Patrones de interés y motivación para promover la identidad científica femenina en la educación secundaria
DOI
https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2025.v22.i3.3301Información
Autores/as
- Lourdes López-Pérez (ES) Unidad de Cultura Científica e Innovación, Sección de Divulgación Científica, Parque de las Ciencias de Andalucía https://orcid.org/0000-0002-5787-1531
- Fátima Poza-Vilches (ES) Departamento de Métodos de Investigación y Diagnóstico en Educación. Universidad de Granada
- Francisco Javier Abarca-Álvarez (ES) Departamento de Urbanística y Ordenación del Territorio. Universidad de Granada https://orcid.org/0000-0002-2725-8939
- Luis Alcalá (ES) Parque de las Ciencias de Andalucía https://orcid.org/0000-0002-6369-6186
Resumen
La organización de actividades de visibilización de referentes femeninos en el sector STEM es la principal estrategia que, desde la educación formal y no formal de ciencias, se desarrolla en la actualidad para reducir la brecha de género en ciencia desde edades tempranas. Sin embargo, la literatura ha evidenciado que estas iniciativas pueden ser un arma de doble filo y provocar el efecto contrario si no responde a perfiles de público diversos. Así se presenta un estudio evaluativo de corte diagnóstico centrado en el alumnado de educación secundaria que contribuye a la caracterización de patrones de identidad científica que favorezcan la eficacia de estas iniciativas. Los resultados señalan la existencia de dos patrones claramente diferenciados por género en la percepción sobre la igualdad en ciencia: las chicas son más conscientes de la falta de equidad que ellos, principalmente aquellas que cursan los niveles más avanzados de la enseñanza secundaria.
Palabras clave
Descargas
Agencias de apoyo
Cómo citar
Licencia
Derechos de autor 2025 Lourdes López-Pérez, Fátima Poza-Vilches, Francisco Javier Abarca-Álvarez, Luis Alcalá
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Aquellos autores/as que tengan publicaciones con esta revista, aceptan los términos siguientes:
- Los autores/as podrán conservar sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cual estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
- Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional) siempre que se indique la publicación inicial en esta revista.
- Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos telemáticos institucionales o en su página web) una vez el manuscrito sea aceptado, lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto). También se permite la difusión de la versión pre-print de los artículos a partir del momento en que son aceptados o publicados
Reconocimiento-NoComercial
CC BY-NC
Citas
Abarca-Alvarez, F. J., Campos-Sánchez, F. S. y Mora-Esteban, R. (2019a). Survey assessment for decision support using self-organizing maps profile characterization with an odds and cluster heat map: Application to children’s perception of urban school environments. Entropy, 21(9). https://doi.org/10.3390/E21090916
Abarca-Alvarez, F. J., Mora-Esteban, R. y Campos-Sánchez, F. S. (2018). Transparentar el conocimiento urbano para el apoyo a la decisión mediante inteligencia artificial: comprendiendo la percepción infantil de los entornos escolares de Granada. Teknokultura, 15(1), 89-104. https://doi.org/10.5209/TEKN.57194
Abarca-Alvarez, F. J., Reinoso-Bellido, R. y Campos-Sánchez, F. S. (2019b). Decision model for predicting social vulnerability using artificial intelligence. ISPRS Int. J. Geo-Information, 8(12), 1-26. https://doi.org/10.3390/ijgi8120575
Bian, L., Leslie, S. J. y Cimpian, A. (2017). Gender stereotypes about intellectual ability emerge early and influence children’s interests. Science, 355(6323), 389-391. https://doi.org/10.1126/science.aah6524
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
Chestnut, E., Lei, R. Leslie, S. y Cimpian, A. (2018). The myth that only brilliant people are good at math and its implications for diversity. Education Sciences, 8(2), https://doi.org/10.3390/educsci8020065
Cheryan, S., Ziegler, S. A., Montoya, A. K. y Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143(1), 1-35. https://doi.org/10.1037/bul0000052
Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in medicine, 19(22), 3127-3131. https://doi:10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0.CO;2-M
Coe, R. (2002). It’s the effect size, stupid - What effect size is and why it is important. Annual Conference of the British Education Research Association, 1-16. https://dradamvolungis.com/wp-content/uploads/2012/01/its-the-effect-size-stupid-what-effect-size-is-why-it-is-important-coe-2002.pdf
Coe, R. y Merino, C. (2003). Magnitud del efecto: Una guía para investigadores y usuarios. Revista de Psicología, 21(1), 147-177. https://doi.org/10.18800/psico.200301.006
Cohen, J. (1998). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates Publishers. https://doi.org/10.1234/12345678
Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45(12), 1304-1312. https://doi.org/10.1037/10109-028
Dancstep, T. y Sindorf, L. (2018). Creating a female‐responsive design framework for STEM exhibits. Curator: The Museum Journal, 61(3), 469-484.
