STEM, a Non-Place for Women? Evidences and Transformative Initiatives
Abstract
:1. Introduction
2. STEM, a Non-Place for Women
3. The Role of Spanish Universities Promoting Gender Equality in STEM
3.1. Initiatives in University of Santiago de Compostela (USC)
4. An Alternative Proposal: Informatics and Life
4.1. Methodology
4.2. Online Material for School Teachers
- Quen inventou a informática? (“Who invented computing?”): addresses the role of women in the birth of computer science, making visible many female scientists and professionals in this field. It also includes materials and instructions for developing games that explore the inspiration and importance of some female scientists’ inventions, using the paradigm of unplugged computational thinking.
- Como son as tripas dun ordenador? (“What are the insides of the computer like?”): explains the operation and parts of a computer by establishing analogies between the computer hardware and our human body. It relates engineering to biology, which is frequently very enjoyed by girls.
- Os recordos dos ordenadores: escribindo nunha libreta (“Memories of computers: writing in a notebook”): uses a squared notebook page to explain the process of storing information on a computer’s memory or hard disk.
- As fotos e imaxes: que son os megapíxeles nas cámaras de fotos? (“Photos and images: what are megapixels in cameras?”): defines the concept of picture element (pixel) in the digital images and its capacity measures, relating image size with camera characteristics. The storage of color images in the hard disk is also introduced.
- Xoguemos a ser científicas e científicos na medicina e na pesca (“Let’s play at being scientists in medicine and fishing”): the objective of this activity is to show the interrelation of computer science with other sciences, such as medicine or biology. As other engineering fields, computer science provides tools to help other disciplines. The work environment of scientists in biomedicine and biology is simulated through two research software products shared with students, STERApp and CystAnalyser, described by Mbaidin et al. (2021) and Cordido et al. (2020), respectively. Software tools and videos can be downloaded to simulate the diary work of researchers in the biomedical and fishering labs.
- Medindo células contando puntos (píxeles) nas imaxes (“Measuring cells by counting pixels in images”): the objective here is to make students aware that all the results provided by a computer program have to be verified with the established method, that is, it always has to be validated by people in order to ensure that calculations are right. In this case, students validate that the CystAnalyser software measures correctly the area of irregular objects in images.
- A escala dixital: medindo a realidade que nos rodea (“The digital scale: measuring the reality around us”): this activity introduces the concept of calibration to relate picture elements with truth measures of objects, in this case, the area of geometric shapes in images. Provide materials and instructions to discuss with students how humans measure the surface area of geometric shape compare to how computers do.
- Medindo a realidade nas ciencias naturais e sociais (“Measuring reality in natural and social sciences”): the concept of a digital scale allows us to measure every object in the world which can be captured by a digital camera. This technology is used to measure the area of a plant leaf, and the extension of a region on a map.
- En que se parece un conto a un programa? (“How is a story similar to a computer program?”): ICT professionals use programming languages to build computer programs. Our goals are to introduce the concept of programming language as if it were the learning of another human language, that people uses to communicate with a computer.
- Que é a intelixencia artificial? (“What is artificial intelligence?”): This activity is open access10. We model a computer program as a box that receives data to provide an answer. In classical programming, the programmer sets rules to the program in order to achieve a positive answer or outcome to the input data. Supervised machine-learning (SML) algorithms are a specific type of computer program, which represent the majority of programs within AI. In SML programs, the programmer gives to the box examples (data) and the desired answer (outcome) in order for the box (AI system) to learn the rules to produce the outcome from the data, in a process called training. Once the box or AI algorithm is trained, it can operate to give answers to examples not seen in the training. Many people in society do not understand the differences between classical and SML programs. This activity explains both concepts without using technology, through the sorting of spaghetti by their length (classical programming), and the distance traveled by a ball kicked by a girl (ML program). Depending on the educational level, this activity can be extended by putting on a mini-play in the classroom with the students, or creating programs with a professional programming language like Python11) for the oldest students. Since AI systems introduce bias in different ways, this activity, using common examples, explains how the different types of bias are encoded in the ML algorithms. Gender bias is normally due to bias in data used for training, and in the criteria for the outcome verification. Other biases are due to the statistical representation of minority groups. The provided examples allow us to reflect with the students about gender bias in AI systems, following the recommendation of Vicente and Matute (2023).
