Formation of Models for Registering Systemic Processes in The Digital Educational Environment of the University Based on Log File Analysis


  • Valerii Lakhno National University of Life and Environmental Sciences of Ukraine
  • Bakhytzhan Akhmetov Abai Kazakh National Pedagogical University
  • Kaiyrbek Makulov Caspian University of Technology and Engineering named after Sh.Yesenova
  • Bauyrzhan Tynymbayev Caspian University of Technology and Engineering named after Sh.Yesenova
  • Svitlana Tsiutsiura State University of Trade and Economics
  • Mikola Tsiutsiura State University of Trade and Economics
  • Vitaliy Chubaievskyi State University of Trade and Economics


It has been demonstrated that technologies and methods of intelligent data analysis (IDA) in the educational domain, particularly based on the analysis of digital traces (DT) of students, offer substantial opportunities for analyzing student activities. Notably, the DT of students are generated both during remote learning sessions and during blended learning modes. By applying IDA methods to DT, one can obtain information that is beneficial for both the educator in a specific discipline and for the educational institution's management. Such information might pertain to various aspects of the functioning of the digital educational environment (DEE) of the institution, such as: the student's learning style; individual preferences; the amount of time dedicated to a specific task, among others. An algorithm has been proposed for constructing a process model in the DEE based on log analysis within the DEE. This algorithm facilitates the description of a specific process in the DEE as a hierarchy of foundational process elements. Additionally, a model based on cluster analysis methods has been proposed, which may prove beneficial for analyzing the registration logs of systemic processes within the university's DEE. Such an analysis can potentially aid in detecting anomalous behavior of students and other individuals within the university's DEE. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.



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