Cluster Analysis of Behavioral Factors in the Formation of Students' Digital Identity

With the development of technology and the transition to online learning, ensuring the security of student data and their authentication in a digital environment are becoming important tasks for educational institutions. The purpose of the study is to use cluster analysis to analyze survey data in order to identify the features of students' digital identity. The general methodology of the study is presented, according to which preliminary data processing was carried out in order to prepare them for subsequent analysis. The paper uses hierarchical clustering using Ward's algorithm to analyze behavioral factors affecting the formation of students' digital identity. The study used survey data collected among 324 students from three regional universities of the Republic of Kazakhstan. The content of the questions includes digital activity and interaction of students in the online space, as well as questions about their academic, research and social activities. The Cronbach's α coefficient was used to assess the reliability and reliability of the data. As a result of the study, groups of students with different levels of digital identity ("Intensive digital activity", "Hybrid form of digital activity", "Limited digital activity").