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Big data analysis methodology for educational standards development based on intelligent processing models

https://doi.org/10.32517/0234-0453-2025-40-1-57-68

Abstract

The research presented in the article focuses on analyzing of the prospects for integrating big data into the process of developing national educational standards in Russia. The growing volume of educational data and progress in analytical technologies create new opportunities for modernizing approaches to the development and adaptation of educational standards. The aim of the research is to identify key directions and mechanisms for using big data to increase the effectiveness and relevance of educational standards. The research employs methods of statistical analysis, comparative institutional analysis, as well as expert interviews with representatives from the education sector and data specialists. The empirical basis consists of educational data arrays collected from 2018 to 2023. The research results demonstrate the significant potential of big data for adapting standards to labor market demands, personalizing educational trajectories, and improving the quality of the educational process. In particular, data analysis allows for a 25 % increase in the speed of standards development and a 20 % improvement in their compliance with international requirements. The practical significance of the research is related to the development of recommendations for implementing big data analytics in the processes of modernizing educational standards.

About the Authors

G. V. Palatkina
Astrakhan Tatishchev State University
Russian Federation

Galina V. Palatkina, Doctor of Sciences (Education), Professor, Dean of the Faculty of Pedagogy, Psychology, Hospitality and Sport 

Astrakhan 



N. O. Dubchenkova
Astrakhan Tatishchev State University
Russian Federation

Natalia O. Dubchenkova, Candidate of Sciences (Education), Associate Professor at the Department of Pedagogical Practices and Service Industries, Faculty of Pedagogy, Psychology 

Astrakhan 



V. V. Palatkin
Astrakhan Tatishchev State University
Russian Federation

Vladimir V. Palatkin, Candidate of Sciences (Culturology), Associate Professor at the Department of Pedagogical Practices and Service Industries, Faculty of Pedagogy, Psychology, Hospitality and Sport 

Astrakhan 



A. A. Podlipalin
Astrakhan Tatishchev State University
Russian Federation

Alexey A. Podlipalin, Candidate of Sciences (Education), Associate Professor at the Department of Pedagogical Practices and Service Industries, Faculty of Pedagogy, Psychology, Hospitality and Sport 

Astrakhan 



References

1. Shchelokova Yu.K. Using big data for analysis and optimization of educational process in higher education institutions. Education Management Review. 2024;(2-1(76)):114– 122. (In Russian.) EDN: DATNWK. DOI: 10.25726/w5010-9600-8416-x.

2. Yusupova A. S. Research and use of modern technologies for big data analysis in education. Trends in Science and Education Development. 2024;(109-13):166–169. (In Russian.) EDN: VPTWHU. DOI: 10.18411/trnio-05-2024-693.

3. Natapov M. L. Current trends in the development of big data based information systems for educational purposes. Vestnik of Russian New University. 2024;(1):109– 122. (In Russian.) EDN: OIBKTS. DOI: 10.18137/RNU. V9276.24.01.P.109.

4. Andreev S. V., Fazulianov D. V. Big data in education, opportunities and issues. Vektor Ehkonomiki. 2024;1(91):30. (In Russian.) EDN: QUATJO.

5. Ma S., Xie B. Research of the innovative path of education management in the age of big data. International Journal of Humanities and Natural Sciences. 2024;(5-3(92)):180– 183. (In Russian.) EDN: KGSDCJ. DOI: 10.24412/2500-1000-2024-5-3-180-183.

6. Ptitsyna L. K., Ptitsyn N. A. Quality-managed big data analysis in scientific and educational environments. Science. Informatization. Technologies. Education. Proc. 16th Int. Scientific and Practical Conf. Yekaterinburg, Russian State Professional Pedagogical University; 2023:161–166. (In Russian.) EDN: NQFDUS.

7. Boyarinov D.A. Big data in the context of information educational environment. Problems of Modern Education. 2023;(2):120–132. (In Russian.) EDN: EJCOVQ. DOI: 10.31862/2218-8711-2023-2-120-132.

8. Zhavoronkova Z. A. The use of big data in the organization of the educational process. Digital Content of Social and Ecosystem Development of the Economy. Proc. Int. Scientific and Practical Conf. Simferopol, V. I. Vernadsky Crimean Federal University; 2022:234–236. (In Russian.) EDN: RLWGUC.

9. Parsaeva D. S. Big data analysis technology in education. Young Researcher: From Idea to Project. Proc. 6th Student Scientific and Practical Conf. Yoshkar-Ola, Mari State University; 2022:513-514. (In Russian.) EDN: FOINJO.

