Unpacking research on computational thinking in mathematics education: A systematic literature review

Authors

DOI:

https://doi.org/10.29408/jel.v11i2.29183

Keywords:

computational thinking, mathematics education, systematic literature review

Abstract

Computational Thinking (CT) is an essential 21st-century skill to prepare students for higher education and future careers. However, comprehensive insights into how CT is effectively implemented in mathematics learning regarding strategies, suitable topics, and integration trends are still limited. This systematic review explores empirical studies on CT in mathematics education from December 2019 to November 2024, sourced from Emerald, EBSCO, and ProQuest databases. Following PRISMA guidelines, 22 articles were selected from an initial 8,518 based on defined inclusion and exclusion criteria. The findings show that CT strongly supports students’ problem-solving skills, particularly through Project-Based Learning (PjBL), which fosters engagement, collaboration, and algorithmic thinking. Geometry and statistics emerged as the most effective topics for developing CT, as they promote decomposition, pattern recognition, and abstraction skills aligned with junior high school cognitive development. Although CT-related research varies in focus, integrating CT into mathematics remains vital, especially with the rise of digital tools and interdisciplinary learning. This review provides insight into current research trends, key strategies, and appropriate mathematical content for CT development. Recommendations include providing CT training for teachers, embedding CT into the curriculum, and encouraging interdisciplinary collaboration to equip students with the digital-age competencies needed for real-world problem-solving and conceptual understanding.

References

Abadi, S. M. (2024). Research trends of computational thinking: A bibliometric review over three decades. Journal of Research in Environmental and Science Education, 1(1), 58-75. https://doi.org/10.70232/tdwe6046

Abramovich, S. (2021). Using wolfram alpha with elementary teacher candidates: from more than one correct answer to more than one correct solution. Mathematics, 9(17), 2112. https://doi.org/10.3390/math9172112

Anthonysamy, L., Sugendran, P., Wei, L. O., & Hoon, T. S. (2024). An improved metacognitive competency framework to inculcate analytical thinking among university students. Education and Information Technologies, 1-23. https://doi.org/10.1007/s10639-024-12678-z

Anuar, N. H., Mohamad, F. S., & Minoi, J. L. (2020). Contextualising computational thinking: A case study in remote rural sarawak borneo. International Journal of Learning, Teaching and Educational Research, 19(8), 98-116. https://doi.org/10.26803/ijlter.19.8.6

Apsari, R. A., Putri, R. I. I., Abels, M., & Prayitno, S. (2020). Geometry representation to develop algebraic thinking: A recommendation for a pattern investigation in pre-algebra class. Journal on Mathematics Education, 11(1), 45-58. https://doi.org/10.22342/jme.11.1.9535.45-58

Araya, R. (2021). Enriching elementary school mathematical learning with the steepest descent algorithm. Mathematics, 9(11), 1197. https://doi.org/10.3390/math9111197

Aristizábal Zapata, J. H., Gutiérrez Posada, J. E., & Diago, P. D. (2024). Design and validation of a computational thinking test for children in the first grades of elementary education. Multimodal Technologies and Interaction, 8(5), 39. https://doi.org/10.3390/mti8050039

Belmar, H. (2022). Review on the teaching of programming and computational thinking in the world. Frontiers in Computer Science, 4, 997222. 1-19. https://doi.org/10.3389/fcomp.2022.997222

Bertrand, M. G., & Namukasa, I. K. (2020). STEAM education: student learning and transferable skills. Journal of Research in Innovative Teaching & Learning, 13(1), 43-56. https://doi.org/10.1108/JRIT-01-2020-0003

Chan, S. W., Looi, C. K., Ho, W. K., Huang, W., Seow, P., & Wu, L. (2021). Learning number patterns through computational thinking activities: A Rasch model analysis. Heliyon, 7(9). 1-14. https://doi.org/10.1016/j.heliyon.2021.e07922

Cheng, L., Wang, X., & Ritzhaupt, A. D. (2023). The effects of computational thinking integration in STEM on students’ learning performance in K-12 education: A meta-analysis. Journal of Educational Computing Research, 61(2), 416-443. https://doi.org/10.1177/07356331221114183

