Navigating Mathematical Landscapes: Investigating the Dynamics of Learning Environments and Attitudinal Influence on Mathematics Education

This qualitative study explored students’ attitudes toward learning mathematics within dynamic and disrupted educational environments, particularly those shaped by instructional shifts, technology integration, and crisis-related interruptions. Conducted at Agusan del Sur University (ADSSU) during the Academic Year 2024–2025, the study examined how students experienced mathematics learning across face-to-face, online, and blended modalities. The participants included nine students who joined in-depth interviews (IDIs) and fifteen students who participated in focus group discussions (FGDs), enabling the study to capture both individual experiences and shared perspectives. Data were analyzed using Braun and Clarke’s six-phase thematic analysis, which guided the process of familiarization, coding, theme development, review, definition, and reporting. The interpretation of the findings was anchored on the Affective-Behavioral-Cognitive (ABC) model of attitudes, allowing the study to examine students’ emotional responses, learning behaviors, and cognitive perceptions toward mathematics. The findings revealed that students’ attitudes toward mathematics were strongly influenced by changes in the learning environment. The shift from face-to-face to online learning during the COVID-19 pandemic, along with recurring floods and other disruptions, affected students’ motivation, confidence, focus, and persistence. Face-to-face instruction was often associated with clearer explanations, immediate feedback, and stronger emotional support, while online learning was linked to comprehension difficulties, reduced interaction, unstable connectivity, and increased dependence on digital tools. Overall, the study suggests that students’ attitudes toward mathematics are not fixed but are shaped by learning conditions. Their emotional responses, behavioral engagement, and cognitive perceptions continuously interact with instructional delivery, teacher support, peer influence, technology use, and crisis experiences.

students’ attitudes, learning mathematics, ABC model, blended learning

1. Acido, J.V., & Caballes, D.G. (2024). Assessing educational progress: A comparative analysis of PISA results (2018 vs. 2022) and HDI correlation in the Philippines. World Journal of Advanced Research and Reviews, 21(1), 462–474. https://doi.org/10.30574/wjarr.2024.21.1.0020 [Google Scholar] [Crossref]

2. Ahmed, S. K. (2024). The pillars of trustworthiness in qualitative research. Journal of Medicine, Surgery, and Public Health, 2, 100051. https://doi.org/10.1016/j.glmedi.2024.100051 [Google Scholar] [Crossref]

3. Alabdulaziz, M. S., & Tayfour, E. A. (2023). A comparative study of the effects of distance learning and face-to-face learning during the COVID-19 pandemic on learning mathematical concepts in primary students of the Kingdom of Bahrain. Education Sciences, 13(2), 133. https://doi.org/10.3390/educsci13020133 [Google Scholar] [Crossref]

4. Alshaboul, Y. M., Alazaizeh, M. A., Sellami, A. L., Abu-Tineh, A. M., Ghamrawi, N., & Shal, T. (2024). The perceived challenges to online learning during the COVID-19 pandemic: A nationwide study of K-12 parental perspectives (Arab and other parents) in Qatar. Heliyon, 10(7), e28578. https://doi.org/10.1016/j.heliyon.2024.e28578 [Google Scholar] [Crossref]

5. Bernardo, A.B.I., Cordel, M.O., II, Lapinid, M.R.C., Teves, J.M.M., Yap, S.A., & Chua, U.C. (2022). Contrasting profiles of low-performing mathematics students in public and private schools in the Philippines: Insights from machine learning. Journal of Intelligence, 10(3), 61. https://doi.org/10.3390/jintelligence10030061 [Google Scholar] [Crossref]

6. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

7. Breckler, S. J. (1984). Empirical validation of affect, behavior, and cognition as distinct components of attitude. Journal of Personality and Social Psychology, 47(6), 1191–1205. https://doi.org/10.1037/0022-3514.47.6.1191 [Google Scholar] [Crossref]

8. Broda, M. D., Ross, E., Sorhagen, N., & Ekholm, E. (2023). Exploring control-value motivational profiles of mathematics anxiety, self-concept, and interest in adolescents. Frontiers in Psychology, 14, 1140924. https://doi.org/10.3389/fpsyg.2023.1140924 [Google Scholar] [Crossref]

