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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. 

About the Authors

A. G. Leonov
Scientific Research Institute for Systems Analysis of the National Research Centre "Kurchatov Institute"; Lomonosov Moscow State University; Moscow Pedagogical State University; State University of Management
Russian 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
Scientific Research Institute for Systems Analysis of the National Research Centre "Kurchatov Institute"; Lomonosov Moscow State University; State University of Management
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
Scientific Research Institute for Systems Analysis of the National Research Centre "Kurchatov Institute"
Russian Federation

Nikolay S. Martynov, Junior Researcher at the Department
of Educational Informatics



A. V. Shlyakhov
Scientific Research Institute for Systems Analysis of the National Research Centre "Kurchatov Institute"
Russian Federation

Artem V. Shlyakhov, Junior Researcher at the Department
of Educational Informatics



T. G. Khan
Scientific Research Institute for Systems Analysis of the National Research Centre "Kurchatov Institute"
Russian Federation

Tatyana G. Khan, a postgraduate student



References

1. Moltudal S. H., Krumsvik R. J., Høydal K. L. Adaptive learning technology in primary education: Implications for professional teacher knowledge and classroom management. Frontiers in Education. 2022;7:1–18. EDN: OVOKQG. DOI: 10.3389/feduc.2022.830536.

2. ToshevaS., MartinovskaC. Adaptive e-learning system in secondary education. International Journal of Emerging Technologies in Learning (iJET). 2012;7(S1):36–41. DOI: 10.3991/ijet.v7iS1.1913.

3. Muñoz J. L., Ojeda F. M., Jurado D. L., Peña P. F., Carranza C. P., Berríos H. Q., Molina S. U., Farfan A. R., Arias-Gonzáles J. L., Vasquez-Pauca M. J. Systematic review of adaptive learning technology for learning in higher education. Eurasian Journal of Educational Research. 2022;98:221–233. DOI: 10.14689/ejer.2022.98.014.

4. Oh I.-S., Schmidt F. L., Shaffer J. A., Le H. The Graduate Management Admission Test (GMAT) is even more valid than we thought: A new development in meta-analysis and its implications for the validity of the GMAT. Academy of Management Learning & Education. 2008;7(4):567–569. DOI: 10.5465/AMLE.2008.35882196.

5. Cosyn E., UzunH. B., DobleC., MatayoshiJ. A practical perspective on knowledge space theory: ALEKS and its data. Journal of Mathematical Psychology. 2021;101:102512. EDN: FYNMIL. DOI: 10.13140/RG.2.2.20082.53448.

6. Conklin T. A. Knewton (An adaptive learning platform available at https://www.knewton.com/). Academy of Management Learning & Education. 2016;15(3):635–647. DOI: 10.5465/amle.2016.0206.

7. Carbonell J. R. AI in CAI: An artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems. 1970;11(4):190–202. DOI: 10.1109/TMMS.1970.299942.

8. Gjermeni F., Prodani F. AI and student engagement: A comparative analysis. Interdisciplinary Journal of Research and Development. 2024;11(3):194–208. DOI: 10.56345/ijrdv11n326.

9. Shahiri A. M., Husain W., Rashid N. A. A review on predicting student’s performance using data mining techniques. Procedia Computer Science. 2015;72:414–422. DOI: 10.1016/j.procs.2015.12.157.

10. Romero C., Ventura S. Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics. 2010;40(6):601–618. DOI: 10.1109/TSMCC.2010.2053532.

11. Almazroei E., Alsharif E. F. Automated learning style classification using autoencoders and Felder—Silverman model from students’ digital footprints.Machine Intelligent Research. 2024;18(02):1–23.

12. GorbatovS.V., KrasnovaE.A.Adigitalfootprintasamechanism of individualizinga student’s educational trajectory (on the case of the “Digital technologies of self-education” course). Perspectives of Science and Education. 2022;58(4):193–208. (In Russian.) DOI: 10.32744/pse.2022.4.12.

13. Zimina D. V., Muromtsev D. I. Automation of developing adaptive e-learningusingblockchaintechnology.Vestnik of Astrakhan State Technical University. Series: Management, Computer Science and Informatics.2022;(2):119–128.(InRussian.) EDN:WVSUAE. DOI: 10.24143/2072-9502-2022-2-119-128.

14. Bagdasaryan L. Sh., Ardeev A. H., Kulikova T. A., Poddubnaya N. A. Application of a digital footprint of educationalintelligent technologies. Newsletter of North-Caucasus Federal University. 2024;(2):150–157. (In Russian.) EDN: PXGKJF. DOI: 10.37493/2307-907X.2024.2.18.

15. Azcona D., Hsiao I.-H., Smeaton A. F. Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction. 2019;29(2):759–788. EDN: HNAIFM. DOI: 10.1007/s11257-019-09234-7.

16. Chen K.-T. Integration of paths, geometric invariants and a generalized Baker—Hausdorff formula. Annals of Mathematics. 1957;65(1):163–178. DOI: 10.2307/1969671.

17. Chen K.-T. Integration of paths — a faithful representation of paths by non-commutative formal power series. Transactions of the American Mathematical Society. 1958;89:395–407. DOI: 10.1090/S0002-9947-1958-0106258-0.

18. Youngs R., Fisher E. Learning from the past and predicting the future. The Journal of Laryngology & Otology. 2015;129(6):519. DOI: 10.1017/S0022215115001383.

19. Vasilyev I. A., Karavaeva A. S., Leonov A. G., Mashchenko K. A., Shlyakhov A.V. About innovations in the digital educational platform of Mirera. Proceedings of the Research Institute for System Research of the Russian Academy of Sciences. 2023;13(4):127–134.(InRussian.)EDN:MPQKZN. DOI: 10.25682/NIISI.2023.4.0018.

20. Leonov A. G., Martynov N.S., Mashchenko K. A., Kholkina A. A., Shlyakhov A. V. Automation of semantic analysis for textual responses of students in a digital educational platform.Software & Systems. 2024;37(3):440–452.(In Russian.) EDN: CTTAJN. DOI: 10.15827/0236-235X.142.440-452.


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

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ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)