Prediction, Monitoring, and Management of the Classified Training Quality of English Majors Based on Support Vector Machine

Abstract

To attain higher teaching quality, adopting both quantitative and qualitative methods to discuss the mechanism of how active learning attitude influences the classified training quality of English majors is of practical meaning. However, few existing studies have concerned about this research topic, so to fill in this research blank, this paper aims to study the prediction, monitoring, and management of the classified training quality of English major students. At first, this paper analyzed the classified training of English majors, and introduced the factors of the cultivation of active learning attitude in English majors and the typical manifestations of English majors with an active learning attitude. Then, this paper used the comprehensive improvement level of each evaluation index to measure the classified training quality, and combined the Grey Wolf Optimizer (GWO) with the Support Vector Machine (SVM) to propose a model for predicting the classified training quality of English majors, which laid a basis for further quality monitoring and management. At last, the experimental results verified the effectiveness of using the constructed model to predict the classified training quality of English majors, and gave a few suggestions for monitoring and managing the classified training of English majors.