Application of computational thinking in education: strategies for the development of 21st-century competencies

Authors

DOI:

https://doi.org/10.63688/806c5n20

Keywords:

computational thinking, education, 21st-century competencies, programming, problem solving

Abstract

Computational thinking is a fundamental competency for addressing the educational, social, and technological challenges of the 21st century because it enables learners to formulate problems, organize information, identify patterns, and design logical and systematic solutions. The objective of this article was to analyze the application of computational thinking in education and identify the pedagogical strategies that contribute to the development of critical thinking, creativity, collaboration, digital competence, and problem-solving skills. The study was conducted through a systematic literature review with a qualitative, documentary, and analytical approach, following the PRISMA 2020 guidelines. Academic databases such as Scopus, Web of Science, ScienceDirect, ACM Digital Library, SpringerLink, ERIC, and Google Scholar were consulted, and 21 studies published between 2006 and 2024 were included. The findings show that visual programming, educational robotics, unplugged activities, problem-based learning, and interdisciplinary projects strengthen decomposition, abstraction, algorithmic design, and debugging. It is concluded that effective integration requires teacher training, appropriate assessment criteria, and equitable access to technological resources.

References

Allsop, Y. (2019). Assessing computational thinking process using a multiple evaluation approach. International Journal of Child-Computer Interaction, 19, 30–55. https://doi.org/10.1016/j.ijcci.2018.10.004

Chen, P., Yang, D., Metwally, A. H. S., Lavonen, J., & Wang, X. (2023). Fostering computational thinking through unplugged activities: A systematic literature review and meta-analysis. International Journal of STEM Education, 10, Article 47. https://doi.org/10.1186/s40594-023-00434-7

Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39. https://doi.org/10.1145/2998438

Dong, W., Li, Y., Sun, L., & Liu, Y. (2024). Developing pre-service teachers’ computational thinking: A systematic literature review. International Journal of Technology and Design Education, 34, 191–227. https://doi.org/10.1007/s10798-023-09811-3

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051

Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004

Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2021). The associations between computational thinking and creativity: The role of personal characteristics. Journal of Educational Computing Research, 58(8), 1415–1447. https://doi.org/10.1177/0735633120940954

Kong, S.-C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026

Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales. Computers in Human Behavior, 72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005

Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K–12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012

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

Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37, Article 43. https://doi.org/10.17705/1CAIS.03743

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71

Palts, T., & Pedaste, M. (2020). A model for developing computational thinking skills. Informatics in Education, 19(1), 113–128. https://doi.org/10.15388/infedu.2020.06

Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, Article 103798. https://doi.org/10.1016/j.compedu.2019.103798

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

Weintrop, D., Beheshti, E., Horn, M. S., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5

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

Witherspoon, E. B., Higashi, R. M., Schunn, C. D., Baehr, E. C., & Shoop, R. (2017). Developing computational thinking through a virtual robotics programming curriculum. ACM Transactions on Computing Education, 18(1), Article 4, 1–20. https://doi.org/10.1145/3104982

Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971

Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), Article 5, 1–16. https://doi.org/10.1145/2576872

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, Article 3. https://doi.org/10.1186/s40594-023-00396-w

Published

2026-06-28

How to Cite

Fernández Acevedo, J. E. ., García, J. L. ., Naranjo Brito, A. L. ., Sánchez Ronquillo, K. A. ., & Gómez Freire, F. J. . (2026). Application of computational thinking in education: strategies for the development of 21st-century competencies. Sage Sphere of Technology, Sciences, Discoveries And Society, 4(1), 1-30. https://doi.org/10.63688/806c5n20