Identifying Student Behavior Patterns Based on LMS Data

Abstract

In contemporary education, data analysis methods to establish patterns of student behavior for subsequent optimization of the educational process are becoming increasingly relevant. This paper presents research aimed at identifying patterns of student behavior based on the processing of data obtained from Learning Management Systems (LMS). The article examines data collection and analysis issues from LMS, including activity logs, grades, participation in forums, and other interactive elements. Additionally, methods such as statistical analysis and machine learning, applied to identify patterns of student behavior, are discussed. The text describes the identified patterns of student behavior in the electronic educational environment, which are subsequently linked to students' academic performance levels. The paper's concluding section presents the research findings and potential scenarios for their application. Key Words: adaptive learning, machine learning, learning management systems, student behavior patterns.