A Study on Optimizing Student Stratification Management in English Teaching Based on K-mean Clustering Algorithm

The continuous development of digital informatization has opened the era of intelligent education in the field of education.Higher education has accumulated a huge amount of data, but it is not fully utilized, and in-depth mining and analysis of these data can reveal the students' learning and life status and provide powerful support for teaching management.Therefore, the research of using clustering algorithm to build a hierarchical management model for English teaching is very necessary.Clustering algorithm provides an effective way for the analysis of students' learning behavior, and for the research needs of English teaching, this paper proposes a multi-factor improved K-means clustering algorithm and compares and verifies its clustering effect.For the problem of stratified division of student groups, firstly, the clustering index system of students' book borrowing behavior and English course learning behavior constructed is used.Then, the improved K-Means clustering algorithm is used to cluster and mine the data of each student's behavior to discover the student groups under different behaviors, so as to realize the hierarchical clustering of students in hierarchical management.Finally, for English teaching, a student stratification management model is established from three aspects: student stratification, teaching goal stratification and teaching process stratification, which provides important decision support for student stratification determination in English teaching and provides a more rationalized management model for student management workers.