Utiliser la visualisation des données pour lire la parole et le monde
DOI :
https://doi.org/10.23925/1983-3156.2026.v28.e77361Mots-clés :
Enseignement des statistiques, Visualisation des données, Littératie critique, Littératie statistique critiqueueRésumé
Dans leur vie quotidienne, les gens sont fréquemment exposés à une grande variété de visualisations de données. Bien que les enseignants en mathématiques et en statistiques cherchent depuis longtemps des moyens d'aider les gens à lire les graphiques de données, les exigences actuelles en matière de visualisation de données sont différentes et, qui plus est, sociopolitiques. Dans cet article, nous présentons le processus de développement que nous avons suivi pour créer un cadre théorique et empirique riche pour la lecture des visualisations de données, en étudiant comment les enseignants lisent les visualisations de données de manière critique. Pour ce faire, nous présentons deux itérations d'un projet de recherche en conception visant à développer les compétences statistiques critiques des enseignants de mathématiques pour mener et enseigner des enquêtes sur des questions sociopolitiques à partir de données. Nos deux études qualitatives nous ont permis de tirer trois conclusions significatives : 1) Les questions que nous posons dans le cadre d'une activité de visualisation de données semblent contribuer à créer des occasions pour les gens de lire les graphiques de différentes manières. 2) La manière dont une visualisation de données est liée à l'expérience vécue d'une personne semble avoir un impact sur la façon dont elle lit la visualisation de données. 3) Les activités de visualisation des données peuvent servir de point de départ pour développer une culture statistique critique reliant la lecture actuelle du monde par les individus à la lecture du mot.
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Bailey, N. G., & McCulloch, A. W. (2023). Describing critical statistical literacy habits of mind. Journal of Mathematical Behavior, 70. https://doi.org/10.1016/j.jmathb.2023.101063
Bakker, A. (2018). Design research in education: A practical guide for early career researchers. Routledge.
Boaler, J., & Greeno, J. G. (2000). Identity, agency, and knowing in mathematics worlds. In J. Boaler (Ed.), Multiple perspectives on mathematics teaching and learning (pp. 171–200). Ablex Publishing.
Brantlinger, A. (2011). Rethinking critical mathematics: A comparative analysis of critical, reform, and traditional geometry instructional texts. Educational Studies in Mathematics, 78(3), 395–411.
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.
Curcio, F. R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18(5), 382–393. https://doi.org/10.2307/749086
da Silva, A. S., Barbosa, M. T. S., de Souza Velasque, L., da Silveira Barroso Alves, D., & Magalhães, M. N. (2021). The COVID-19 epidemic in Brazil: How statistics education may contribute to unravel the reality behind the charts. Educational Studies in Mathematics, 108(1–2), 269–289. https://doi.org/10.1007/s10649-021-10112-6
Darder, A. (2014). Freire and education. Routledge.
Drozda, Z., Johnstone, D., & Van Horne, B. (2022). Previewing the national landscape of K-12 data science implementation (Workshop on Foundations of Data Science for Students in Grades K-12). National Academies. https://www.nationalacademies.org/event/09-13-2022/docs/D688ED916E82498DA0E2171A109936D679FD5DE26556?noSaveAs=1
Engledowl, C., & Weiland, T. (2021). Data (mis)representation and COVID-19: Leveraging misleading data visualizations for developing statistical literacy across grades 6–16. Journal of Statistics and Data Science Education, 29(2), 160–164. https://doi.org/10.1080/26939169.2021.1915215
Esmonde, I. (2016). Power and sociocultural theories of learning. In I. Esmonde & A. N. Booker (Eds.), Power and privilege in the learning sciences: Critical and sociocultural theories of learning (pp. 6–27). Routledge.
Fernández-Pascual, R., Pinto, M., & Marco, F. J. G. (2024). Emergence and evolution of data literacy: Insights from a bibliometric study. Journal of Librarianship and Information Science. https://doi.org/10.1177/09610006241265102
Frankenstein, M. (1994). Understanding the politics of mathematical knowledge as an integral part of becoming critically numerate. Radical Statistics, 56, 22–40.
