Teaching statistics in engineering courses: suggestions and reflections from research in statistical education
-
DOI:
https://doi.org/10.59741/agraria.v22i3.647Keywords:
Educación estadística, ingeniería, recursos instruccionalesAbstract
This document presents and describes recommendations for teaching statistics in engineering courses and some challenges associated with their implementation. The suggestions revolve around recommended statistical content for engineering courses, desirable ways in which statistics teaching and learning processes should be conducted, and instructional resources to carry them out.
Downloads
References
Aguiar, M.E., Gutiérrez, H. & Vargas, V. (2023). Statistics Education Research Journal, 22(3), 15-34. https://doi.org/10.52041/serj.v22i3.431 DOI: https://doi.org/10.52041/serj.v22i3.431
Andre, M., y Lavicza, Z. (2019). Technology changing statistics education: Defining possibilities, opportunities and obligations. The Electronic Journal of Mathematics and Technology, 13(3), 253-264.
Bakker, A., Kent, P., Derry, J., Noss, R. & Hoyles, C. (2008). Statistical inference at work: statistical, process control as an example. Statistics Education Research Journal, 7(2), 130-145. http://dx.doi.org/10.52041/serj.v7i2.473 DOI: https://doi.org/10.52041/serj.v7i2.473
Bakker, Ben-Zvi, D. & Makar, K. (2016). An inferentialist perspective on the coordination of actions and reasons involved in making a statistical inference. Mathematics Education Research Journal, 29, 455-470. https://doi.org/10.1007/s13394-016-0187-x DOI: https://doi.org/10.1007/s13394-016-0187-x
Bargagliotti, A., Franklin, C., Arnold, P. Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre-K-12 guidelines for assessment and instruction in statistics education II (GAISE II). A Framework for Statistics and Data Science Education. American Statistical Association. Disponible en línea: https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf DOI: https://doi.org/10.1162/99608f92.246107bb
Batanero, C. & Gea, M.M. (2020). Making sense of correlation and regression. En K. Villalba et al., Education and Technology in Sciences (pp. 22-35). Cham: Springer. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-030-45344-2_3. DOI: https://doi.org/10.1007/978-3-030-45344-2_3
Batanero, C. & Díaz, C. (2015). Aproximación informal al contraste de hipótesis. En J. M. Contreras, C. Batanero, J. D. Godino, G.R. Cañadas, P. Arteaga, E. Molina, M.M. Gea y M.M. López (Eds.), Didáctica de la Estadística, Probabilidad y Combinatoria 2, (pp. 207-214). Granada, 2015.
Biehler, R., Ben-Zvi, D., Bakker, A. & Makar, K. (2013). Technology for enhancing statistical reasoning at the school level. En M. A. Clements et al. (Eds.), Third International Handbook of Education (pp. 643-689). Springer. DOI: https://doi.org/10.1007/978-1-4614-4684-2_21
Borovcnik, M. & Kapadia, R. (2018). Reasoning with Risk: Teaching Probability and Risk as Twin Concepts. En Batanero, C. & Chernoff, E. J. (eds.), Teaching and Learning Stochastics, ICME-13 Monographs, (pp. 3-22). https://doi.org/10.1007/978-3-319-72871-1_1 DOI: https://doi.org/10.1007/978-3-319-72871-1_1
Budgett, S., & Pfannkuch, M. (2018). Modeling and linking the Poisson and exponential distributions. ZDM Mathematics Education, 50(7), 1281–1294. https://doi.org/10.1007/s11858-018-0957-x DOI: https://doi.org/10.1007/s11858-018-0957-x
Burrill, G. & Biehler, R. (2011). Fundamental statistical ideas in the school curriculum and in training teachers. En C. Batanero, G. Burrill, & C. Reading (Eds.), Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education: A Joint ICMI/IASE Study, (pp. 57-69). Springer. DOI: https://doi.org/10.1007/978-94-007-1131-0_10
Büscher, C. (2022). Design Principles for Developing Statistical Literacy in Middle Schools. Statistics Education Research Journal, 21(1), 8-8. https://doi.org/10.52041/serj.v21i1.80 DOI: https://doi.org/10.52041/serj.v21i1.80
Case, J., y Jacobbe, T. (2018). A framework to characterize student difficulties in learning inference from a simulation-based approach. Statistics Education Research Journal, 17(2), 9-29. https://doi.org/10.52041/serj.v17i2.156 DOI: https://doi.org/10.52041/serj.v17i2.156
Carver, R., Everson, M., Gabrosek, J., Horton, N., Lock, R., Mocko, M., Rossman, A., Holmes, G., Velleman, P., Witmer, J. & Wood, B. (2016). Guidelines for Assessment and Instruction in Statistics Education (GAISE) in Statistics Education (GAISE) College Report 2016. American Statistical Association. Disponible en línea: https://www.amstat.org/docs/default-source/amstat-documents/gaisecollege_full.pdf
Chance, B., y Rossman, A. (2021). One proportion inference [Applet]. Rossman/Chance Applet Collection 2021. Disponible en línea: https://www.rossmanchance.com/applets/2021/oneprop/OneProp.htm
Cobb, G.W. (1992). Report of the joint ASA/MAA committee on undergraduate statistics. En: The American Statistical Association 1992 proceedings of the section on statistical education, (pp. 281-283). American Statistical Association.