9. Cañeda, M.E., Mata, L.P., & Bedrijo, R.W. (2024). Investigating Students’ Lived Experiences in Mathematics Classroom Activities. International Journal of Research and Innovation in Social Science, 8(11), 1508-1525. https://doi.org/10.47772/IJRISS.2024.8110122 [Google Scholar] [Crossref]

10. Cevikbas, M., & Kaiser, G. (2022). Student engagement in a flipped secondary mathematics classroom. International Journal of Science and Mathematics Education, 20, 1455–1480. https://doi.org/10.1007/s10763-021-10213-x [Google Scholar] [Crossref]

11. Chiu, T.K.F. (2022). Applying the self-determination theory to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(S1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998 [Google Scholar] [Crossref]

12. Creswell, J. W., & Creswell, J.D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (Sixth Edition). Sage Publications, Inc. [Google Scholar] [Crossref]

13. Daker, R. J., Gattas, S. U., Sokolowski, H. M., Green, A. E., & Lyons, I. M. (2021). First-year students’ math anxiety predicts STEM avoidance and underperformance throughout university, independently of math ability. npj Science of Learning, 6(17). https://doi.org/10.1038/s41539-021-00095-7 [Google Scholar] [Crossref]

14. Dweck, C. S. (2006). Mindset: The new psychology of success. Random House. [Google Scholar] [Crossref]

15. Eidlin-Levy, H., Avraham, E., Fares, L., & Rubinsten, O. (2023). Math anxiety affects career choices during development. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00441-8 [Google Scholar] [Crossref]

16. Hakim, A. R., Dewi, M. L., Zamrudy, W., Dewajani, H., & Ro’isatin, A. (2023). How student beliefs related to mathematics are influenced by students’ past experiences. Technium Social Sciences Journal, 43, 97–101. https://doi.org/10.47577/tssj.v43i1.8738 [Google Scholar] [Crossref]

17. Hanaysha, J. R., Shriedeh, F. B., & In’airat, M. (2023). Impact of classroom environment, teacher competency, information and communication technology resources, and university facilities on student engagement and academic performance. International Journal of Information Management Data Insights, 3(2), 100188. https://doi.org/10.1016/j.jjimei.2023.100188 [Google Scholar] [Crossref]

18. Hanin, V., & Gay, P. (2023). Comparative analysis of students’ emotional and motivational profiles in mathematics in grades 1–6. Frontiers in Education, 8, 1117676. https://doi.org/10.3389/feduc.2023.1117676 [Google Scholar] [Crossref]

19. Kapasi, A., & Pei, J. (2022). Mindset theory and school psychology. Canadian Journal of School Psychology, 37(1), 57–74. https://doi.org/10.1177/08295735211053961 [Google Scholar] [Crossref]

20. Lara, F. L., Egoy, C. J. Q., Dagasdas, M. J., & Orzales, V. M. (2025). Factors Contributing to Mathematics Dislike Among Students: A Quantitative Analysis. International Journal of Research and Innovation in Social Science, 578–583. https://doi.org/10.47772/ijriss.2025.90700047 [Google Scholar] [Crossref]

21. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications. [Google Scholar] [Crossref]

22. McMullin, C. (2023). Transcription and Qualitative Methods: Implications for Third Sector Research. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 34(1),140-153. https://doi.org/10.1007/s11266-021-00400-3 [Google Scholar] [Crossref]

23. Möhring, W., Moll, L., & Szubielska, M. (2024). Mathematics anxiety and math achievement in primary school children: Testing different theoretical accounts. Journal of experimental child psychology, 247, 106038. https://doi.org/10.1016/j.jecp.2024.106038 [Google Scholar] [Crossref]

24. O’Leary, K., Fitzpatrick, C. L., & Hallett, D. (2017). Math Anxiety Is Related to Some, but Not All, Experiences with Math. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.02067 [Google Scholar] [Crossref]

25. Ongcoy, P.J.B., Jasmin, D.R.A., Guiamal, I.P., Guinita, S.S., & Iligan, A.M.M. (2023). Experiences and mathematics anxiety of STEM students. Journal of Mathematics and Science Teacher, 3(1), em028. https://doi.org/10.29333/mathsciteacher/12870 [Google Scholar] [Crossref]

