Sergiy Londar
https://orcid.org/0000-0003-1838-288X
Lidia Londar
https://orcid.org/0000-0002-0873-5664
Olena Barabash
https://orcid.org/0000-0003-2545-0369
Ivan Gaiduk
https://orcid.org/0000-0003-3144-1469
A CONFIGURATIONAL APPROACH TO IDENTIFYING FACTORS INFLUENCING THE QUALITY OF MATHEMATICS EDUCATION IN THE REGIONS OF UKRAINE DURING WARTIME
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Language: English
Abstract. The aim of this article is to identify ways of ensuring the quality of mathematics education in Ukraine under wartime conditions using the method of fuzzy-set qualitative comparative analysis (fsQCA), drawing on educational and statistical data on regional differences. The study utilised the fsQCA-method, version 4.1, which is based on set theory and Boolean algebra and enables analysis of small samples within complex social systems. For the analysis, a set of six influencing factors was identified: the prestige of the teaching profession, the proportion of face-to-face teaching, tutoring, the human capital index of teachers, technical support for distance learning, and the safety index of the educational environment. The scientific novelty lies in the application of the fsQCA analytical framework to conduct a comprehensive qualitative configurational study of the factors influencing the quality of mathematics education in the regions of Ukraine under martial law, as well as in drawing conclusions for educational management on this basis. The resulting model demonstrated high reliability (consistency – 0.915, coverage – 0.392) and identified four specific configurations (pathways) for achieving high results in the National Multi-subject Test (NMT). An overall conclusion drawn from all the findings is the weak direct influence of the security factor, which may indicate the operation of compensatory mechanisms (the “survival effect”) in frontline regions. The configuration with the highest coverage (17.8 %) is one where the key factor is the availability of high-quality human capital of teachers, which compensates for distance learning, low security and poor technical support. The second significant pathway (14.1 %) demonstrates a substitution effect, where, given weaker human resources, success is ensured by high-quality technical infrastructure for e-learning.
Keywords: quality of mathematics education, martial law, fsQCA analysis, National Multi-Subject Test (NMT), teacher staffing, distance learning, educational management.
https://doi.org/10.32987/2617-8532-2026-3-19-35
Keywords: quality of mathematics education, martial law, fsQCA analysis, National Multi-Subject Test (NMT), teacher staffing, distance learning, educational management.
https://doi.org/10.32987/2617-8532-2026-3-19-35
References:
1. Vasylieva, D. (2022). State of mathematics remote teaching during the war in Ukraine. Ukrainian Educational Journal, 2, 38-47. DOI: https://doi.org/10.32405/2411-1317-2022-2-38-47 [in Ukrainian].
2. Khvorostina, Yu., & Yurchenko, A. (2025). SWOT analysis of mathematics education in Ukraine. Physical and Mathematical Education, 40(3), 44-50. DOI: https://doi.org/10.31110/fmo2025.v40i3-07 [in Ukrainian].
3. Vorotnykova, I. (2023). Professional Development оf Teachers for the Implementation of Quality Science and Mathematical Education in the Context of War in Ukraine. Educological Discourse, 4(43), 7-20. DOI: https://doi.org/10.28925/2312-5829.2023.41 [in Ukrainian].
4. Dinarte-Diaz, L., Gresham, J., Lemos, R., Patrinos, H., & Rodriguez-Ramirez, R. (2025). Investing in Human Capital During Wartime: Experimental Evidence from Ukraine. Policy Research Working Paper, 11196. Retrieved from http://hdl.handle.net/10986/43666.
5. Madrilejos, K. (2024). Predicting Academic Performance in Mathematics through Online Learning Constructs and Content Proficiencies. Journal of Interdisciplinary Perspectives, 2(5), 62-77. DOI: https://doi.org/10.69569/jip.2024.0065.
6. Medina, M. V. J., & Del Rosario, A. L. P. (2022). Online Collaborative Learning and the Enhancement of Most Essential Learning Competencies in General Mathematics among Grade Eleven Senior High School Students. International Journal of Scientific and Management Research, 5(3), 55-65. DOI: http://doi.org/10.37502/IJSMR.2022.5306.
