Clustering Study of Student Groups Based on Analysis of Online Learning Behavior

Abstract

Online education platform's network learning can make effective integration of education and information technology, and has a great impact on students' learning style and mode. The students' learning characteristics can be represented as operable and understandable data indicators through their behavior data. By recording and analyzing the data of students' learning behavior, we can understand the learning status of different students and improve their learning effect. This paper first obtains the observation indicators with typical significance as the original data set, then through correlation analysis to extract six indicators that can objectively reflect learning characteristics. In this paper, cluster analysis of these indicators is carried out based on students' performance to illustrate that students' online learning has the characteristics of group. This paper focuses on the characteristics of learning behavior of different groups, and gives corresponding suggestions from the perspective of teaching management.