Christidou, D., Papavlasopoulou, S., & Giannakos, M. (2021). Using the lens of science capital to capture and explore children’s attitudes toward science in an informal making-based space. Information and Learning Sciences, 122(5/6), 317-340. https://doi.org/10.1108/ILS-09-2020-0210

Coufal, P. (2022). Project-based STEM learning using educational robotics as the development of student problem-solving competence. Mathematics, 10(23), 1-14. https://doi.org/10.3390/math10234618

Daher, W., Baya’a, N., & Jaber, O. (2022). Understanding prospective teachers’ task design considerations through the lens of the theory of didactical situations. Mathematics, 10 (3), 1-14. https://doi.org/10.3390/math10030417

Ergin, H., & Arıkan, Y. D. (2023). The effect of project based learning approach on computational thinking skills and programming self-efficacy beliefs. AJIT-e: Academic Journal of Information Technology, 14(55), 320-334. https://doi.org/10.5824/ajite.2023.04.001.x

Ezeamuzie, N. O., Leung, J. S., Fung, D. C., & Ezeamuzie, M. N. (2024). Educational policy as predictor of computational thinking: A supervised machine learning approach. Journal of Computer Assisted Learning, 40(6), 2872-2885. https://doi.org/10.1111/jcal.13041

Fojtík, M., Cápay, M., Medová, J., & Valovičová, Ľ. (2023). Activities with BBC micro: bit as a foundation for statistical reasoning of lower-secondary students. Mathematics, 11(14), 1-16. https://doi.org/10.3390/math11143206

Garcia-Piqueras, M., & Ruiz-Gallardo, J. R. (2021). Green stem to improve mathematics proficiency: Esa mission space lab. Mathematics, 9(17), 1-18. https://doi.org/10.3390/math9172066

Gonda, D., Ďuriš, V., Tirpáková, A., & Pavlovičová, G. (2022). Teaching algorithms to develop the algorithmic thinking of informatics students. Mathematics, 10(20), 1-13. https://doi.org/10.3390/math10203857

Gonda, D., Ďuriš, V., Tirpáková, A., & Pavlovičová, G. (2022). Teaching algorithms to develop the algorithmic thinking of informatics students. Mathematics, 10(20), 3857. https://doi.org/10.3390/math10203857

Hamidi, A. (2024). Advancing computational thinking education: Insights from systems thinking applications. Human Systems Management, (Preprint), 1-18. https://doi.org/10.3233/hsm-240024

Hilario, L., Mora, M. C., Montés, N., Romero, P. D., & Barquero, S. (2022). Gamification for maths and physics in university degrees through a transportation challenge. Mathematics, 10(21), 4112. https://doi.org/10.3390/math10214112

Hsieh, M. C., Pan, H. C., Hsieh, S. W., Hsu, M. J., & Chou, S. W. (2022). Teaching the concept of computational thinking: A STEM-based program with tangible robots on project-based learning courses. Frontiers in Psychology, 12, 828568. https://doi.org/10.3389/fpsyg.2021.828568

Huang, W., & Looi, C. K. (2021). A critical review of literature on “unplugged” pedagogies in K-12 computer science and computational thinking education. Computer Science Education, 31(1), 83-111. https://doi.org/10.1080/08993408.2020.1789411

Humble, N., & Mozelius, P. (2022). Making programming part of teachers' everyday life–programming affordances and constraints for K-12 mathematics and technology. The international journal of information and learning technology, 40(1), 98-112. https://doi.org/10.1108/IJILT-03-2022-0069

Jalinus, N., & Putra, R. R. (2024). Implementation of project-based learning computational thinking models in mobile programming courses. International Journal of Interactive Mobile Technologies, 18(11). https://doi.org/10.3991/ijim.v18i11.49097

Kallia, M., van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. (2021). Characterising computational thinking in mathematics education: A literature-informed Delphi study. Research in mathematics education, 23(2), 159-187. https://doi.org/10.1080/14794802.2020.1852104