7. Proshkin, V., & Foster, C. (2025). Challenges faced by Ukrainian students learning mathematics in UK schools. Cambridge Journal of Education, 55(1), 39-71. DOI: https://doi.org/10.1080/0305764X.2024.2444453.
8. Wang, X., & Madihie, A. (2026). A quasi-experimental study: cultivating mathematical resilience via intervention in Chinese classrooms. Frontiers in Psychology, 17, 1745726. DOI: https://doi.org/10.3389/fpsyg.2026.1745726.
9. Dougherty, C. (2016). Introduction to Econometrics. UK: Oxford University Press. 5th ed. Retrieved from https://global.oup.com/academic/product/introduction-to-econometrics-9780199676828?cc=ua&lang=en&.
10. Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2011). Choices and Uses of DEA Weights. In Handbook on Data Envelopment Analysis (pp. 93-126). Boston : Springer. DOI: https://doi.org/10.1007/978-1-4419-6151-8_4.
11. Xu, X., Tahir, S. H., Khan, K. B., Sajid, M. A., & Safdar, M. A. (2024). Beyond regression: Unpacking research of human complex systems with qualitative comparative analysis. Heliyon, 10(10), e31457. DOI: https://doi.org/10.1016/j.heliyon.2024.e31457.
12. Thiem, A. (2022). Qualitative Comparative Analysis (QCA). In Handbook of Research Methods in International Relations (pp. 607-628). Cheltenham: Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781839101014.00044.
13. Kumar, S., Sahoo, S., Lim, W. M., Kraus, S., & Bamel, U. (2022). Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview. Technological Forecasting and Social Change, 178, 121599. DOI: https://doi.org/10.1016/j.techfore.2022.121599.
14. Ragin, C. C., & Davey, S. (n. d.). fsQCA software (version 4.1). Retrieved from https://sites.socsci.uci.edu/~cragin/fsQCA/software.shtml.
15. SSI “Institute of Educational Analytics”. (2023). Analysis of the use of educational grant funds for the remuneration of teaching staff in educational institutions in 2022. Retrieved from https://iea.gov.ua/wp-content/uploads/2023/06/az_osvitnya-subvencziya_za-2022.pdf [in Ukrainian].
16. Ministry of Education and Science of Ukraine. (n. d.). Educational indicators: A national perspective. Retrieved from https://tinyurl.com/bdk64zk5 [in Ukrainian].
17. SSI “Institute of Educational Analytics”. (n. d.). Data on the number and structure of teaching staff of the general secondary education institutions of the Ministry of Education and Science of Ukraine, other Ministries, authorities and private institutions (2022/2023 academic years). Retrieved from https://iea.gov.ua/wp-content/uploads/2023/05/byuleten-83-rvk-2022_2023.xlsx [in Ukrainian].
18. SSI “Institute of Educational Analytics”. (n. d.). Data on facilities and modern information technologies usage in general secondary education institutions (without special general secondary education institutions) of the Ministry of Education and Science of Ukraine, other Ministries and authorities, as well as private institutions (2022/2023 academic years). Retrieved from https://iea.gov.ua/wp-content/uploads/2023/03/byuleten_d_4_2022_2023.xlsx [in Ukrainian].
19. Ukrainian Center for Educational Quality Assessment (UCEQA). (n. d.). Report on the National Multidisciplinary Test in 2022. Retrieved from https://zno.testportal.com.ua/opendata [in Ukrainian].
2. Khvorostina, Yu., & Yurchenko, A. (2025). SWOT analysis of mathematics education in Ukraine. Physical and Mathematical Education, 40(3), 44-50. DOI: https://doi.org/10.31110/fmo2025.v40i3-07 [in Ukrainian].
3. Vorotnykova, I. (2023). Professional Development оf Teachers for the Implementation of Quality Science and Mathematical Education in the Context of War in Ukraine. Educological Discourse, 4(43), 7-20. DOI: https://doi.org/10.28925/2312-5829.2023.41 [in Ukrainian].
4. Dinarte-Diaz, L., Gresham, J., Lemos, R., Patrinos, H., & Rodriguez-Ramirez, R. (2025). Investing in Human Capital During Wartime: Experimental Evidence from Ukraine. Policy Research Working Paper, 11196. Retrieved from http://hdl.handle.net/10986/43666.