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
STEM | Science, technology, engineering, and maths |
ICT | Information and communication technologies |
AI | Artificial intelligence |
CT | Computational thinking |
GPT | Generative pre-trained transformer |
1 | https://data.europa.eu/doi/10.2777/592260 (accessed on 12 June 2025). |
2 | Informe sobre Empleabilidad y Talento Digital 2024. |
3 | Red de Unidades de Igualdad de Género de Excelencia Universitaria. |
4 | https://cifex.usc.gal/actividade/informe_fenda_salarial (accessed on 12 June 2025). |
5 | https://unhaencadacole.gal/ (accessed on 12 June 2025). |
6 | https://scratch.mit.edu/ (accessed on 12 June 2025). |
7 | Guía para docentes: Integración de la perspectiva de género en las disciplinas STEM de educación primaria. Link: https://inspirasteam.net/guias-docentes/ (accessed on 12 June 2025). |
8 | https://tec.citius.usc.es/ainformaticaeavida/ (accessed on 12 June 2025). |
9 | https://www.python.org/ (accessed on 12 June 2025). |
10 | https://tec.citius.usc.es/ainformaticaeavida/que-e-a-intelixencia-artificial/ (accessed on 12 June 2025). |
11 | See Note 9. |
12 | |
13 |
References
- Aguayo-Lorenzo, Eva, and Encina Calvo-Iglesias. 2024. Claves para una docencia universitaria con perspectiva de género. In Calidad e Innovación Pedagógica: Experiencias Docentes y Tecnológicas Aplicadas al Aula. Madrid: Dykinson, pp. 172–85. [Google Scholar]
- Alcalde-González, Verna, and Simone Belli. 2024. Managing the work-care conflict in the scientific-academic field: A qualitative study on the experiences of women researchers in Spain. Revista Espanola de Investigaciones Sociologicas 188: 3–20. [Google Scholar] [CrossRef]
- Alonso-Álvarez, Alba, and Isabel Diz-Otero. 2022. ¿Nadando contra corriente? Resistencias a la promoción de la conciliación en las universidades españolas. International Review of Sociology 80: e208. [Google Scholar] [CrossRef]
- Alonso-Ruido, Patricia, Alexandra Miroslava Rodríguez-Gil, Iris Estévez, and Bibiana Regueiro. 2025. Gender equality and sexism. The views of Education Sciences students. EDUCAR 61: 53–68. [Google Scholar] [CrossRef]
- Ascencio Cortés, María Soledad, Yasna Anabalón Anabalón, and Emmanuel Vega-Román. 2025. Institutional Gender Change in Higher Education? Resistance and Perspectives from a Scoping Review of Literature. Revista Fuentes 27: 15–30. [Google Scholar] [CrossRef]
- Augé, Marc. 1995. Non-Places: Introduction to an Anthropology of Supermodernity. London: Verso. [Google Scholar]
- Avraamidou, Lucy. 2024. Can we disrupt the momentum of the AI colonization of science education? Journal of Research in Science Teaching 61: 2570–74. [Google Scholar] [CrossRef]
- Ayoub, Noel F., Karthik Balakrishnan, Marc S. Ayoub, Thomas F. Barrett, Abel P. David, and Stacey T. Gray. 2024. Inherent bias in large language models: A random sampling analysis. Mayo Clinic Proceedings: Digital Health 2: 186–91. [Google Scholar] [CrossRef]
- Ayuso, Natalia, Elena Fillola, Belén Masiá, Ana C. Murillo, Raquel Trillo-Lado, Sandra Baldassarri, Eva Cerezo, Laura Ruberte, M. Dolores Mariscal, and María Villarroya-Gaudó. 2021. Gender Gap in STEM: A Cross-Sectional Study of Primary School Students’ Self-Perception and Test Anxiety in Mathematics. Transactions on Education 64: 40–49. [Google Scholar] [CrossRef]
- Beltrán, Marta. 2023. MR. INTERNET: Cómo se Relacionan la Tecnología y el Género y cómo te Afecta a Ti. Pamplona: Next Door Publishers. [Google Scholar]