Lee, H. Y., Wu, T. T., Lin, C. J., Wang, W. S., & Huang, Y. M. (2024). Integrating computational thinking into scaffolding learning: An innovative approach to enhance Science, Technology, Engineering, and Mathematics hands-on learning. Journal of Educational Computing Research, 62(2), 431-467. https://doi.org/10.1177/07356331231211916

Li, Z., & Oon, P. T. (2024). The transfer effect of computational thinking (CT)-STEM: a systematic literature review and meta-analysis. International Journal of STEM Education, 11(1), 44. https://doi.org/10.1186/s40594-024-00498-z

Looi, C. K., Chan, S. W., Wu, L., Huang, W., Kim, M. S., & Sun, D. (2024). Exploring computational thinking in the context of mathematics learning in secondary schools: Dispositions, engagement and learning performance. International Journal of Science and Mathematics Education, 22(5), 993-1011. https://doi.org/10.1007/s10763-023-10419-1

Muhammad, I., Rusyid, H. K., Maharani, S., & Angraini, L. M. (2024). Computational thinking research in mathematics learning in the last decade: a bibliometric review. International Journal of Education in Mathematics, Science and Technology, 12(1), 178-202. Https://dx.doi.org/10.46328/ijemst.3086

Mukhibin, A., Herman, T., Cahya M A, E., & Suryo Utomo, D. A. (2024). Kemampuan computational thinking siswa pada materi garis dan sudut ditinjau dari self-efficacy [Students' computational thinking skills in lines and angles material reviewed from self-efficacy]. JPMI(Jurnal Pembelajaran Matematika Inovatif). 7(1), 143-152. https://doi.org/10.22460/jpmi.v7i1.21239

Musaeus, L. H., & Musaeus, P. (2024). Computational thinking and modeling: a quasi-experimental study of learning transfer. Education Sciences, 14(9), 980. https://doi.org/10.3390/educsci14090980

Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Education Inquiry, 11(1), 1-17. https://doi.org/10.1080/20004508.2019.1627844

Nuzzaci, A. (2024). Incorporating computational thinking into education: from teacher training to student mastery. Journal of Education and Training, 11(2), 70-97. https://doi.org/10.5296/jet.v11i2.21942

Oyelere, S. S., Agbo, F. J., & Sanusi, I. T. (2022, August). Developing a pedagogical evaluation framework for computational thinking supporting technologies and tools. In Frontiers in Education (Vol. 7, p. 957739). Frontiers Media SA.

Pramudiani, P., Alyani, F., Dolk, M., & Widjaja, W. (2024). Investigating fraction computation problem-solving among pre-service primary school teachers. Jurnal Elemen, 10(3), 685-710. https://doi.org/10.29408/jel.v10i3.27462

Purwasih, R., Turmudi, T., Dahlan, J. A., & Ishartono, N. (2024). Computational thinking on concept pattern number: A study learning style Kolb. Jurnal Elemen, 10(1), 89-104. https://doi.org/10.29408/jel.v10i1.23056

Rich, K. M., Yadav, A., & Fessler, C. J. (2024). Computational thinking practices as tools for creating high cognitive demand mathematics instruction. Journal of Mathematics Teacher Education, 27(2), 235-255. https://doi.org/10.1007/s10857-022-09562-3

Saad, A., & Zainudin, S. (2024). A review of teaching and learning approach in implementing Project-Based Learning (PBL) with Computational Thinking (CT). Interactive Learning Environments, 1-25. https://doi.org/10.1080/10494820.2024.2328280

Seckel, M. J., Breda, A., Font, V., & Vásquez, C. (2021). Primary school teachers’ conceptions about the use of robotics in mathematics. Mathematics, 9(24), 3186. https://doi.org/10.3390/math9243186

Sezer, H. B., & Namukasa, I. K. (2021). Real-world problems through computational thinking tools and concepts: The case of coronavirus disease (COVID-19). Journal of Research in Innovative Teaching & Learning, 14(1), 46-64. https://doi.org/10.1108/JRIT-12-2020-0085

Silver, P., Dupuis, J., Durham, R. E., Schaaf, R., Pallett, L., & Watson, L. (2024). Building technology integration at an urban school through a PDS partnership. School-University Partnerships, (ahead-of-print). https://doi.org/10.1108/SUP-10-2023-0041

