Author(s)

Lingli Liu

Corresponding Author

Lingli Liu

ABSTRACT

This article aims to explore the application of big data analysis in identifying students' behavior patterns in art universities and evaluate its potential impact on educational practice. The introduction first points out the diversity and complexity of students' behavior patterns in art universities and the limitations of traditional teaching methods in meeting students' individual needs. Therefore, this study proposes to use big data analysis technology to deeply understand students' behavior in order to optimize teaching plans and improve teaching efficiency. Through big data analysis tools such as cluster analysis and time series analysis, the behavior data of art university students can be comprehensively analyzed; At the same time, we can identify typical student behavior patterns and analyze the characteristics of students in these patterns. Therefore, big data analysis can accurately reveal students' behavior patterns and provide educators with valuable insight into students' behavior. Based on these findings, this article puts forward strategies to optimize teaching plans, promote students' personalized development and support social and mental health. It not only provides a scientific basis for the teaching management of art universities, but also provides a useful reference for the application of big data in other educational fields.

KEYWORDS

Big data analysis; Art university students; Behavior pattern; Optimization of teaching plan; Personalized development