10. Abdulaeva S. I., Namaeva M. M., Khaliyeva Kh. S. Big data in education: Current status, limitations and future research directions. Herald of GSTOU. Humanitarian, social and economic sciences. 2022;18(3(29)):65–70. (In Russian.) EDN: NGUUCM. DOI: 10.34708/GSTOU.2022.15.34.009.

11. Ilyassov D. K., Raikhanova G. A. The challenges in using economic big data analytics in higher education. Bulletin of the Karaganda University. Economy Series. 2022;2(106):41–47. DOI: 10.31489/2022ec2/41-47.

12. Shabardin A. M. Big data in the education system. Information Technologies in Science, Industry and Education. Proc. Scientific and Technical Conf. within the Russian Youth Scientific Forum “Communication of Students and Postgraduates in Scientific and Professional Spheres”. Izhevsk, Publishing House of the Information Resources Department of Izhevsk State Technical University named after M. T. Kalashnikov; 2021:62–69. (In Russian.) EDN: IGTPQJ.

13. Leskina E. I. Opportunities for the use of big data in the education sector. Juridical Education and Science. 2021;(9):11–14. (In Russian.) EDN: DGKWEN. DOI: 10.18572/1813-1190-2021-9-11-14.

14. Shunina L. A., Usova N. A. BigData-analysis of approaches to definition and application in education. Informatization of Education and E-learning Methods: Digital Technologies in Education. Proc. 4th Int. Scientific Conf. In two parts. Part 2. Krasnoyarsk, Siberian Federal University; 2020:266–269. (In Russian.) EDN: FOFWIY.

15. Fiofanova O. A. Analysis of the current state of research in the field of education management based on data. Values and Meanings/Tsennosti I Smysly. 2020;(1(65)):71–83. (In Russian.) EDN: TIYZXK. DOI: 10.24411/2071-6427-2020-10005.

16. Fiofanova O. A. Analysis of big data in education: Methodology and technologies. Moscow, Publishing House “Delo” RANEPA; 2020. 200 p. (In Russian.) EDN: SLBKRW.

17. Fiofanova O. A. Big-data management in education. Narodnoe obrazovanie. 2019;(4(1475)):135–142. (In Russian.) EDN: WNBLOW.

18. Fiofanova O. A. Organization of educational programs for training specialists in education management based on data (Big Data in Education). Professional’noe obrazovanie. Stolitsa. 2019;(9):27–34. (In Russian.) EDN: MRHDPC.

19. Bagutdinov R. A., Sargsan N. A., Krasnoplakhtych M. A. Analytics, tools and intellectual analysis of large different and differential data. Economics. Information Technjlogies. 2020;47(4):792–802. (In Russian.) EDN: MSHDFV. DOI: 10.18413/2687-0932-2020-47-4-792-802.

20. Ptitsyna L. K., Ptitsyn N. A. Quality-managed big data analysis in scientific and educational environments. Science. Informatization. Technologies. Education. Proc. 16th Int. Scientific and Practical Conf. Yekaterinburg, Russian State Vocational Pedagogical University; 2023:161–166. (In Russian.) EDN: NQFDUS.

21. Lin Lin, Zhou D., Wang J., Wang Yu. A systematic review of big data driven education evaluation. SAGE Open. 2024;14(2). DOI: 10.1177/21582440241242180.

22. Ridhani D., Krismadinata K., Novaliendry D., Ambiyar, Effendi H. Development of an intelligent learning evaluation system based on big data. Data and Metadata. 2024;3:1–14. DOI: 10.56294/dm2024.569.

23. Xu Bo. Artificial intelligence teaching system and data processing method based on big data. Complexity. 2021:(3):1–11. DOI: 10.1155/2021/9919401.

24. Caspari-Sadeghi S. Learning assessment in the age of big data: Learning analytics in higher education. Cogent Education. 2022;10(1):2162697. DOI: 10.1080/2331186X.2022.2162697.

25. Fahd K., Miah S. J. Designing and evaluating a big data analytics approach for predicting students' success factors. Research Square. 2022:1–15. DOI: 10.21203/rs.3.rs-2075479/v1.

26. Zaslavskaya O. Yu., Pobedinskaya T. V. Teacher in the age of digitalization: Key aspects of personalization of training and machine learning algorithms. Continuum. Matematika. Informatika. Obrazovaniye. 2024;(4(36)):80– 87. (In Russian.) EDN: BVGIJS. 10.24888/2500-1957-2024-4-80-87.


Review

For citations:


Palatkina G.V., Dubchenkova N.O., Palatkin V.V., Podlipalin A.A. Big data analysis methodology for educational standards development based on intelligent processing models. Informatics and education. 2025;40(1):57-68. (In Russ.) https://doi.org/10.32517/0234-0453-2025-40-1-57-68

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