Análisis del nivel de pensamiento computacional de los futuros maestrosuna propuesta diagnóstica para el diseño de acciones formativas

  1. Lourdes Villalustre-Martínez 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Pixel-Bit: Revista de medios y educación

ISSN: 1133-8482

Año de publicación: 2024

Número: 69

Páginas: 169-194

Tipo: Artículo

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Otras publicaciones en: Pixel-Bit: Revista de medios y educación

Resumen

El pensamiento computacional supone una forma de alfabetización emergente que busca fomentar el aprendizaje de la programación de forma progresiva utilizando principios básicos de codificación informática. En este estudio se evaluó el pensamiento computacional de 164 estudiantes universitarios de los grados de maestro/a en educación infantil y primaria. Se examinaron las diferencias según el género y la experiencia previa en programación robótica. Para ello,se empleó el Test de Pensamiento Computacional (TPC). Los resultados revelan que los hombres obtuvieron mejores resultados y que la experiencia previa en programación influyó en el nivel de desarrollo del pensamiento computacional. Además, se identificaron tres perfiles de estudiantes mediante un análisis de clúster. Las mujeres con experiencia previa en programación robótica y el uso de lenguajes de programación mostraron los mejores resultados en el TPC. Estos hallazgos resaltan la importancia de realizar evaluaciones diagnósticas para conocer el nivel de competencia de los estudiantes en este ámbito, ya que puede ayudar a identificar áreas de mejora y adaptar las acciones formativas de acuerdo a las necesidades de cada grupo de estudiantes.

