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Logo Revista Latinoamericana de Investigación en Matemática Educativa

Artículos

Vol. 27 N.º 1 (2024): Marzo

DISEÑO DE UNA TRAYECTORIA HIPOTÉTICA DE APRENDIZAJE PARA INTRODUCIR LA INFERENCIA ESTADÍSTICA INFORMAL EN PRIMARIA

DOI
https://doi.org/10.12802/relime.24.2711
Enviado
setembro 4, 2024
Publicado
2024-03-31

Resumo

Através de um estudo de desenho que desenvolveu uma trajetória de aprendizagem hipotética para a inferência estatística informal (ISI), os alunos do 3.º e 4.º anos (n=23) foram introduzidos nos conceitos-chave de variabilidade, amostragem repetida e distribuição empírica da amostragem. A trajetória em cinco etapas, com uma abordagem lúdica à experiência aleatória do lançamento de duas moedas, incluía: obter amostras; reconhecer a incerteza e expressá-la com linguagem de possibilidades; contrastar previsões através de amostragem repetida; visualizar e reconhecer a variabilidade entre amostras; atribuir níveis de possibilidades, considerando a distribuição empírica da amostragem ao generalizar para além dos dados. Os resultados indicam que os alunos de ambos os graus de ensino conseguem aceder a conceitos ISI, fazendo inferências informais e mostrando níveis sofisticados de raciocínio ISI.