- Berrío-Zapata, Cristian, Ester Ferreira da Silva, Tamara de Souza Brandão Guaraldo, and Ângela Maria Grossi de Carvalho. 2019, dic. Exclusión digital de género: Rompiendo el silencio en la ciencia de la información. Revista Interamericana de Bibliotecología 43: eRv1/1–eRv1/14. [CrossRef]
- Bian, Lin, Sarah-Jane Leslie, and Andrei Cimpian. 2017. Gender stereotypes about intellectual ability emerge early and influence children’s interests. Science 355: 389–91. [Google Scholar] [CrossRef]
- Buolamwini, Joy, and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research em 81: 77–91. [Google Scholar]
- Butcher, Madeleine, Elizabeth L. Cohen, Christine E. Kunkle, and Daniel Totzkay. 2023. Geek girl today, scientist tomorrow? Inclusive experiences and efficacy mediate the link between women’s engagement in popular geek culture and stem career interest. International Journal of Science Education 13: 276–91. [Google Scholar] [CrossRef]
- Calvo-Iglesias, Encina. 2015. El no-lugar de las científicas. en locas, escritoras y personajes femeninos cuestionando las normas. In XII Congreso Internacional del Grupo de Investigación Escritoras y Escrituras. Sevilla: Alciber, pp. 212–20. [Google Scholar]
- Calvo-Iglesias, Encina. 2020. Preparing biographies of STEM women in the wikipedia format, a teaching experience. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje/ 15: 211–14. [Google Scholar] [CrossRef]
- Calvo-Iglesias, Encina, and Eva Aguayo-Lorenzo. 2023a. Promoting Female STEM Vocations Within the SDG Framework. Paper presented at the 17th International Technology, Education and Development Conference, Valencia, Spain, March 6–8; pp. 2245–48. [Google Scholar] [CrossRef]
- Calvo-Iglesias, Encina, and Eva Aguayo-Lorenzo. 2023b. Talleres online para fomentar las vocaciones CTIM femeninas. In Educación, Tecnología, Innovación y Transferencia del Conocimiento. Madrid: Dykinson, pp. 177–86. [Google Scholar]
- Calvo-Iglesias, Encina, Eva Cernadas-García, and Manuel Fernández-Delgado. 2022. Providing female role models in STEM higher education careers, a teaching experience. Paper presented at the XII International Conference on Virtual Campus, Arequipa, Perú, September 29–30; pp. 1–4. [Google Scholar] [CrossRef]
- Carballo, Julia, Alma María Gómez-Rodríguez, and María de las Nieves Lorenzo-González. 2020. Providing female models and promoting vocations: A practical experience in stem fields. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 15: 317–25. [Google Scholar] [CrossRef]
- Casad, Bettina J., Jillian E. Franks, Christina E. Garasky, Melinda M. Kittleman, Alanna C. Roesler, Deidre Y. Hall, and Zachary W. Petzel. 2021. Gender inequality in academia: Problems and solutions for women faculty in STEM. Journal of Neuroscience Research 99: 13–23. [Google Scholar] [CrossRef]
- Castaño-Collado, Cecilia, and Susana Vázquez-Cupeiro. 2023. Resistance and counter-resistance to gender equality policies in Spanish universities. Papers 108: e3105. [Google Scholar] [CrossRef]
- Cernadas, Eva, and Encina Calvo-Iglesias. 2020. Gender perspective in artificial intelligence (AI). In Proceedings of the VIII International Conference on Technological Ecosystems for Enhancing Multiculturality, Salamanca, Spain, 21–23 October 2020. Edited by Francisco García-Penalvo and Alicia García-Holgado. ACM Intl Conf Proc Series; Piscataway: IEEE, p. 4. [Google Scholar] [CrossRef]
- Cernadas, Eva, and Encina Calvo-Iglesias. 2022. Perspectiva de género en inteligencia artificial, una necesidad. Cuestiones de Género: De la Igualdad y la Diferencia 117: 111–127. [Google Scholar] [CrossRef]