Frankenstein, M. (1998). Reading the world with math: Goals for a critical mathematical literacy curriculum. In E. Lee, D. Menkart, & M. Okazawa-Rey (Eds.), Beyond heroes and holidays: A practical guide to K-12 antiracist, multicultural education and staff development. Network of Educators on the Americas.
Freire, P. (1970). Pedagogy of the oppressed. Continuum.
Freire, P., & Macedo, D. (1987). Literacy: Reading the word and the world. Taylor and Francis.
Friel, S. N., Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education, 32(2), 124–158. https://doi.org/10.2307/749671
Gal, I. (2002). Adults’ statistical literacy: Meaning, components, responsibilities. International Statistical Review, 70(1), 1–25.
Ghodoosi, B., West, T., Li, Q., Torrisi-Steele, G., & Dey, S. (2023). A systematic literature review of data literacy education. Journal of Business & Finance Librarianship, 28(2), 112–127. https://doi.org/10.1080/08963568.2023.2171552
Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22–25. https://doi.org/10.52041/serj.v16i1.209
Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5–26.
Gutiérrez, R. (2013). The sociopolitical turn in mathematics education. Journal for Research in Mathematics Education, 44(1), 37–68.
Gutstein, E. (2003). Teaching and learning mathematics for social justice in an urban, latino school. Journal for Research in Mathematics Education, 34(1), 37–73.
Gutstein, E. (2006). Reading and writing the world with mathematics. Routledge.
Kahn, J. B., Peralta, L. M., Rubel, L. H., Lim, V. Y., Jiang, S., & Herbel-Eisenmann, B. (2022). Notice, Wonder, Feel, Act, and Reimagine as a Path Toward Social Justice in Data Science Education. Educational Technology & Society, 25 (4), 80-92. https://www.jstor.org/stable/48695983
Konold, C., Higgins, T., Russell, S. J., & 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
Ladson-Billings, G. (1995). But that’s just good teaching! The case for culturally relevant pedagogy. Theory into Practice, 34(3), 159–165.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
Lee, V. R., Wilkerson, M. H., & Lanouette, K. (2021). A Call for a Humanistic Stance Toward K–12 Data Science Education. Educational Researcher, 50(9), 664–672. https://doi.org/10.3102/0013189X211048810
Lerman, S. (2000). The social turn in mathematics education research. In J. Boaler (Ed.), Multiple perspectives on mathematics teaching and learning (pp. 19–44). Ablex.
Lesser, L. M. (2007). Critical values and transforming data: Teaching statistics with social justice. Journal of Statistics Education, 15(1), 1–21.
Lim, V. Y., Peralta, L. M. M., Rubel, L. H., Jiang, S., Kahn, J. B., & Herbel-Eisenmann, B. (2023). Keeping pace with innovations in data visualizations: A commentary for mathematics education in times of crisis. ZDM – Mathematics Education, 55(1), 109–118. https://doi.org/10.1007/s11858-022-01449-0
Lincoln, Y.S. and Guba, E.G. (1985) Establishing worthiness. In Naturalistic Inquiry (pp. 289-331). Sage.
Lovett, J. N., & Lee, H. S. (2017). New standards require teaching more statistics: Are preservice secondary mathematics teachers ready? Journal of Teacher Education, 68(3), 299–311.
