Analysis of Postgraduates’ Behavior and Learning Achievements based on Clustering Method

Abstract

With the rapid development of information technology, the application of big data in the education management has attracted more and more scholars’ attention. The widespread use of information recognition methods, especially the Ecards’ swiping technology provides an important support for the collection of students’ data. In this paper, the data of dormitory access, library access, breakfast consumption, published paper and course grades are combined to describe the characteristics of graduate students. Then academic graduate students are clustered into seven categories, from which data portraits for "straight A student" and "top researcher" are obtained. The colleges are divided into three categories according to the nature of their students’ paper, thus we can explore the differences of students’ behavior in different colleges. The research shows the prospect of machine learning in education management, and provides some inspiration to managers in this field.