Analysis of the Digital Footprint in the Communities of an Educational Organization in Social Networks in the Context of Digital Transformation of the Educational Environment

The purpose of the study. The digitalization of education implies large-scale transformations encompassing the implementation of digital technologies at every level of general and vocational education, as well as additional education, as well as changes in the interactions of all participants in the educational process. One of the six key challenges addressed by the Education Development Strategy to 2036 is the rapid spread of digital technologies and artificial intelligence. The aim of this study is to determine the role of digital traces generated during the educational process as a tool for digital transformation in educational management, as well as to demonstrate the practical implementation of digital trace processing using the VKontakte social network API and data analysis methods (Educational Data Mining).

Materials and Methods. The theoretical and methodological basis for the study was formed by the work of Russian and international researchers in the field of digital data analysis arising during the educational process. The study utilized data analysis methods, natural language processing techniques, and Python libraries such as pandas, numpy, mathplotlib, and others. The empirical portion of the study is based on the analysis of the digital footprint of the educational organization's communities on the social network VKontakte, represented as unstructured text.

Results. Research shows that large volumes of heterogeneous digital trace data, including those presented in the form of semi-structured data, inevitably arise in the context of the digitalization of education and ensuring the information openness of educational organizations. This data is of interest for educational analytics used to address issues related to the digital transformation of the educational process, the digital transformation of educational management, and the continuity and integration of educational levels. The digital trace generated through interactions with the electronic information and educational environment and other digital resources of educational organizations on the internet (websites, social media pages, and instant messaging apps) opens up opportunities for analyzing data on the educational process and participants in educational relationships. However, systematic approaches to its analysis and use in the context of the digital transformation of education are required, including those that take into account legal requirements for personal data, ethical aspects, and security aspects. This article examines the prospects for analyzing digital data in educational organization communities on social media using data analysis and machine learning methods and presents a practical example of data analysis in such communities on the social network VKontakte using an API.

Conclusion. The obtained results can be used both for initial studies of digital footprint analysis and as a basis for developing a system for generating educational analytics. Practical application of the results will facilitate the digital transformation of educational management.

1. Lukianova O. V. Pedagogicheskoye vzaimodeystviye v tsifrovoy srede: monografiya. M.: MPGU;2019:168 s.

2. Demkin V. P.. Rudenko T. V. Distantsionnyye tekhnologii v obrazovanii. Tomsk: TGU;2015:156 s.

3. Amiraliev A.D., Magomedova P.I., Ozdamirova E.M. How social media platforms can be used for interaction between students and teachers. Humanities, social-economic and social sciences. 2025;(6):13-18. DOI: 10.24412/2220-2404-2025-6-40.

4. Panyukova S.V. Formation of a Student’s Digital Profile for Presentation in the Labor Market. International conference on innovative approaches to the application of digital technologies in education and research. 2025;1222:529-537. DOI: 10.1007/978-3-031-78776-8_54.

5. Ng E.Y.L., Law N., Yuen A.H.K. Understanding learner lives through digital footprints, in: Proceedings of the Technology, Mind, and Society conference (TECHMINDSOCIETY18), ed. A.L. Story. New York, ACM, 2018, Article 26. DOI: 10.1145/3183654.3183708

6. Lekhov K.A., Speranskiy D.D., Mitrokhin M.A., Karamysheva N.S. System for extracting and analyzing text data from social networks for an educational institution. Modeli, sistemy, seti v ekonomike, tekhnike, prirode i obshchestve = Models, systems, networks in economics, technology, nature and society. 2021;1(37):128–136. DOI:10.21685/2227-8486-2021-1-11.

7. Shamsutdinova T.M. Digital footprint as a source of big data in education. Open Education. 2024;28(6):13-21. https://doi.org/10.21686/10.21686/1818-4243-2024-6-13-21.

8. Krylova M.A. The study of students' motivational involvement in educational activities based on a digital footprint. Vestnik Tverskogo gosudarstvennogo universiteta. Seriya: Pedagogika i psikhologiya. 2021;4(57):77-86. DOI: 10.26456/vtpsyped/2021.4.077.

9. Dyachenko M.S., Leonov A.G. Digital footprint in education as a driver of professional growth in the digital age. E-Management. 2022;5(4):23-30. DOI: 10.26425/2658-3445-2022-5-4-23-30.

10. Vilkova K. A.. Lebedev D. V. Adaptivnoye obucheniye v vysshem obrazovanii: za i protiv. M.: Izd-vo NIU VShE;2020:36 s.

11. Krishtal M.M., Bogdanova A.V., Myagkov M.G., Alexandrova YU.K.. Digital footprint: assessing student satisfaction with education quality. Vysshee Obrazovanie v Rossii. 2024;33(2):89-108. DOI: 10.31992/0869-3617-2024-33-2-89-108.

12. Lakhno V. Kurbaiyazov N., Lakhno M., Kryvoruchko O., Desiatko A., Tsiutsiura S., Tsiutsiura M. Analysis of digital footprints associated with cybersecurity behavior patterns of users of University Information and Education Systems. International journal of electronics and telecommunications. 2024;70(3):673-682. DOI: 10.24425/ijet.2024.149596.

13. Gasanov I.Z., Lixakov M.V. Efficient work with community data on the example of the VKontakte API. Innovation & Investment. 2023;(6):144-146.

14. Ostanina E.A., Pokolodina E.V. Assessment of the Digital Footprint of a Participant in Internet Interactions Using Mathematical Statistics Methods. Open Education. 2025;29(4):64-74. DOI: 10.21686/1818-4243-2025-4-64-74.

15. Kalinichenko A. V. Student's digital footprint in an electronic educational environment: objectives and tools of analysis. V sbornike: Aktualnyye problemy pedagogiki. sbornik nauchnykh trudov. 2025:68-72.

16. Mantulenko V.V. Prospects for digital footprint usage in the higher education. Prepodavatel XXI vek. 2020;(3-1):32-42. DOI: 10.31862//2073-9613-2020-3-32-42.

17. Standart organizatsii. Standart tsifrovogo sleda 1.0.3 [Elektronnyy resurs]. URL: https://standard.2035.university/ (data obrashcheniya: 02.10.2025).

18. Vayndorf-Sysoeva M.E., Pchelyakova V.V. Prospects for Using the Digital Footprint in Educational and Scientific Processes. Vestnik of Minin University. 2021;9(3(36)):1-14. DOI: 10.26795/2307-1281-2021-9-3-1.

19. Kalinichenko A. V. А dialogue technique for automation semantically similar doc-uments search. The Bulletin of Voronezh State Technical University. 2012;8(8):15-17.

20. Kalinichenko A. V. Podkhod k poisku semanticheski pokhozhikh dokumentov. Informatsionnyye tekhnologii modelirovaniya i upravleniya. 2012;1(73):60-65.

21. Gerardo Bárcena Ruiz, Richard De Jesús Gil Herrera. Textual emotion detection with complementary BERT transformers in a Condorcet’s Jury theorem assembly. Knowledge-Based Systems. 2025; 326:1-17. DOI: 10.1016/j.knosys.2025.114070.