5. Madrilejos, K. (2024). Predicting Academic Performance in Mathematics through Online Learning Constructs and Content Proficiencies. Journal of Interdisciplinary Perspectives, 2(5), 62-77. DOI: https://doi.org/10.69569/jip.2024.0065.
6. Medina, M. V. J., & Del Rosario, A. L. P. (2022). Online Collaborative Learning and the Enhancement of Most Essential Learning Competencies in General Mathematics among Grade Eleven Senior High School Students. International Journal of Scientific and Management Research, 5(3), 55-65. DOI: http://doi.org/10.37502/IJSMR.2022.5306.
7. Proshkin, V., & Foster, C. (2025). Challenges faced by Ukrainian students learning mathematics in UK schools. Cambridge Journal of Education, 55(1), 39-71. DOI: https://doi.org/10.1080/0305764X.2024.2444453.
8. Wang, X., & Madihie, A. (2026). A quasi-experimental study: cultivating mathematical resilience via intervention in Chinese classrooms. Frontiers in Psychology, 17, 1745726. DOI: https://doi.org/10.3389/fpsyg.2026.1745726.
9. Dougherty, C. (2016). Introduction to Econometrics. UK: Oxford University Press. 5th ed. Retrieved from https://global.oup.com/academic/product/introduction-to-econometrics-9780199676828?cc=ua&lang=en&.
10. Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2011). Choices and Uses of DEA Weights. In Handbook on Data Envelopment Analysis (pp. 93-126). Boston : Springer. DOI: https://doi.org/10.1007/978-1-4419-6151-8_4.
11. Xu, X., Tahir, S. H., Khan, K. B., Sajid, M. A., & Safdar, M. A. (2024). Beyond regression: Unpacking research of human complex systems with qualitative comparative analysis. Heliyon, 10(10), e31457. DOI: https://doi.org/10.1016/j.heliyon.2024.e31457.
12. Thiem, A. (2022). Qualitative Comparative Analysis (QCA). In Handbook of Research Methods in International Relations (pp. 607-628). Cheltenham: Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781839101014.00044.
13. Kumar, S., Sahoo, S., Lim, W. M., Kraus, S., & Bamel, U. (2022). Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview. Technological Forecasting and Social Change, 178, 121599. DOI: https://doi.org/10.1016/j.techfore.2022.121599.
14. Ragin, C. C., & Davey, S. (n. d.). fsQCA software (version 4.1). Retrieved from https://sites.socsci.uci.edu/~cragin/fsQCA/software.shtml.
15. SSI “Institute of Educational Analytics”. (2023). Analysis of the use of educational grant funds for the remuneration of teaching staff in educational institutions in 2022. Retrieved from https://iea.gov.ua/wp-content/uploads/2023/06/az_osvitnya-subvencziya_za-2022.pdf [in Ukrainian].
16. Ministry of Education and Science of Ukraine. (n. d.). Educational indicators: A national perspective. Retrieved from https://tinyurl.com/bdk64zk5 [in Ukrainian].
17. SSI “Institute of Educational Analytics”. (n. d.). Data on the number and structure of teaching staff of the general secondary education institutions of the Ministry of Education and Science of Ukraine, other Ministries, authorities and private institutions (2022/2023 academic years). Retrieved from https://iea.gov.ua/wp-content/uploads/2023/05/byuleten-83-rvk-2022_2023.xlsx [in Ukrainian].
18. SSI “Institute of Educational Analytics”. (n. d.). Data on facilities and modern information technologies usage in general secondary education institutions (without special general secondary education institutions) of the Ministry of Education and Science of Ukraine, other Ministries and authorities, as well as private institutions (2022/2023 academic years). Retrieved from https://iea.gov.ua/wp-content/uploads/2023/03/byuleten_d_4_2022_2023.xlsx [in Ukrainian].
19. Ukrainian Center for Educational Quality Assessment (UCEQA). (n. d.). Report on the National Multidisciplinary Test in 2022. Retrieved from https://zno.testportal.com.ua/opendata [in Ukrainian].
Received: [15.04.2026]
Reviewed: [01.06.2026]
Accepted: [22.06.2026]
Published: [31.08.2026]
