Analytics and neural network generation of digital footprint for constructing personalised learning trajectories
https://doi.org/10.32517/0234-0453-2025-40-4-6-17
Abstract
In a teacher’s work, a large amount of data accumulates about students taking courses. The data must be processed to improve materials and test pedagogical hypotheses. But with an increase in the amount of information, this becomes almost impossible. The article discusses approaches to the analysis of students’ digital footprint and its neural network generation for constructing intelligent personalized learning trajectories. Existing approaches to adaptive learning and propose a new methodology based on time series analysis and machine learning are investigated. Application results of digital learning platform Mirera for collecting and processing student activity data and the ability to predict students’ academic success based on their digital footprint are presented. A method for categorizing learners based on their learning characteristics and behavior data collected during training is proposed. Synthetic data generation is used to improve the accuracy of prediction models and build long-term predictions. The conducted computational experiment confirms the effectiveness of the proposed approach compared to traditional methods of educational data analysis. The results obtained can be used to automate adaptive learning and improve the quality of the educational process.
Keywords
About the Authors
A. G. LeonovRussian Federation
Alexander G. Leonov, Doctor of Sciences (Education), Candidate of Sciences (Physics and Mathematics), Docent, Head of the Department of Continuing Professional Education; Leading
Researcher at the Laboratory of Computational Methods, The Department ofComputationalMathematics, BranchofMathematics, Faculty of Mechanics and Mathematics; Professor at the Department of Mathematics and Informatics in Elementary School, Faculty of Elementary Education, Institute of Childhood; Master’s Program Supervisor of the “Digital Technologies in Public and Corporate Governance”, Associate Professor at the Department of Mathematical Methods in Economics and Management, Institute of Information Systems
K. A. Mashchenko
Russian Federation
Kirill A. Mashchenko, Junior Researcher at the Department
of Educational Informatics; tional Research Centre “Kurchatov
Institute”, Moscow, Russia; Junior Researcher at the Laboratory of Computational Methods, The Department of Computational Mathematics, Branch of Mathematics, Faculty of Mechanics and Mathematics; Master’s Program Instructor of the “Digital Technologies in Public and Corporate Governance”, The Department of
Mathematical Methods in Economics and Management, Institute of Information Systems
N. S. Martynov
Russian Federation
Nikolay S. Martynov, Junior Researcher at the Department
of Educational Informatics
A. V. Shlyakhov
Russian Federation
Artem V. Shlyakhov, Junior Researcher at the Department
of Educational Informatics
T. G. Khan
Russian Federation
Tatyana G. Khan, a postgraduate student
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Review
For citations:
Leonov A.G., Mashchenko K.A., Martynov N.S., Shlyakhov A.V., Khan T.G. Analytics and neural network generation of digital footprint for constructing personalised learning trajectories. Informatics and education. 2025;40(4):6-17. (In Russ.) https://doi.org/10.32517/0234-0453-2025-40-4-6-17