- Cernadas, Eva, and Manuel Fernández-Delgado. 2021. Embedded ethics to teach machine learning courses: An experience. Paper presented at the XI International Conference on Virtual Campus, Salamanca, Spain, September 30–October 1; pp. 1–4. [Google Scholar] [CrossRef]
- Cernadas, Eva, and Manuel Fernández-Delgado. 2024. Experiences teaching machine thinking with gender perspective in schools. In Virtual Campuses and Smart E-Learning Environments. Singapore: Springer Nature, p. 10. [Google Scholar]
- Cernadas, Eva, Manuel Fernández-Delgado, Mar Lorenzo, and María Ferraces. 2023. Promoting equality in higher education computer programming courses through cooperative learning. Paper presented at the XIII International Conference on Virtual Campus, Porto, Portugal, September 25–26; pp. 1–4. [Google Scholar] [CrossRef]
- Cheryan, Sapna, Ella J. Lombard, Fasika Hailu, Linh N. H. Pham, and Katherine Weltzien. 2025. Global patterns of gender disparities in stem and explanations for their persistence. Nature Reviews Psychology 4: 6–19. [Google Scholar] [CrossRef]
- Chomsky, Noam. 2025. Noam Chomsky: The False Promise of ChatGPT. The New York Times. Available online: https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html (accessed on 12 June 2025).
- Cimpian, Joseph R., Taek H. Kim, and Zachary T. McDermott. 2020. Understanding persistent gender gaps in stem. Science 368: 1317–19. [Google Scholar] [CrossRef]
- Collett, Clementine, Gina Neff, and Livia Gouvea. 2022. The Effects of AI on the Working Lifes of Women. Paris: UNESCO. [Google Scholar] [CrossRef]
- Cordido, Adrián, Eva Cernadas, Manuel Fernández-Delgado, and Miguel A. García-González. 2020. Cystanalyser: A new software tool for the automatic detection and quantification of cysts in polycystic kidney and liver disease, and other cystic disorders. PLoS Computational Biology 16: 1–18. [Google Scholar] [CrossRef]
- Crawford, Kate. 2021. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press. [Google Scholar]
- Criado, Carolina Pérez. 2019. Invisible Women: Exposing Data Bias in a World Designed for Men. New York: Random House. [Google Scholar]
- De la Torre-Sierra, Ana María, and Virginia Guichot-Reina. 2025. Women in video games: An analysis of the biased representation of female characters in current video games. Sexuality and Culture 29: 532–60. [Google Scholar] [CrossRef]
- Desai, Pooja, Hao Wang, Lindy Davis, Timothy M. Ullmann, and Sandra R. DiBrito. 2024. Bias perpetuates bias: ChatGPT learns gender inequities in academic surgery promotions. Journal of Surgical Education 81: 1553–57. [Google Scholar] [CrossRef] [PubMed]
- Dornis, Tim W., and Sebastian Stober. 2024. Copyright Law and Generative AI Training—Technological and Legal Foundations. Baden-Baden: NOMOS Verlag. Available online: https://ssrn.com/abstract=4946214 (accessed on 12 June 2025).
- Engel-Hermann, Patricia, and Alexander Skulmowski. 2024. Appealing, but misleading: A warning against a naive AI realism. AI Ethics 5: 3407–13. [Google Scholar] [CrossRef]
- Epifanio, Irene, and Encina Calvo-Iglesias. 2024. Actions for gender equality in scientific-technical areas in Spanish universities. Educación XX1 27: 19–36. [Google Scholar] [CrossRef]
- Eurostat. 2024. Women Totalled Almost a Third of STEM Graduates in 2021. Available online: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20240308-2 (accessed on 12 June 2025).