Referencias bibliográficas

  • Angeli, C. & Valanides, N. (2020). Developing young children's computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in human behavior, 105, 105954. https://doi.org/10.1016/j.chb.2019.03.018
  • Atmatzidou, S. & Demetriadis, S. (2016). Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661-670. https://doi.org/10.1016/j.robot.2015.10.008
  • Álvaro Paje, M. (1990). Hacia un modelo causal del rendimiento académico. CIDE.
  • Bati, K. (2022). A systematic literature review regarding computational thinking and programming in early childhood education. Educ Inf Technol, 27, 2059–2082. https://doi.org/10.1007/s10639-021-10700-2
  • Ben, A., Dahmani, M., & Ragni, L. (2022). ICT use, digital skills and students’ academic performance: Exploring the digital divide. Information, 13(3). https://doi.org/10.3390/info13030129
  • Bers, M. (2010). The TangibleK robotics program: Applied computational thinking for young children. Early Childhood Research & Practice, 12(2), 1-20. http://ecrp.uiuc.edu/v12n2/bers.html
  • Bers, M.U., Flannery, L., Kazakoff, E.R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
  • Chan, R. (2022). A social cognitive perspective on gender disparities in self-efficacy, interest, and aspirations in science, technology, engineering, and mathematics (STEM): the influence of cultural and gender norms. International Journal of STEM Education, 9(1), 1-13. https://doi.org/10.1186/s40594-022-00352-0
  • Charlesworth, T. & Banaji, M. (2019). Gender in science, technology, engineering, and mathematics: issues, causes, solutions. Journal of Neuroscience, 39(37), 7228-7243. https://doi.org/10.1523/JNEUROSCI.0475-18.2019
  • Chan, S., Looi, C., & Sumintono, B. (2021). Assessing computational thinking abilities among Singapore secondary students: A rasch model measurement analysis. Journal of Computers in Education, 8(2), 213-236. https://doi.org/10.1007/s40692-020-00177-2
  • Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
  • Fennema, E., Carpenter, T., & Jacobs, V. (2016). A longitudinal study of gender differences in young children’s mathematical thinking. Educational Researcher, 27 (5), 6-11. https://doi.org/10.3102/0013189X027005006
  • Guggemos, J., Seufert, S., & Román-González, M. (2022). Computational Thinking Assessment–Towards More Vivid Interpretations. Technology, Knowledge and Learning, 1-30. https://doi.org/10.1007/s10758-021-09587-2
  • Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press.
  • Kaufman, L. & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.
  • Kanny, M., Sax, L., & Riggers-Piehl, T. (2014). Investigating forty years of STEM research: how explanations for the gender gap have evolved over time. Journal of Women and Minorities in Science and Engineering, 20(2), 127–148. https://doi.org/10.1615/JWomenMinorScienEng.2014007246
  • Kelleher, C. & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83-137. https://doi.org/10.1145/1089733.1089734
  • Lu, C., Macdonald, R., Odell, B., Kokhan, V., Demmans, C., & Cutumisu, M. (2022). A scoping review of computational thinking assessments in higher education. Journal Computer High Education, 34, 416–461. https://doi.org/10.1007/s12528-021-09305-y
  • Majeed, B., Jawad, L., & ALRikabi, H. (2002). Computational Thinking (CT) Among University Students. International Journal of Interactive Mobile Technologies, 16(10), 244-252
  • Maya, I., Pearson, J., Tapia, T., Wherfel, Q., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: a cross-case qualitative analysis. Computers & Education, 82, 263-279. https://doi.org/10.1016/j.compedu.2014.11.022
  • Master, A., Cheryan, S., Moscatelli, A., & Meltzoff, A. (2017). Programming experience promotes higher STEM motivation among first-grade girls. Journal of Experimental Child Psychology, 160 (1), 92-106. https://doi.org/10.1016/j.jecp.2017.03.013
  • Morris, K. (2013). Revising the Declaration of Helsinki. World Report, 381, 1889–1890. https://doi.org/10.1016/S0140-6736(13)60951-4
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
  • Popat, S. & Starkey, L. (2019). Learning to code or coding to learn? A systematic review. Computers & Education, 128, 365-376. https://doi.org/10.1016/j.compedu.2018.10.005
  • Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game design and the development of a checklist for getting computational thinking into public schools. Proceedings of the 41st ACM Technical Symposium on Computer Science Education, Milwaukee, Wisconsin, USA.
  • Román-González, M. (2015). Computational Thinking Test: Design Guidelines and Content Validation. International Conference on Education and New Learning Technologies EDULEARN. Barcelona. https://bit.ly/3yZBd7t
  • Rubio, M.J. & Vilà, R. (2017). El análisis de conglomerados bietápico o en dos fases con SPSS. REIRE. Revista d’Innovació i Recerca en Educació, 10(1), 118–126. https://doi.org/10.1344/reire2017.10.11017
  • Selby, C. (2012). Promoting computational thinking with programming. Proceedings of the 7th workshop in primary and secondary computing education, ACM. New York. 74-77. https://doi.org/10.1145/2481449.2481466
  • Sun, L., Hu, L. & Zhou, D. (2022). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers &p; Education, 181, 104457. https://doi.org/10.1016/j.compedu.2022.104457
  • Tsarava, K., Moeller, K., Román-González, M., Golle, J., Leifheit, L., Butz, M. V., & Ninaus, M. (2022). A cognitive definition of computational thinking in primary education. Computers & Education, 179, 104425. https://doi.org/10.1016/j.compedu.2021.104425
  • 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, 104083. https://doi.org/10.1016/j.compedu.2020.104083
  • Ung, L., Labadin, J., & Mohamad, F. (2022). Computational thinking for teachers: Development of a localised E-learning system. Computers & Education, 177, 104379. https://doi.org/10.1016/j.compedu.2021.104379
  • Wang, C., Shen, J., & Chao, J. (2022). Integrating computational thinking in STEM education: A literature review. International Journal of Science and Mathematics Education, 20(8), 1949-1972. https://doi.org/10.1007/s10763-021-10227-5
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
  • Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53 (4), 562-590. https://doi.org/10.1177/0735633115608444
Fuente de los datos: Dialnet