Mathematics Education Students' Adaptation to the Digital Learning Ecosystem

Agusalim Juhari 1 Indonesia , Sitti Habibah 2 Indonesia
1 Pendidikan Matematika, Universitas Negeri Makassar
2 Administrasi Pendidikan, Universitas Negeri Makassar
Link Copied!
Published 2026-06-05
Vol. 5 No. 3 (2026) PAGES 1934-1946
DOI 10.51574/ijrer.v5i3.5002
Views / Downloads 115 63

The transition to a Digital Learning Environment (DLE) in mathematics education often highlights a gap between rigid instructional delivery and students' diverse sensory preferences. This study aims to analyze the prevalence of learning styles among mathematics education students at Universitas Negeri Makassar (UNM) and explore their interactions with digital platforms. Employing a descriptive quantitative design, data were gathered via a standardized learning style inventory and a digital engagement questionnaire, then analyzed using descriptive statistics. The results reveal a structured hierarchical distribution dominated by visual learners (45%), followed by multimodal individuals (30%), and auditory learners (25%). These modalities directly dictate technology interaction patterns: visual students excel with dynamic geometry software and video tutorials, whereas auditory learners rely on audio-based discussions and podcasts. Notably, multimodal students exhibit the highest adaptability within Learning Management Systems (LMS) due to their capacity to simultaneously process dual sensory inputs. This research offers critical empirical insights into higher education pedagogy within the Indonesian context. It concludes that a successful digital transition requires a strategic shift toward a Dual-Coding instructional model that harmonizes dynamic visualizations with auditory narratives, thereby fostering an inclusive, adaptive, and highly effective mathematics learning environment.

Juhari, A., & Habibah, S. (2026). Mathematics Education Students’ Adaptation to the Digital Learning Ecosystem. ETDC: Indonesian Journal of Research and Educational Review , 5(3), 1934–1946. https://doi.org/10.51574/ijrer.v5i3.5002
More Citation Formats
Download Citation

