Integration of APOS, RME, and Digital Learning: A Strategic Model to Enhance Computational Thinking

Trisnawati Trisnawati, Sugeng Sutiarso, Rangga Firdaus, Tina Yunarti

Abstract


Computational Thinking (CT) is essential for 21st-century problem-solving but remains underdeveloped in abstract mathematics courses like linear algebra. This study addresses the gap by integrating APOS theory, Realistic Mathematics Education (RME), and digital tools into a strategic learning model to enhance CT and conceptual understanding. A Design-Based Research (DBR) approach was employed to develop, implement, and evaluate the model with 73 first-year university students enrolled in a linear algebra course at Institut Bakti Nusantara. Participants were grouped by class section into experimental (APOS–RME–digital integration) and comparison (conventional digital instruction) groups. Data were collected through pre-post CT assessments, classroom observations, surveys, and learning analytics. Students in the experimental group showed significantly higher gains across five CT components—abstraction, decomposition, algorithmic design, evaluation, and generalization—compared to the control group, with medium to large effect sizes. Improved conceptual understanding of vectors and linear transformations was also observed. Learners reported high usability and perceived instructional value, particularly in contextual and interactive tasks. Implementation challenges related to digital access and skills were mitigated through structured onboarding and offline resources. The findings demonstrate that a well-integrated APOS–RME digital model can systematically develop CT and mathematical understanding. High engagement and usability support its practical viability in higher education settings. This model offers a scalable, theory-informed framework for digital mathematics instruction. Future research should explore long-term impacts, equity strategies, and cross-institutional adoption to further enhance its applicability and sustainability.

Keywords


APOS theory; RME; digital learning; computational thinking; higher education

Full Text:

PDF

References


Abejuela, J. A., Santos, L. R., & Ramos, E. P. (2022). Digital equity in remote learning: Barriers and enablers in higher education. Journal of Educational Technology and Innovation, 15(2), 110–126.

Alam, M., Tariq, R., & Hassan, S. (2025). Integrating computational thinking in higher education: A framework for scalable digital pedagogy. International Journal of Educational Reform, 34(1), 47–66.

Aquino, C. P. (2024). Reusable LMS packages for higher education scalability: Lessons from digital curriculum design. Journal of Learning Systems, 12(1), 89–105.

Bishop, M. (2023). Practical considerations in educational quasi-experiments: Managing intact groups and schedule constraints. Educational Researcher, 52(4), 215–225.

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications.

Clark-Wilson, A. (2024). Implementing digital tools for equitable mathematics instruction: Strategies for low-bandwidth environments. Mathematics Teacher Education and Development, 26(1), 41–58.

de Jong, T., & Jeuring, J. (2020). The role of digital technology in mathematics education. ZDM–Mathematics Education, 52(6), 1125–1135. https://doi.org/10.1007/s11858-020-01189-8

Dreyhaupt, J., Koch, B., & Rundfeldt, C. (2017). Cluster randomization in educational research: Methodological and practical considerations. International Journal of Educational Research, 85, 111–123.

Dubinsky, E., & McDonald, M. A. (2001). APOS: A constructivist theory of learning in undergraduate mathematics education. In D. Holton (Ed.), The teaching and learning of mathematics at university level (pp. 275–282). Springer. https://doi.org/10.1007/0-306-47231-7_26

Easterday, M. W., Rees Lewis, D., & Gerber, E. M. (2021). Design-based research process: Problems, phases, and applications. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (3rd ed., pp. 190–209). Cambridge University Press.

Field, A. (2020). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

Fukui, K., Hayashi, R., & Sugimoto, M. (2023). Enhancing abstract mathematics understanding with interactive visualization tools: A longitudinal study. Technology, Pedagogy and Education, 32(2), 203–222. https://doi.org/10.1080/1475939X.2023.2170913

Gravemeijer, K., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics, 39(1–3), 111–129. https://doi.org/10.1023/A:1003749919816

Johnson, M., Cummins, J., & O'Neill, R. (2020). Equity in digital learning: A global perspective on barriers and interventions. Educational Technology Research and Development, 68(6), 3035–3054.

Latysheva, A., Moro, M., & Jennings, J. (2021). Overcoming digital disparities in university mathematics: The role of peer support and low-tech tools. Journal of Educational Media and Technology, 25(1), 56–72.

Lu, Y., Wang, Y., & Hassan, M. (2023). Enhancing computational thinking in undergraduate mathematics education. Journal of Mathematics and Technology, 17(2), 89–103.

McKenney, S., & Reeves, T. C. (2019). Conducting educational design research (2nd ed.). Routledge.

Saadah, N., & Indrawatiningsih, D. (2024). Structured digital onboarding for blended learning: A case study in Indonesian higher education. Journal of Technology Enhanced Learning, 8(1), 55–70.

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2021). Demystifying computational thinking. Educational Research Review, 32, 100364. https://doi.org/10.1016/j.edurev.2021.100364

Tariq, R., Alam, M., & Shah, I. (2024). Building future skills: Computational thinking in STEM curricula. International Journal of STEM Education, 11(1), 1–18.

Tuktamyshov, A., & Gorskaya, A. (2023). Conceptual difficulties in linear algebra among first-year students. Mathematics Education Research Journal, 35(3), 245–262. https://doi.org/10.1007/s13394-022-00450-9

Van den Heuvel-Panhuizen, M., & Drijvers, P. (2022). Realistic Mathematics Education. In S. Lerman (Ed.), Encyclopedia of Mathematics Education (pp. 675–679). Springer. https://doi.org/10.1007/978-3-030-93846-8_100214

Van Zanten, M., & van den Heuvel-Panhuizen, M. (2021). Theoretical frameworks in mathematics education research: A bibliometric review. Educational Studies in Mathematics, 106(2), 215–236. https://doi.org/10.1007/s10649-020-09989-7

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2021). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 30(1), 20–37. https://doi.org/10.1007/s10956-020-09803-w

Wikman, T., Nyström, P., & Lindberg, E. (2025). Managing validity in quasi-experimental classroom studies: Cluster assignment and statistical control. Educational Methodology Review, 18(1), 41–58.

Zana, A., Kristanto, A., & Putri, N. (2024). Modular digital resources for mathematics instruction: Building scalable infrastructure in teacher education. Asia Pacific Journal of Educational Technology, 19(2), 122–139.




DOI: https://doi.org/10.35445/alishlah.v17i4.8695

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Trisnawati Trisnawati, Sugeng Sutiarso, Rangga Firdaus, Tina Yunarti

Al-Ishlah Jurnal Pendidikan Abstracted/Indexed by:

    

 


 

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