Figueroa, G. & Montoya, J.A. (2020). Probabilidad y Estadística. Recuperado del sitio de internet de la Universidad de Sonora, Departamento de Ingeniería Química y Metalurgia: https://www.mat.uson.mx/web/wp-content/uploads/Ing_Biomedica_Probabilidad_Estadistica.pdf
Freeman, S., Eddy, S.L., McDonough, M., Smith, M.K., Okoroafor, N., Jordt, H. & Wenderoth, M.P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), (pp. 8410-8415). https://psycnet.apa.org/doi/10.1073/pnas.1319030111 DOI: https://doi.org/10.1073/pnas.1319030111
Gal, I. (2002). Adults' statistical literacy: Meanings, components, responsibilities. International statistical review, 70(1), 1-25. https://doi.org/10.2307/1403713 DOI: https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
Garfield, J., delMas, R., & Zieffler, A. (2012). Developing statistical modelers and thinkers in an introductory, tertiary-level statistics course. ZDM Mathematics Education, 44(7), 883-898. https://doi.org/10.1007/s11858-012-0447-5 DOI: https://doi.org/10.1007/s11858-012-0447-5
González, D.U. (2001). Programa analítico de la materia de Bioestadística. Recuperado del sitio de internet de la Universidad Autónoma Agraria Antonio Narro, División de Ingeniería: http://evaluarte.uaaan.mx/CALIDAD/HORTICULTURA%20SEDE/CUARTO-INFORME-ACREDITACION/3.%20PLAN%20DE%20ESTUDIOS/CATALOGO%20DE%20PROGRAMAS%20ANALITICOS/PLAN-2004-2012/2%20BLOQUE/BIOESTADISTICA%20DEC423.pdf
Inzunza, S. & Islas, E. (2019a). Análisis de una trayectoria de aprendizaje para desarrollar razonamiento sobre muestras, variabilidad y distribuciones muestrales. Educación Matemática, 31(3), 203-230. https://doi.org/10.24844/em3103.08 DOI: https://doi.org/10.24844/EM3103.08
Inzunza, S. & Islas, E. (2019b). Diseño y Evaluación de una Trayectoria Hipotética de Aprendizaje para Intervalos de Confianza basada en Simulación y Datos Reales. Bolema, Rio Claro, 33(63), 1-26. https://doi.org/10.1590/1980-4415v33n63a01 DOI: https://doi.org/10.1590/1980-4415v33n63a01
Meng, X.L. (2011). Statistics: Your Chance for Happiness (or Misery). The Harvard Undergraduate Research Journal, 2. Disponible en: http://thurj.org/as/2011/01/1259
Moore, T.J., Hjalmarson, M. & delMas, R. (2011). Statistical Analysis When the Data is an Image: Eliciting Student Thinking About Sampling and Variability. Statistics Education Research Journal, 10(1), 15-34. http://dx.doi.org/10.52041/serj.v10i1.353 DOI: https://doi.org/10.52041/serj.v10i1.353
Neumann, D., Hood, M., & Neumann, M. (2013). Using Real-Life Data when Teaching Statistics: Student Perceptions of this Strategy in an Introductory Statistics Course. Statistics Education Research Journal, 12, 59-70. Disponible en: http://iaseweb.org/documents/SERJ/SERJ12%282%29_Neumann.pdf DOI: https://doi.org/10.52041/serj.v12i2.304
Rossman, A. (2008). Reasoning about informal statistical inference: One statistician’s view. Statistics Education Research Journal, 7(2), 5-19, https://doi.org/10.52041/serj.v7i2.467 DOI: https://doi.org/10.52041/serj.v7i2.467
Silvestre, E., Sánchez, 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 DOI: https://doi.org/10.24844/EM3401.04
Silvestre, E., Armenta, M. & Inzunza, S. (2024). Diseño y Evaluación de una Trayectoria Hipotética de Aprendizaje orientada a introducir la Prueba de Hipótesis desde un Acercamiento Informal. Bolema, Rio Claro, 38, 1-22. http://dx.doi.org/10.1590/1980-4415v38a230138 DOI: https://doi.org/10.1590/1980-4415v38a230138
Stemock, B. & Kerns, L. (2019). Use of commercial and free software for teaching statistics. Statistics Education Research Journal, 18(2), 54-67. https://doi.org/10.52041/serj.v18i2.140 DOI: https://doi.org/10.52041/serj.v18i2.140
UNAM (2015). Proyecto de modificación del plan de estudios de la licenciatura en ingeniería civil, Facultad de Ingeniería: https://www.ingenieria.unam.mx/programas_academicos/licenciatura/Civil/2016/asignaturas_civil_2016.pdf
van Dijke-Droogers, M., Drijvers, P., & Bakker, A. (2019). Repeated sampling with a black box to make informal statistical inference accessible. Mathematical Thinking and Learning, 21(2), 1-23. https://doi.org/10.1080/10986065.2019.1617025 DOI: https://doi.org/10.1080/10986065.2019.1617025
Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry (with discussion). International Statistical Review, 67(3), 223-265. DOI: https://doi.org/10.2307/1403699
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Universidad Autónoma Agraria Antonio Narro

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
PLUMX Metrics