types of teaching and research approaches stand out in each subject taught in colleges and universities. For example, in college English lectures, given the practice of individualized tiered teaching, the development of relevant teaching models for students at different levels has become a new type of teaching and research developed year by year. Based on the English classroom program, teachers should make cognizance of the tiered teaching model when teaching. This paper discusses the tiered teaching method of English teaching and carries out teaching from strategies such as paying attention to students’ tiered teaching, doing well in lecture tiered teaching, developing homework tiered teaching, and paying attention to evaluation tiered teaching. In addition, the assessment system of college English courses lags behind the development of college English teaching reform and cannot play a guiding role in teaching. In response to the above-mentioned views and problems, this paper proposes a convolutional neural network-based algorithm that provides different learning styles for different students in the stratified teaching method of college English, making capable students understand what they learn in class, improving the teaching quality of high school English courses, and, at the same time, establishing a standardized and scientific course with high reliability and validity that meets the actual situation of applied technical college students. At the same time, a standardized and scientific course assessment system with high reliability and validity has been established to meet the actual needs of applied technical college students.