- Farkas, Anna, and Renáta Németh. 2022. How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points. Social Sciences & Humanities Open 5: 100239. [Google Scholar] [CrossRef]
- Fernandez-Morante, Carmen, Beatriz Cebreiro López, and Lorena Casal Otero. 2020. Train and motivate girls for their future participation in the ict sector. proposal from five countries. Innoeduca. International Journal of Technology and Educational Innovation 6: 115–27. [Google Scholar] [CrossRef]
- Ferran-Ferrer, Núria, Juan-José Boté-Vericad, and Julià Minguillón. 2023. Wikipedia gender gap: A scoping review. Profesional de la Información 32: 1–13. [Google Scholar] [CrossRef]
- Goldin, Claudia. 2024. Nobel Lecture: An Evolving Economic Force. American Economic Review 114: 1515–39. [Google Scholar] [CrossRef]
- González-Gallego, Sofía, Mariana Hernández-Pérez, José A. Alonso-Sánchez, Pedro M. Hernández-Castellano, and Eduardo G. Quevedo-Gutiérrez. 2025. A critical examination of the underlying causes of the gender gap in stem and the influence of computational thinking projects applied in secondary school on stem higher education. Frontiers in Education 10: 1–24. [Google Scholar] [CrossRef]
- González-González, Carina S., and Alicia García-Holgado. 2021. Strategies to gender mainstreaming in engineering studies: A workshop with teachers. Paper presented at the XXI International Conference on Human-Computer Interaction, Málaga, Spain, September 22–24; pp. 1–5. [Google Scholar] [CrossRef]
- González Ramos, Ana M., Núria Vergés Bosch, and José Saturnino Martínez García. 2024. Women in the technology labour market. Revista Española de Investigaciones Sociológicas 159: 73–90. [Google Scholar] [CrossRef]
- González-Rogado, Ana Belén, Ana Belén Ramos-Gavilán, María Ascensión Rodríguez-Esteban, and Alicia García-Holgado. 2025. Perception disparity between women and men on the gender gap in stem at a spanish university. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 20: 1–11. [Google Scholar] [CrossRef]
- Gómez-Trigueros, Isabel María. 2023. Validation of the tpack-dgg scale and its implementation to measure self-perception of teacher digital competencies and the digital gender gap in teacher training. Bordon. Revista de Pedagogia 75: 151–75. [Google Scholar] [CrossRef]
- Hallström, Jonas, Helene Elvstrand, and Kristina Hellberg. 2015. Gender and technology in free play in swedish early childhood education. International Journal of Technology and Design Education 25: 137–49. [Google Scholar] [CrossRef]
- Hosein, Anesa. 2019. Girls’ video gaming behaviour and undergraduate degree selection: A secondary data analysis approach. Computers in Human Behavior 91: 226–35. [Google Scholar] [CrossRef]
- Jaffe, Sarah. 2021. Work Won’t Love You Back: How Devotion to Our Jobs Keeps Us Exploited, Exhausted, and Alone. New York: Bold Type Books. [Google Scholar]
- Jarquín-Ramírez, Mauro Rafael, Héctor Alonso-Martínez, and Enrique Díez-Gutiérrez. 2024. Educational scope and limits of AI: Control and ideology in the use of ChatGPT. DIDAC 84: 84–102. [Google Scholar] [CrossRef]
- Katirai, Amelia, Noa Garcia, Kazuki Ide, Yuta Nakashima, and Atsuo Kishimoto. 2024. Situating the social issues of image generation models in the model life cycle: A sociotechnical approach. AI Ethics 5: 1769–86. [Google Scholar] [CrossRef]
- Kim, Byeongsu, Taehun Kim, and Jonghoon Kim. 2013. Paper-and-pencil programming strategy toward computational thinking for non-majors: Design your solution. Journal of Educational Computing Research 49: 437–59. [Google Scholar] [CrossRef]