Dawson, E. (2014). Equity in Informal Science Education: Developing an Access and Equity Framework for Science Museums and Science Centres. Studies in Science Education, 50(2), 209-247. https://doi.org/10.1080/03057267.2014.957558
DeWitt, J., Archer, L. y Osborne, J. (2014). Science-related Aspirations across the Primary-secondary Divide: Evidence from Two Surveys in England. International Journal of Science Education, 36(10), 1609-1629. https://doi.org/10.1080/09500693.2013.871659
Eccles, J. y Wigfield, A. (2020). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132. https://doi.org/10.1016/j.cedpsych.2020.101859
European Commission: Directorate-General for Research and Innovation (2021). She figures 2021 – Gender in research and innovation – Statistics and indicators. Publications Office. https://data.europa.eu/doi/10.2777/06090
Evagorou, M., Blanca, P., Dury, B. y Hedvika, J. (2024). Addressing the gender gap in STEM education across educational levels – Analytical report. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/260477
Faggiano, L., de Zwart, D., García-Berthou, E., Lek, S. y Gevrey, M. (2010). Patterning ecological risk of pesticide contamination at the river basin scale. Science of the Total Environment, 408(11), 2319-2326. https://doi.org/10.1016/j.scitotenv.2010.02.002
Gladstone, J. y Cimpian, A. (2021). Which role models are effective for which students? A systematic review and four recommendations for maximizing the effectiveness of role models in STEM. International Journal of STEM Education, 8(59). https://doi.org/10.1186/s40594-021-00315-x
Hair, J., Black, W., Babin, B. y Anderson, R. (2009) Multivariate Data Analysis. Prentice Hall.
Handley, I. M., Brown, E. R., Moss-Racusin, C. A., y Smith, J. L. (2015). Quality of evidence revealing subtle gender biases in science is in the eye of the beholder. Proceedings of the National Academy of Sciences, 112(43), 13201-13206. https://doi.org/10.1073/pnas.1510649112
Ibourk, A., Hughes, R. y Mathis, C. (2022). “It is what it is”: Using Storied‐Identity and intersectionality lenses to understand the trajectory of a young Black woman's science and math identities. Journal of Research in Science Teaching, 59(7), 1099-1133. https://doi.org/10.1002/tea.21753
Johnson, T., Burgoyne, A., Mix, K., Young, C. y Levine, S. (2022). Spatial and mathematics skills: Similarities and differences related to age, SES, and gender. Cognition, 218(104918). https://doi.org/10.1016/j.cognition.2021.104918
Kang, H., Calabrese, A., Tan, E., Simpkins, S., Rhee, H. y Turner, C. (2019). How do middle school girls of color develop STEM identities? Middle school girls’ participation in science activities and identification with STEM careers. Science Education, 103(2), 418-439. https://doi.org/10.1002/sce.21492
Kaski, S. y Kohonen, T. (1996). Exploratory data analysis by the Self-Organizing Map: structures of welfare and poverty in the world (1996). Neural Networks in Financial Engineering. Proceedings of the Third International Conference on Neural Networks in the Capital Markets (pp.498-507). https://doi.org/10.1.1.53.3954
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69. https://doi.org/10.1007/BF00337288
Lawner, E., Quinn, D., Camacho, G., Johnson, B. y Pan-Weisz, B. (2019). Ingroup role models and underrepresented students’ performance and interest in STEM: A meta-analysis of lab and field studies. Social Psychology of Education, 22, 1169-1195. https://doi.org/10.1007/s11218-019-09518-1
Lawner, E. (2014). Impact of role model gender and communality on college women’s math performance and interest in STEM. University of Connecticut, https://digitalcommons.lib.uconn.edu/gs_theses/688/
Mainhard, T., Oudman, S., Hornstra, L., Bosker, R. y Goetz, T. (2018). Student emotions in class: The relative importance of teachers and their interpersonal relations with students. Learning and Instruction, 53, 109-119. https://doi.org/10.1016/j.learninstruc.2017.07.011
Menacho, A., Plaza, P., Sancristóbal, E., Pérez-Molina, C., Blazquez, M. y Castro, M. (2021). Halloween Educational Robotics. IEEE Transactions on Education. https://doi.org/10.1109/TE.2021.3066891
Ministerio de Educación, Formación Profesional y Deportes, (2023). Programa para la Evaluación Internacional de los Estudiantes. Informe Español. https://www.libreria.educacion.gob.es/libro/pisa-2022-programa-para-la-evaluacion-internacional-de-los-estudiantes-informe-espanol_183950/
Mohan, R. (2023). Measurement, evaluation and assessment in education. PHI Learning Pvt. Ltd.
Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. y Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41), 16474-16479. https://doi.org/10.1073/pnas.1211286109
Nix, S., Perez-Felkner, L. y Thomas, K. (2015). Perceived Mathematical Ability Under Challenge: A Longitudinal Perspective on sex Segregation Among STEM Degree Fields. Frontiers in Psychology, 6(530). https://doi.org/10.3389/fpsyg.2015.00530
OECD (2021). Education at a glance 2021: OECD indicators. https://doi.org/10.1787/b35a14e5-en
Palmer, T., Burke, P. y Aubusson, P. (2017). Why school students choose and reject science: A study of the factors that students consider when selecting subjects. International Journal of Science Education, 39(6), 645-662. https://doi.org/10.1080/09500693.2017.1299949
Pinos-Navarrete, A., Abarca-Álvarez, F. J. y Maroto-Martos, J. C. (2022). Perceptions and Profiles of Young People Regarding Spa Tourism: A Comparative Study of Students from Granada and Aachen Universities. International Journal of Environmental Research and Public Health, 19(5), 2580. https://doi.org/10.3390/ijerph19052580
Reznic, G., Massarani, L. y Calabrese, A. (2023). Informal science learning experiences for gender equity, inclusion and belonging in STEM through a feminist intersectional lens. Cultural Studies of Science Education, 18(3). https://doi.org/10.1007/s11422-023-10149-4
Spielmans, S. E. y Thill, J. C. (2008). Social area analysisss, data mining, and GIS. Computers, Environment and Urban Systems, 32(2), 110-122. https://doi.org/10.1016/j.compenvurbsys.2007.11.004
Strick, M. y Helfferich, S. (2023). Active ingredients of science communication impact: a quantitative study at a science festival. Journal of Science Communication, 22(2), 1-13. https://doi.org/10.22323/2.22020801
Sullivan, G. y Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of graduate medical education, 4(3), 279-282. https://doi:10.4300/JGME-D-12-00156.1
Tan, E., Calabrese, A., Kang, H. y O’Neill, T. (2013). Desiring a Career in STEM-related Fields: How Middle School Girls Articulate and Negotiate Identities-in-practice in Science. Journal of Research in Science Teaching, 50(10), 1143-1179. https://doi.org/10.1002/tea.21123
Treagust, D. F. y Won, M. (2023). Paradigms in science education research. In Treagust D.F., Won, M. (coord.). Handbook of research on science education (pp. 3-27). Routledge. https://doi.org/10.4324/9780367855758
UNESCO (2017). Cracking the code: Girls’ and women’s education in science, technology, engineering and mathematics (STEM). UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000253479
Vennix, J., Den Brok, P. y Taconis, R. (2018). Do outreach activities in secondary STEM education motivate students and improve their attitudes towards STEM? International Journal of Science Education, 40(11), 1263-1283. https://doi.org/10.1080/09500693.2018.1473659
Verniers, C., Aelenei, C., Breda, T., Cimpian, J., Girerd, L., Molina, M., Sovet, L. y Cimpian, E. (2024). The Double-Edge Sword of Role Models: A Systematic Narrative Review of the Unintended Effects of Role Model Interventions on Support of the Status Quo. Review of Research in Education, 48(1), 89-120. https://doi.org/10.3102/0091732X241261310
Vesanto, J. y Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council, 11(3), 586-600. https://doi.org/10.1109/72.846731
Wasserstein, R. y Lazar, N. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129-133. https://doi.org/10.1080/00031305.2016.1154108
Wonch, P., McQuillan, J., Spiegel, A. y Diamon, J. (2018). Discovery Orientation, Cognitive, Schemas and Disparities in Science Identity in Early Adolescence. Indentity and Inequality, 61(1), 99-125. https://doi.org/10.1177/07311214177247
Yates, F. (1934). Contingency tables involving small numbers and the χ 2 test. Supplement to the Journal of the Royal Statistical Society, 1(2), 217-235. https://doi.org/10.2307/2983604