How computational thinking is represented in mathematics assessments: Insights from Indonesia, Malaysia, Singapore and their alignment with PISA

Ginda Maruli Andi Siregar(1), Roni Priyanda(2),


(1) Universitas Samudra, Langsa, Aceh, Indonesia, 24416
(2) Universitas Samudra, Langsa, Aceh, Indonesia, 24416

Abstract


Computational Thinking (CT) has emerged as an essential competency in twenty-first-century education and is increasingly integrated into mathematics curricula and assessment systems globally. Despite its growing significance, limited studies have examined how CT dimensions are represented in mathematics assessments across countries and how these dimensions align with the characteristics of the Programme for International Student Assessment (PISA). This study employed a qualitative approach using comparative content analysis. The data consisted of official junior secondary mathematics assessment documents administered in 2025 in Indonesia, Malaysia, and Singapore, which were selected through purposive sampling and analyzed using a coding framework encompassing decomposition, pattern recognition, abstraction, algorithmic thinking, and evaluation. The findings reveal substantial differences in the distribution of CT dimensions across countries. Indonesia predominantly emphasizes Decomposition (33.3%), reflecting a stronger focus on contextual understanding and information extraction. Malaysia places greater emphasis on Algorithmic Thinking (26.4%) and Abstraction (26.2%), indicating a focus on procedural fluency, mathematical modeling, and systematic problem-solving. Singapore demonstrates the highest proportion of Evaluation (15.4%) alongside strong representation of Abstraction (25.6%), highlighting formal reasoning, validation, and justification processes. Comparison with PISA tasks indicates that the assessments of the three countries share similarities in contextual problem-solving, data interpretation, and mathematical reasoning; however, they provide limited opportunities for complex system modeling, causal reasoning, simulation, and evidence-based argumentation.

Keywords


Computational thinking; assessment analysis; examination questions; junior secondary school; comparative study

Full Text:

PDF

References


Aditomo, A., Syahril, I., Yuliati, K., Suryani, N., Muhammad, H., & Handayani, F. (2024). Kajian akademik Kurikulum Merdeka. Pusat Kurikulum dan Pembelajaran, Badan Standar, Kurikulum, dan Asesmen Pendidikan, Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi.

Bahagian Pembangunan Kurikulum. (2018). Dokumen standard kurikulum dan pentaksiran tingkatan 4 dan 5 matematik tambahan. Kementerian Pendidikan Malaysia.

Bonilla-Castañeda, M. N., Villalba-Condori, K. O., Cardona-Reyes, H., Acra-Despradel, C., & Turner-Lam, K.-F. (2024). Global trends in computational thinking in curricula: A comparative review. International Conference on Computers in Education. https://doi.org/10.58459/icce.2024.5049

Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 Annual Meeting of the American Educational Research Association. https://scratched.gse.harvard.edu/ct/files/AERA2012.pdf

Büscher, C. (2025). Differences in students’ computational thinking activities when designing an algorithm for drawing plane figures. International Journal of Science and Mathematics Education, 23(2), 365–386. https://doi.org/10.1007/s10763-024-10465-3

Chytas, C., Van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. L. J. (2024). Computational thinking in secondary mathematics education with GeoGebra: Insights from an intervention in calculus lessons. Digital Experiences in Mathematics Education, 10(2), 228–259. https://doi.org/10.1007/s40751-024-00141-0

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.

El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E., Mondada, F., Zufferey, J. D., Bruno, B., & Román-González, M. (2024). The competent computational thinking test (cCTt): A valid, reliable and gender-fair test for longitudinal CT studies in grades 3–6. arXiv. https://doi.org/10.48550/arXiv.2305.19526

Falloon, G. (2024). Advancing young students’ computational thinking: An investigation of structured curriculum in early years primary schooling. Computers & Education, 216, Article 105045. https://doi.org/10.1016/j.compedu.2024.105045

Krippendorff, K. (2019). Content analysis: An introduction to its methodology (4th ed.). SAGE Publications.

Lehmann, T. H. (2024). How current perspectives on algorithmic thinking can be applied to students’ engagement in algorithmatizing tasks. Mathematics Education Research Journal, 36(3), 609–643. https://doi.org/10.1007/s13394-023-00462-0

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.

Liu, Z., Gearty, Z., Richard, E., Orrill, C. H., Kayumova, S., & Balasubramanian, R. (2024). Bringing computational thinking into classrooms: A systematic review on supporting teachers in integrating computational thinking into K–12 classrooms. International Journal of STEM Education, 11(1), 51. https://doi.org/10.1186/s40594-024-00510-6

Ministry of Education Singapore. (2024). MOE Committee of Supply 2024. https://www.moe.gov.sg/microsites/cos2024/

OECD. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing. https://doi.org/10.1787/53f23881-en

Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K–12 education: A conceptual model based on a systematic literature review. Computers & Education, 162, Article 104083. https://doi.org/10.1016/j.compedu.2020.104083

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215




DOI: https://doi.org/10.34007/jdm.v7i1.3143

Refbacks

  • There are currently no refbacks.


Journal of Didactic Mathematics

Mahesa Research Center