Referências

  1. Arnold, P., Confrey, J., Jones, R., Lee, H., y Pfannkuch, M. (2018). Statistics learning trajectories. En D. Ben-Zvi et al. (Eds.), International handbook of research in statistics education (pp. 295–326). Springer. https://doi.org/10.1007/978-3-319-66195-7_9
  2. Ben-Zvi, D., Aridor, K., y Makar, K. (2012). Students’ emergent articulations of uncertainty while making informal statistical inferences. ZDM Mathematics Education, 44(1), 913–925. https://doi.org/10.1007/s11858-012-0420-3
  3. Ben-Zvi, D. (2016). Three paradigms in developing student´s statistical reasoning. En S. Estrella (Ed.), XX Jornadas Nacionales de Educación Matemática (pp. 13–22). Universidad Católica de Valparaíso.
  4. Clements, D., y Sarama, J. (2004). Learning Trajectories in Mathematics Education. Mathematical Thinking and Learning, 6(2), 81–89. https://doi.org/10.1207/s15327833mtl0602_1
  5. Estrella, S. (2018). Data representations in Early Statistics: data sense, meta-representational competence and transnumeration. En A. Leavy et al. (Eds.), Statistics in Early Childhood and Primary Education – Supporting early statistical and probabilistic thinking, (pp. 239–256). Singapur: Springer. https://doi.org/10.1007/978-981-13-1044-7_14
  6. Estrella, S., Zakaryan, D., Olfos, R., y Espinoza, G. (2020). How teachers learn to maintain the cognitive demand of tasks through Lesson Study. Journal of Mathematics Teacher Education, 23(1), 293–310. https://doi.org/10.1007/s10857-018-09423-y
  7. Estrella, S., Vergara, A., y Gonzalez, O. (2021). Developing data sense: Making inferences from variability in tsunamis at primary school. Statistics Education Research Journal, 20(2), 16–16. https://doi.org/10.52041/serj.v20i2.413
  8. Estrella, S., Méndez-Reina, M., y Vidal-Szabó, P. (2023a). Exploring informal statistical inference in early statistics: a learning trajectory for third-grade students. Statistics Education Research Journal, 22(2), 1–16. https://doi.org/10.52041/serj.v22i2.426
  9. Estrella, S., Méndez-Reina, M., Salinas, R., y Rojas, T. (2023b). The Mystery of the Black Box: An Experience of Informal Inferential Reasoning. En G. Burrill et al. (Eds), Research on Reasoning with Data and Statistical Thinking, (pp. 191–210). ISBN 978-3-031-29458-7. SPRINGER. https://doi.org/10.1007/978-3-031-29459-4_16
  10. Garfield, J., y Ben-Zvi, D. (2008). Preparing school teachers to develop students’ statistical reasoning. En C. Batanero, G. Burrill, C. Reading, y A. Rossman (Eds.), Joint ICMI/IASE study: teaching statistics in school mathematics. Proceedings ICMI Study 18 and 2008 IASE. ICMI y IASE.
  11. Gravemeijer, K., Bowers, J., y Stephan, M. (2003). A hypothetical learning trajectory on measurement and flexible arithmetic. Journal for Research in Mathematics Education. Monograph, 12, 51–66.
  12. Konold, C., Higgins, T., Russell, S. J., y Khalil, K. (2015). Data seen through different lenses. Educational Studies in Mathematics, 88(3), 305–325. https://doi.org/10.1007/s10649-013-9529-8
  13. Langrall, C., Nisbet, S., Mooney, E., y Jansem, S. (2011). The Role of Context Expertise When Comparing Data. Mathematical Thinking and Learning, 13(1), 47–67. https://doi.org/10.1080/10986065.2011.538620
  14. Leavy, A., Meletiou-Mavrotheris, M., y Paparistodemou, E. (Eds.). (2018). Statistics in early childhood and primary education: Supporting early statistical and probabilistic thinking. Springer. https://doi.org/10.1007/978-981-13-1044-7
  15. Lobato, L., y Walters, C. (2017). A taxonomy of approaches to learning progressions. En J. Cai (Ed.), Compendium for Research in Mathematics Education (pp. 74–101). NCTM.
  16. Makar, K., y Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82–105. https://doi.org/10.52041/serj.v8i1.457
  17. Makar, K., Bakker, A., y Ben-Zvi, D. (2011). The reasoning behind informal statistical inference. Mathematical Thinking and Learning, 13(1), 152–173. https://doi.org/10.1080/10986065.2011.538301
  18. Makar, K., y Rubin, A. (2018). Learning about statistical inference. En D. Ben-Zvi, K. Makar y J. Garfield (Eds.), International handbook of research in statistics education (pp. 261–294). Springer. https://doi.org/10.1007/978-3-319-66195-7_8
  19. Méndez-Reina, M., y Estrella, S. (2024). Razonamiento inferencial en Estadística Temprana: aspectos estructurales desde tipos de razonamiento de Peirce. En S. Estrella et al. (Eds.), Pensamiento Matemático: Aportes a la práctica docente desde la didáctica de la matemática. ISBN: 978-84-128559-8-2. España: GRAO.
  20. Moore, D. S. (2005). Estadística aplicada básica. Antoni Bosch.
  21. Pfannkuch, M. (2006). Informal inferential reasoning. En A. Rossman y B. Chance (Eds.), Proceedings ICOTS7: Working cooperatively in statistics education, Brazil.
  22. Pfannkuch, M., Arnold, P. y Wild, C. (2015). What I see is not quite the way it really is: students’ emergent reasoning about sampling variability. Educational Studies in Mathematics, 88, 343–360. https://doi.org/10.1007/s10649-014-9539-1
  23. Pratt, D., Johnston-Wilder, P., Ainley, J., y Mason, J. (2008). Local and global thinking in statistical inference. Statistics Education Research Journal, 7(2), 107–129. https://doi.org/10.52041/serj.v7i2.472
  24. Rossman, A. (2008). Reasoning about informal statistical inference: A statistician's view. Statistics Education Research Journal, 7(2), 5–19. https://doi.org/10.52041/serj.v7i2.467
  25. Rubin, A., Hammerman, J., y Konold, C. (2006). Exploring informal inference with interactive visualization software. En A. Rossman y B. Chance (Eds.), Working Cooperatively in Statistics Education. Proceedings ICOTS7, Brazil.
  26. Sannomiya, M., Mashimo, T., y Yamaguchi, Y. (2021). Creativity training for multifaceted inferences of reason behind others’ behaviors. Thinking Skills and Creativity, 39, 100757. https://doi.org/10.1016/j.tsc.2020.100757
  27. Silvestre, E., Sánchez, E. A., e Inzunza, S. (2022). El razonamiento de estudiantes de bachillerato sobre el muestreo repetido y la distribución muestral empírica. Educación matemática, 34(1), 100–130. https://doi.org/10.24844/EM3401.04
  28. Simon, M. (2020). Hypothetical Learning Trajectories in Mathematics Education. En S. Lerman (ed.), Encyclopedia of Mathematics Education. Springer. https://doi.org/10.1007/978-3-030-15789-0_72
  29. Soto, C., Gutiérrez de Blume, A., Jacovina, M., McNamara, D., Benson, N., y Riffo, B. (2019). Reading comprehension and metacognition: The importance of inferential skills. Cogent Education, 6(1), 1–20. https://doi.org/10.1080/2331186X.2019.1565067
  30. Steffe, L., y Thompson, P. (2000). Teaching experiment methodology: underlying principles and essential elements. En A. Kelly y R. Lesh (Eds.), Handbook of research design in mathematics and science education (pp. 267–306). Lawrence Erlbaum Associates.
  31. van Dijke-Droogers, M., Drijvers, P., y Bakker, A. (2020). Repeated sampling with a black box to e informal statistical inference accessible. Mathematical Thinking and Learning, 22(2), 116–138. https://doi.org/10.1080/10986065.2019.1617025
  32. van Dijke-Droogers, M., Drijvers, P., y Bakker, A. (2021). Introducing Statistical Inference: Design of a Theoretically and Empirically Based Learning Trajectory. International Journal of Science and Mathematics Education, 20(4), 1743–1766. https://doi.org/10.1007/s10763-021-10208-8
  33. Watson, J., Fitzallen, N., y Carter, P. (2013). Top Drawer Teachers: Statistics. Education Services Australia.
  34. Zieffler, A., Garfield, J., delMas, R., y Reading, C. (2008). A framework to support research on informal inferential reasoning. Statistic Education Research Journal, 7(2), 40–58. https://doi.org/10.52041/serj.v7i2.469

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