The Online Teaching Mode of College English in the Context of Gaussian Variant Genetic Algorithm

Abstract

The current college English online teaching mode is mainly based on the traditional online MOOC teaching, which has some problems such as poor interaction. Under the mixed background, this paper studies the online college English teaching model based on the Gaussian mutation genetic algorithm and neural network algorithm. Firstly, it briefly introduces the general situation of network English teaching and the hybrid application of the Gaussian mutation genetic algorithm. Through the investigation and test analysis of students before and after class, the experiment evaluates students’ network teaching quality in many aspects. On this basis, a better teaching quality evaluation model is proposed. Finally, the practical application shows that the model in this paper is very feasible. In the end, students have higher enthusiasm and seriousness in the hybrid context of college English online teaching based on the dual algorithm. English teaching quality can make use of each student’s test scores in English classroom. This paper realizes the overall teaching through real-time dynamic tracking. Quantitative indicators are used to sort the influence degree of English classroom teaching indicators, which can effectively evaluate the quality of English classroom teaching.