- Lavalle, Ana, Miguel A. Teruel, Alejandro Maté, and Juan Trujillo. 2025. Study of gender perspective in stem degrees and its relationship with video games. Entertainment Computing 52: 100889. [Google Scholar] [CrossRef]
- Lunnemann, Per, Mogens H. Jensen, and Liselotte Jauffred. 2019. Gender bias in Nobel prizes. Palgrave Communications 5: 46. [Google Scholar] [CrossRef]
- Lusa, Amaia, Marta Peña, and Elisabet Mas de les Valls. 2024. Including gender dimension in operations management teaching. Journal of Industrial Engineering and Management 17: 373–84. [Google Scholar] [CrossRef]
- Machado, Mônica da Consolação, and Lucila Ishitani. 2024. Recommendations for games to attract women to computing courses. Entertainment Computing 50: 100633. [Google Scholar] [CrossRef]
- Marco-Simó, Josep Maria, María-Jesús Marco-Galindo, Elena Planas Hortal, and María José García García. 2023. Alignment of the institutional strategy with the teaching action in the implementation of the gender perspective. design and implementation in the case of the UOC. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje 18: 374–83. [Google Scholar] [CrossRef]
- Mateos Sillero, Sara, and Clara Gómez Hernández. 2019. Libro Blanco de las Mujeres en el Ámbito Tecnológico. Madrid: Ministerio de Economía, Comercio y Empresa. [Google Scholar]
- Mbaidin, Almoutaz, Sonia Rábade-Uberos, Rosario Dominguez-Petit, Andrés Villaverde, María Encarnación Gónzalez-Rufino, Arno Formella, Manuel Fernández-Delgado, and Eva Cernadas. 2021. STERapp: Semiautomatic software for stereological analysis. application in the estimation of fish fecundity. Electronics 10: 1432. [Google Scholar] [CrossRef]
- Merayo, Noemí, and Alba Ayuso. 2023. Analysis of barriers, supports and gender gap in the choice of stem studies in secondary education. International Journal of Technology and Design Education 33: 1471–98. [Google Scholar] [CrossRef] [PubMed]
- Meza-Mejia, Mónica del Carmen, Mónica Adriana Villarreal-García, and Claudia Fabiola Ortega-Barba. 2023. Women and leadership in higher education: A systematic review. Social Science 12: 555. [Google Scholar] [CrossRef]
- Mihura López, Rocío, Teresa Piñeiro Otero, and Antonio Seoane Nolasco. 2023. ‘No soy una gamer’ sexismo, misoginia y toxicidad como moduladores de la experiencia de las mujeres videojugadoras. Investigaciones Feministas 14: 215–27. [Google Scholar] [CrossRef]
- Msambwa, Msafiri M., Kangwa Daniel, Cai Lianyu, and Antony Fute. 2024. A systematic review of the factors affecting girls’ participation in science, technology, engineering, and mathematics subjects. Computer Applications in Engineering Education 32: e22707. [Google Scholar] [CrossRef]
- Oreskes, Naomi. 2020. Racism and sexism in science haven’t disappeared. Scientific American 323: 81. [Google Scholar] [CrossRef]
- Paderewski-Rodríguez, Patricia, María Isabel García-Arenas, Rosa María Gil-Iranzo, Carina S. González, Eva M. Ortigosa, and Natalia Padilla-Zea. 2017. Initiatives and strategies to encourage women into engineering. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 12: 106–14. [Google Scholar] [CrossRef]
- Peña, Marta, Noelia Olmedo-Torre, Elisabet Mas de les Valls, and Amaia Lusa. 2021. Introducing and evaluating the effective inclusion of gender dimension in stem higher education. Sustainability 13: 4994. [Google Scholar] [CrossRef]
- Pérez-Sánchez, Beatriz, and Noelia Sánchez-Maroño. 2023. Gender sensitive content in machine learning subjects. Paper presented at the XVII International Technology, Education and Development Conference, Valencia, Spain, March 6–8; pp. 2382–90. [Google Scholar] [CrossRef]