Subramaniam, S., Maat, S. M., & Mahmud, M. S. (2022). Computational thinking in mathematics education: a systematic review. Cypriot Journal of Educational Sciences, 17(6), 2029-2044. http://doi.org/10.18844/cjes.v17i6.7494

Suh, J., Matson, K., Seshaiyer, P., Jamieson, S., & Tate, H. (2021). Mathematical modeling as a catalyst for equitable mathematics instruction: Preparing teachers and young learners with 21st century skills. Mathematics, 9(2), 162. https://doi.org/10.3390/math9020162

Taufik, M., Inam, A., & Susanti, R. D. (2024). Computational thinking in mathematical problem solving: Pattern recognition. International Journal of Multidisciplinary: Applied Business and Education Research, 5(3), 791-797. https://doi.org/10.11594/ijmaber.05.03.05

Tsai, M. J., Liang, J. C., Lee, S. W. Y., & Hsu, C. Y. (2022). Structural validation for the developmental model of computational thinking. Journal of Educational Computing Research, 60(1), 56-73. https://doi.org/10.1177/07356331211017794

Valovičová, Ľ., Ondruška, J., Zelenický, Ľ., Chytrý, V., & Medová, J. (2020). Enhancing computational thinking through interdisciplinary steam activities using tablets. Mathematics, 8(12), 2128. https://doi.org/10.3390/math8122128

Ye, H., Liang, B., Ng, O. L., & Chai, C. S. (2023). Integration of computational thinking in K-12 mathematics education: A systematic review on CT-based mathematics instruction and student learning. International Journal of STEM Education, 10(1), 3. https://doi.org/10.1186/s40594-023-00396-w

Yeni, S., Grgurina, N., Saeli, M., Hermans, F., Tolboom, J., & Barendsen, E. (2024). Interdisciplinary integration of computational thinking in K-12 education: A systematic review. Informatics in Education, 23(1), 223-278.

Yin, S. X., Hoe-Lian Goh, D., & Quek, C. L. (2024). Collaborative learning in k-12 computational thinking education: a systematic review. Journal of Educational Computing Research. https://doi.org/10.1177/07356331241249956

Yunianto, W., Bautista, G., Prasetyo, B. D., & Lavicza, Z. (2024). A HLT for integrated CT and mathematics lessons: Supporting students' possible struggles when debugging in geogebra environment. International Journal for Technology in Mathematics Education, 31(1), 11-20. https://doi.org/10.1564/tme_v31.1.02

Yusrina, H., Narimo, S., Novitasari, M., & Adnan, M. (2024). Mathematics learning model based on computational thinking: preparing elementary school students to be disciplined, independent, and dignified. Journal of Law and Sustainable Development, 12(1). https://doi.org/10.55908/sdgs.v12i1.3086

Zhang, W., Guan, Y., & Hu, Z. (2024). The efficacy of project-based learning in enhancing computational thinking among students: A meta-analysis of 31 experiments and quasi-experiments. Education and Information Technologies, 1-33. https://doi.org/10.1007/s10639-023-12392-2

Zhang, W., Zeng, X., Ming, D., & Wang, J. (2022). Research on the construction of evaluation indicators of students' computational thinking based on spectral clustering. In 2022 10th International Conference on Information and Education Technology (ICIET) (pp. 104-112). IEEE. https://doi.org/10.1109/ICIET55102.2022.9779003

Zhang, Y., & Savard, A. (2023). Defining computational thinking as an evident tool in problem-solving: Comparative research on chinese and canadian mathematics textbooks. ECNU Review of Education, 6(4), 677-699. https://doi.org/10.1177/20965311231158393

Downloads

Published

31-05-2025

How to Cite

Cut Morina Zubainur, Cut Rina Rossalina, Muhammad Subianto, & Dwi Fadhiliani. (2025). Unpacking research on computational thinking in mathematics education: A systematic literature review. Jurnal Elemen, 11(2), 447–467. https://doi.org/10.29408/jel.v11i2.29183

Issue

Section

Articles

Similar Articles

<< < 2 3 4 5 6 7 8 9 10 11 > >> 

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