Aplicación del pensamiento computacional en educación: estrategias para el desarrollo de competencias del siglo XXI
DOI:
https://doi.org/10.63688/806c5n20Palabras clave:
pensamiento computacional, educación, competencias del siglo XXI, programación, resolución de problemasResumen
El pensamiento computacional constituye una competencia fundamental para enfrentar los desafíos educativos, sociales y tecnológicos del siglo XXI, porque permite formular problemas, organizar información, identificar patrones y diseñar soluciones lógicas y sistemáticas. El objetivo de este artículo fue analizar la aplicación del pensamiento computacional en la educación y reconocer las estrategias pedagógicas que contribuyen al desarrollo del pensamiento crítico, la creatividad, la colaboración, la competencia digital y la resolución de problemas. La investigación se desarrolló mediante una revisión sistemática de literatura con enfoque cualitativo, documental y analítico, siguiendo las directrices PRISMA 2020. Se consultaron bases académicas como Scopus, Web of Science, ScienceDirect, ACM Digital Library, SpringerLink, ERIC y Google Scholar, y se incluyeron 21 estudios publicados entre 2006 y 2024. Los resultados evidencian que la programación visual, la robótica educativa, las actividades desenchufadas, el aprendizaje basado en problemas y los proyectos interdisciplinarios favorecen la descomposición, la abstracción, el diseño algorítmico y la depuración. Se concluye que su integración requiere formación docente, criterios de evaluación pertinentes y acceso equitativo a recursos tecnológicos.
Referencias
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
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Jorge Eduardo Fernández Acevedo, José Luis García, Anabel Lisbeth Naranjo Brito, Karolayn Alejandra Sánchez Ronquillo, Félix Javier Gómez Freire (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