26. Pericano, C.I.C., & Leonard, L. (2025). Implementation of 21st-century skills in general mathematics and its impact on student performance: Bases for developing a self-learning module. Formatif: Jurnal Ilmiah Pendidikan MIPA, 15(1), 25–38. https://doi.org/10.30998/formatif.v15i1.23179 [Google Scholar] [Crossref]

27. Ramazan, O., Danielson, R., Rougée, A., Ardasheva, Y., & Austin, B. (2023). Effects of classroom and school climate on language minority students’ PISA mathematics self-concept and achievement scores. Large-Scale Assessments in Education, 11(1). https://doi.org/10.1186/s40536-023-00156-w [Google Scholar] [Crossref]

28. Rotas, E.E., & Cahapay, M.B. (2020). Difficulties in remote learning: Voices of Philippine university students in the wake of COVID-19 crisis. Asian Journal of Distance Education, 15(2), 147–158. https://doi.org/10.5281/zenodo.4299835 [Google Scholar] [Crossref]

29. Rozgonjuk, D., Kraav, T., Mikkor, K., Orav-Puurand, K., & Täht, K. (2020). Mathematics anxiety among STEM and social sciences students: The roles of mathematics self-efficacy, and deep and surface approach to learning. International Journal of STEM Education, 7(46). https://doi.org/10.1186/s40594-020-00246-z [Google Scholar] [Crossref]

30. Sabanal, D., Gako, M., Dela Torre, H., Sabanal, J., So, R. B., Bacal, J. B., Corgio, L. D., Laroga, J. F., Camallere, C., Pagador, M. J., Barino, R. J., Mameng, K., Go, M., & Goles, N. (2024). Predictive model for college students’ performance in higher mathematics. Social Sciences & Humanities Open, 10, 101134. https://doi.org/10.1016/j.ssaho.2024.101134 [Google Scholar] [Crossref]

31. Schukajlow, S., Rakoczy, K., & Pekrun, R. (2023). Emotions and motivation in mathematics education: Where we are today and where we need to go. ZDM–Mathematics Education, 55, 249–267. https://doi.org/10.1007/s11858-022-01463-2 [Google Scholar] [Crossref]

32. Sofroniou, A., Patel, M. H., Premnath, B., & Wall, J. (2025). Advancing Conceptual Understanding: A Meta-Analysis on the Impact of Digital Technologies in Higher Education Mathematics. Education Sciences, 15(11), 1544. https://doi.org/10.3390/educsci15111544 [Google Scholar] [Crossref]

33. Subedi, K.R. (2025). Safeguarding participants: Using pseudonyms for ensuring confidentiality and anonymity in qualitative research. KMC Journal, 7(1), 1–20. https://doi.org/10.3126/kmcj.v7i1.75109 [Google Scholar] [Crossref]

34. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]

35. Walker, E.N. (2006). Urban High School Students’ Academic Communities and Their Effects on Mathematics Success. American Educational Research Journal, 43(1), 43–73. https://doi.org/10.3102/00028312043001043 [Google Scholar] [Crossref]

36. Wang C., Xu Q., & Fei W. (2024a) The effect of student-perceived teacher support on math anxiety: Chain mediation of teacher–student relationship and math self-efficacy. Frontiers in Psychology. 15, 1333012. doi: 10.3389/fpsyg.2024.1333012 [Google Scholar] [Crossref]

37. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024b). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167 [Google Scholar] [Crossref]

38. Wang X., & Wei Y. (2024). The influence of parental involvement on students’ math performance: A meta-analysis. Frontiers in Psychology, 15, 1463359. https://doi.org/10.3389/fpsyg.2024.1463359 [Google Scholar] [Crossref]

39. Xu, A., Baysari, M. T., Stocker, S. L., Leow, L. J., Day, R. O., & Carland, J. E. (2020). Researchers’ views on, and experiences with, the requirement to obtain informed consent in research involving human participants: A qualitative study. BMC Medical Ethics, 21(93). https://doi.org/10.1186/s12910-020-00538-7 [Google Scholar] [Crossref]

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