- Prendes-Espinosa, M., P. García-Tudela, and I. Solano-Fernández. 2020. Gender equality and ict in the context of formal education: A systematic review. Comunicar 63: 9–20. [Google Scholar] [CrossRef]
- Queralt Jiménez, Argelia. 2024. Desinformación por razón de sexo y redes sociales. Gendered disinformation and social networks. International Journal of Constitutional Law 21: 1589–619. [Google Scholar] [CrossRef]
- Regueira, Uxía, and Almudena Alonso-Ferreiro. 2022. La competencia digital del alumnado de Educación Primaria desde la perspectiva de género: Conocimientos, actitudes y prácticas. Estudios Sobre Educación 42: 57–77. [Google Scholar] [CrossRef]
- Regueira, Uxía, Angela González, and Adriana Gewerc. 2023. Tecnoloxía educativa con lentes violetas: Unha experiencia de ensino-aprendizaxe na universidade. In Epistemoloxías feministas en acción: VIII Xornada Universitaria Galega en Xénero. Pontevedra: Universidade de Vigo, pp. 61–72. [Google Scholar]
- Reverter, Sonia. 2024. Why does the inequality between women and men persist in Spanish universities? Cuadernos de Relaciones Laborales 42: 269–86. [Google Scholar] [CrossRef]
- Rodríguez-Jaume, María José, and Diana Gil-González. 2024. Innovación de Género en la Docencia Universitaria (Gender Innovation in University Teaching). Revista Latinoamericana de Educación Inclusiva 18: 167–82. [Google Scholar] [CrossRef]
- Rueda-Pascual, Silvia, Mariano Pérez-Martínez, Miriam Gil-Pascual, Ignacio Panach-Navarrete, and Sergio Casas-Yrurzum. 2021. Including gender perspective in a computer engineering degree. Paper presented at the XI International Conference on Virtual Campus., Salamanca, Spain, September 30–October 1; pp. 1–4. [Google Scholar] [CrossRef]
- RUIGEU. 2022. Las políticas de igualdad universitarias: Diagnóstico de los grupos de trabajo. In Informe de la Red de Unidades de Igualdad de Género para la Excelencia Universitaria. Red de Unidades de Igualdad de Género para la Excelencia Universitaria. Available online: https://redined.educacion.gob.es/xmlui/handle/11162/242904 (accessed on 12 June 2025).
- Samper-Gras, Teresa. 2022. A lo importante, ya van ellos. una propuesta contextual desde los nuevos materialismos para comprender por qué hay tan pocas mujeres en ciencias técnicas. Cuestiones de Género: De la Igualdad y la Diferencia 17: 209–31. [Google Scholar] [CrossRef]
- Sandoval-Martin, Teresa, and Ester Martínez-Sanzo. 2024. Perpetuation of Gender Bias in Visual Representation of Professions in the Generative AI Tools DALL·E and Bing Image Creator. Social Science 13: 250. [Google Scholar] [CrossRef]
- Sánchez-Canut, Sònia, Mireia Usart-Rodríguez, Beatriz Lores-Gómez, and Sonia Martínez-Requejo. 2025. Gender gap in professional digital competence: Construction and initial validation of an instrument for its measurement. Feminismo/s 45: 139–72. [Google Scholar] [CrossRef]
- Schiebinger, Londa. 2021. Gendered innovations: Integrating sex, gender, and intersectional analysis into science, health & medicine, engineering, and environment. Tapuya: Latin American Science, Technology and Society 4: 1867420. [Google Scholar] [CrossRef]
- Sepúlveda Durán, Carmen Mª, Azahara Arévalo Galán, and Cristina Maria García Fernández. 2025. Pensamiento computacional desenchufado en educación primaria: Una propuesta en el ámbito steam desde el desarrollo de la expresión corporal. RETOS 67: 12–26. [Google Scholar] [CrossRef]