Lovett, J. N., & Lee, H. S. (2019). Preservice secondary mathematics teachers’ statistical knowledge: A snapshot of strengths and weaknesses. Journal of Statistics Education, 26(3), 214–222. https://doi.org/10.1080/10691898.2018.1496806
Remillard, J., & Heck, D. J. (2014). Conceptualizing the curriculum enactment process in mathematics education. ZDM, 46(5), 705–718. https://doi.org/10.1007/s11858-014-0600-4
Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Dalhouse University. https://dalspaceb.library.dal.ca/server/api/core/bitstreams/7c0db868-79ac-4c3c-9ad0-f2a93cf2ccc6/content
Rubel, L. H., Nicol, C., & Chronaki, A. (2021). A critical mathematics perspective on reading datavisualizations: Reimagining through reformatting,reframing, and renarrating. Educational Studies in Mathematics, 108, 249–268. https://doi.org/10.1007/s10649-021-10087-4
Rubel, L. H., Lim, V. Y., Hall-Wieckert, M., & Sullivan, M. (2016). Teaching mathematics for spatial justice: An investigation of the lottery. Cognition and Instruction, 34(1), 1–26. https://doi.org/10.1080/07370008.2015.1118691
Santos, E. de V., Silva, A. F. F., & Souza, L. de O. (2024). Statistics education from a critical perspective for the study of intersectionalities. Statistics Education Research Journal, 23(2), 1–14. https://doi.org/10.52041/serj.v23i2.733
Scheaffer, R. L., & Jacobbe, T. (2014). Statistics Education in the K-12 Schools of the United States: A Brief History. Journal of Statistics Education, 22(2). http://www.amstat.org/publications/jse/v22n2/scheaffer.pdf
Shaughnessy, M. (2007). Research on statistics learning and reasoning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957–1009). Information Age Publishing.
Silva, A. F. F., & Souza, L. de O. (2024). Intersectionality as a way of (re)thinking statistics education. Revista de Educación Estadística, 3, 1–23. https://doi.org/10.29035/redes.3.1.8
Sukol, S. (2024). Beyond borders 2024: Primary and secondary data science education around the world. Data Science 4 Everyone. https://www.datascience4everyone.org/_files/ugd/0a9d2b_0dcae9d0b7c049af84cd25d174a1043f.pdf
Sukol, S. (2025). State of the field 2025 data science and data literacy education in US K-12. Data Science 4 Everyone. https://insights.datascience4everyone.org/documents/State%20of%20the%20Field%202025.pdf
Tygel, A.F., Kirsch, R. (2016). Contributions of Paulo Freire to a critical data literacy: a popular education approach. The Journal of Community Informatics, 12(3), 108—121.
Utts, J. (2003). What educated citizens should know about statistics and probability. The American Statistician, 57(2), 74–79.
Vygotsky, L. (1930). Mind and society. Harvard University Press.
Weiland, T. (2017). Problematizing statistical literacy: An intersection of critical and statistical literacies. Educational Studies in Mathematics, 96(1), 33–47. https://doi.org/10.1007/s10649-017-9764-5
Weiland, T. (2019). Critical mathematics education and statistics education: Possibilities for transforming the school mathematics curriculum. In G. Burrill & D. Ben-Zvi (Eds.), Topics and Trends in Current Statistics Education Research (pp. 391–411). Springer International Publishing. https://doi.org/10.1007/978-3-030-03472-6_18
Weiland, T., & Sundrani, A. (2022). Towards a framework for developing a critical statistical literacy. In S. Peters (Ed.), Proceedings of the 11th International Congress of Teaching Statistics. https://doi.org/10.52041/iase.icots11.T1C3
Weiland, T. Sundrani, A. Shelton, L., & Delavari, M. (2024). Creating Data Visualization Activities [Online Resource]. SLiCE: Statistical Literacy and Critical Education. https://drive.google.com/file/d/1w3YlmjDPs1t2vdUh2H8GVIjV8N3Aoalc/view?usp=sharing
Wenger, E., McDermott, R., & Snyder, W. M. (2002). Cultivating communities of practice. Harvard Business Review Press.
Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.
Wilkerson, M. H., & Laina, V. (2018). Middle school students’ reasoning about data and context through storytelling with repurposed local data. ZDM, 50(7), 1223–1235. https://doi.org/10.1007/s11858-018-0974-9
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© Travis Weiland, Anita Sundrani, Shelton Shelton, Mandana Delavari 2026
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