CLUSTER ANALYSIS FOR RESEARCHING DIGITAL FOOTPRINTS OF STUDENTS IN EDUCATIONAL INSTITUTIONS

Abstract

It is shown that Cluster Analysis (CA) can be used in the process of researching the Digital Traces (DT) of students of an educational institution, as well as other educational institutions that introduce a Digital Educational Environment (DEE) into the educational process. Cluster analysis can reveal behavioral patterns of education seekers. Also, the use of CA methods will improve the personalization of training and increase the effectiveness of educational programs. It is shown that in the context of ensuring Information Security (IS) of the DEE of educational institutions, technologies and methods of DT analysis can also be useful, for example, for: monitoring students’ network activity; analysis of student authorization and authentication logs; detection of malicious programs and attacks on the DEE; analysis of IS threats to the DEE as a whole; vulnerability prediction. It is shown that the application of CA methods can be useful in studying the degree of information security of the DEE of universities and other educational institutions. It has been established that CA methods can help identify groups of students with similar patterns of activity from the point of view of IS, both the DEE of the educational institution as a whole, and its computer networks and systems. It has been established that with the help of CA DT, it is possible to detect anomalous behavior of students, to detect unusual patterns of activity, facts of unauthorized use of resources or other deviations from the typical behavior of students in the network of the educational institution. The article also provides the results of experimental studies of the level of competences of students of various specialties at the university in IS and protection of information assets of the DEE. In this, CA methods were used in the process of studying students’ DT. Six types of users were distinguished on the basis of CA DT of different groups of students registered in the university DEE. As a result of the application of CA methods, students registered in the university’s DEE were divided into appropriate clusters according to criteria affecting IS risks.