Downloads

Download data is not yet available.
  1. Al-Ansi, A., et al. (2021). Analyzing the development of digital learning ecosystems in higher education. Education and Information Technologies, 26(6), 7243–7265. https://doi.org/10.1007/s10639-021-10555-5
  2. Bansah, A. K., & Darko Agyei, D. (2022). Perceived convenience, usefulness, effectiveness and user acceptance of information technology: evaluating students’ experiences of a Learning Management System. Technology, Pedagogy and Education, 31(4), 431-449. https://doi.org/10.1080/1475939X.2022.2027267
  3. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
  4. Chai, C. S., et al. (2019). Surveying and modeling students' motivation and self-regulation in cyber-physical learning environments. Educational Technology Research and Development, 67(6), 1431–1453. https://doi.org/10.1007/s11423-019-09687-2
  5. Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th ed.). John Wiley & Sons. https://doi.org/10.1002/9781119232605
  6. Cotič, M., Doz, D., Jenko, M., & Žakelj, A. (2024). Mathematics education: What was it, what is it, and what will it be?. International Electronic Journal of Mathematics Education, 19(3), em0783. https://doi.org/10.29333/iejme/14663
  7. Crompton, H., Burke, D., Jordan, K., & Wilson, S. W. G. (2021). Learning with technology during emergencies: A systematic review of K-12 education since 2010. British Journal of Educational Technology, 52(4), 1554–1575. https://doi.org/10.1111/bjet.13114
  8. Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in Psychology, 7, 508. https://doi.org/10.3389/fpsyg.2016.00508
  9. Drijvers, P. (2019). Embodied instrumentation: different perspectives on using digital technology in mathematics education. International Journal of STEM Education, 6(1), 1–14. https://doi.org/10.1186/s40594-019-0167-y
  10. Goodyear, P. (2020). Design and co-configuration for hybrid learning: Eco-social systems and practical resilience. British Journal of Educational Technology, 51(4), 1045–1060. https://doi.org/10.1111/bjet.13010
  11. Handal, B., et al. (2023). Canvas and beyond: Holistic digital ecosystems in mathematics teacher training. Journal of Digital Learning in Teacher Education, 39(1), 44–59. https://doi.org/10.1080/21532974.2022.2132333
  12. Hohenwarter, M., et al. (2016). Teaching and learning geometry with GeoGebra. International Journal for Technology in Mathematics Education, 23(2), 73–82. https://doi.org/10.1564/tme_v23_i2_a01
  13. Hoyles, C. (2018). Transforming the mathematical knowledge network with digital technologies. Educational Studies in Mathematics, 99(3), 263–277. https://doi.org/10.1007/s10649-018-9844-7
  14. Ilhan, A., Aslaner, R., & Yaşaroğlu, C. (2025). Development of digital literacy skills of 21st century mathematics teachers and prospective teachers through technology assisted education. Education and Information Technologies, 30(8), 10837-10862. https://doi.org/10.1007/s10639-024-13283-w
  15. Jonassen, D. H., et al. (2005). Everyday problem solving in the workplace: A framework for research and practice. Educational Technology Research and Development, 53(1), 41–56. https://doi.org/10.1007/BF02504857
  16. Köhler, T., et al. (2022). The digital transformation of teacher education: Structural and pedagogical challenges. Computers & Education, 182, 104473. https://doi.org/10.1016/j.compedu.2022.104473
  17. Loewus, L. (2021). Adapting to the digital shift: How variable learning modalities affect preservice teacher performance. Journal of Teacher Education, 72(4), 412–425. https://doi.org/10.1177/0022487120974421
  18. Mayer, R. E. (2014). The Cambridge Handbook of Multimedia Learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369
  19. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. https://doi.org/10.1207/S15326985EP3801_6
  20. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
  21. Mishra, N. R. (2023). Constructivist approach to learning: An analysis of pedagogical models of social constructivist learning theory. Journal of research and development, 6(01), 22-29. https://nepjol.info/index.php/jrdn/article/view/55227
  22. Mukhlis, H., Haenilah, E. Y., Maulina, D., & Nursafitri, L. (2024). Connectivism and digital age education: Insights, challenges, and future directions. Kasetsart Journal of Social Sciences, 45(3), 803-814. https://so04.tci-thaijo.org/index.php/kjss/article/view/274837
  23. Ng, W. (2015). New digital technology in education: Conceptualizing professional learning for educators. Springer. https://doi.org/10.1007/978-3-319-17461-7
  24. Nguyen, L. T., & Tuamsuk, K. (2022). Digital learning ecosystem at educational institutions: A content analysis of scholarly discourse. Cogent Education, 9(1), 2111033. https://doi.org/10.1080/2331186X.2022.2111033
  25. Niess, M. L. (2018). Scaffolding mathematics teachers’ technological pedagogical content knowledge (TPACK) development. Journal of Research on Technology in Education, 50(2), 111–127. https://doi.org/10.1080/15391523.2017.1411516
  26. O'Keeffe, L., et al. (2020). Online learning in mathematics education: Shared perspectives on the transition to remote instruction. Mathematics Education Research Journal, 32(4), 573–591. https://doi.org/10.1007/s13394-020-00345-8
  27. Paivio, A. (2014). Mind and its evolution: A dual-coding theoretical approach. Psychology Press. https://doi.org/10.4324/9781410612144
  28. Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Joint Research Centre, European Commission. https://doi.org/10.2760/159770
  29. Ríordáin, M. N., et al. (2021). Exploring the intersection of mathematics anxiety, technology integration, and online learning in initial teacher education. Mathematics Teacher Education and Development, 23(2), 89–111.
  30. Rose, D. H. (2018). Universal Design for Learning: Theory and Practice. CAST Professional Publishing.
  31. Ruthven, K. (2018). Instructional technology and mathematical practices: Core themes, Research trends, and future directions. Educational Studies in Mathematics, 99(3), 245–261. https://doi.org/10.1007/s10649-018-9828-7
  32. Sáiz-Manzanares, M. C., et al. (2020). Effectiveness of self-regulated learning in architecture and engineering students in digital environments. International Journal of Engineering Education, 36(2), 654–666.
  33. Sankey, M., et al. (2010). Engaging students through multimodal learning environments: The journey of a causal map. Australasian Journal of Educational Technology, 26(6), 852–870. https://doi.org/10.14742/ajet.1045
  34. Sweller, J. (2021). Operational definitions of cognitive load theory constructs. Educational Psychology Review, 33(2), 465–475. https://doi.org/10.1007/s10648-020-09572-7
  35. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory (Vol. 1). Springer Science & Business Media. https://doi.org/10.1007/978-1-4419-8126-4
  36. Tall, D. (2020). Advanced Mathematical Thinking. Routledge. https://doi.org/10.4324/9780429341496
  37. Wang, C., Chen, X., Yu, T., Liu, Y., & Jing, Y. (2024). Education reform and change driven by digital technology: A bibliometric study from a global perspective. Humanities and Social Sciences Communications, 11(1), 1-17. https://doi.org/10.1057/s41599-024-02717-y
  38. Zimmerman, B. J. (2015). Self-regulated learning: Theories, measures, and outcomes. International Encyclopedia of the Social & Behavioral Sciences, 21, 541–546. https://doi.org/10.1016/B978-0-08-097086-8.26060-1
  39. Zou, D., Xie, H., & Kohnke, L. (2025). Navigating the future: establishing a framework for educators' pedagogic artificial intelligence competence. European Journal of Education, 60(2), e70117. https://doi.org/10.1111/ejed.70117

Copyright (c) 2026 Agusalim Juhari, Sitti Habibah

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Similar Articles

<< < 42 43 44 45 46 47 48 49 50 51 > >> 

You may also start an advanced similarity search for this article.

Dimension Badge

Downloads

Downloads
Crossmark Badge
Crossmark Badge
Section
Articles
Keywords
Auditory Learning Digital Era Higher Education Mathematics Education Visual Learning