- Shrestha, Sunny, and Sanchari Das. 2022. Exploring gender biases in ML and AI academic research through systematic literature review. Frontiers in Artificial Intelligence 5: 976838. [Google Scholar] [CrossRef]
- Shute, Valerie J., Chen Sun, and Jodi Asbell-Clarke. 2017. Demystifying computational thinking. Educational Research Review 22: 142–58. [Google Scholar] [CrossRef]
- Simon, Vivian, Neta Rabin, and Hila Chalutz-Ben Gal. 2023. Utilizing data driven methods to identify gender bias in LinkedIn profiles. Information Processing and Management 60: 103423. [Google Scholar] [CrossRef]
- Taboada, Maite. 2024. Reported speech and gender in the news: Who is quoted, how are they quoted, and why it matters. Discourse & Communication 19: 93–113. [Google Scholar] [CrossRef]
- Tomassini, Cecilia. 2021. Gender gaps in science: Systematic review of the main explanations and research agenda. Education in The Knowledge Society 22: 25437. [Google Scholar] [CrossRef]
- Tortajada, Iolanda, and Teresa Vera. 2021. Presentación del monográfico: Feminismo, misoginia y redes sociales. Investigaciones Feministas 12: 1–4. [Google Scholar] [CrossRef]
- UN. 2015. United Nations: Transforming Our World: The 2030 Agenda for Sustainable Development. Available online: https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_70_1_E.pdf (accessed on 12 June 2025).
- UNESCO. 2023. Harnessing the Era of Artificial Intelligence in Higher Education: A Primer for Higher Education Stakeholders. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000386670 (accessed on 12 June 2025).
- UNESCO. 2024a. Challenging Systematic Prejudices: An Investigation into Gender Bias in Large Language Models. Technical Report. International Research Center of Artificial Intelligence (IRCAI). Available online: https://unesdoc.unesco.org/ark:/48223/pf0000388971 (accessed on 12 June 2025).
- UNESCO. 2024b. Changing the Equation: Securing STEM Futures for Women. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000391384 (accessed on 12 June 2025).
- Unidad Mujeres y Ciencia. 2025. Científicas en Cifras 2025. Available online: https://www.ciencia.gob.es/Secc-Servicios/Igualdad/CientificasCifras.html (accessed on 12 June 2025).
- Valdeolivas, M. Gracia, M. Ángeles LLopis, Francesc Esteve, and Virginia Viñoles. 2022. La robótica educativa con perspectiva de género: Una investigación colaborativa universidad-escuela. In Edutec 2022 Palma-XXV Congreso Internacional. Palma de Mallorca: EDUTEC, pp. 2018–20. [Google Scholar]
- Veliz, Carissa. 2021. Privacy is Power: Why and How You Should Take Back Control of Your Data. London: Transworld Publishers LTD. [Google Scholar]
- Vicente, Lucia, and Helena Matute. 2023. Humans inherit artificial intelligence biases. Scientific Reports 13: 15737. [Google Scholar] [CrossRef]
- Wing, Jeannette M. 2006. Computational thinking. Communications of the ACM 49: 33–35. [Google Scholar] [CrossRef]
- World Economic Forum. 2022. Global Gender Gap Report. Available online: https://www.weforum.org/publications/global-gender-gap-report-2022 (accessed on 12 June 2025).
- Wu, Yankun, Yuta Nakashima, and Noa Garcia. 2025. Revealing gender bias from prompt to image in stable diffusion. Journal of Imaging 11: 35. [Google Scholar] [CrossRef]
- Zack, Travis, Eric Lehman, Mirac Suzgun, Jorge A. Rodriguez, Leo Anthony Celi, Judy Gichoya, Dan Jurafsky, Peter Szolovits, David W. Bates, Raja-Elie E. Abdulnour, and et al. 2024. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: A model evaluation study. The Lancet Digital Health 6: e12–e22. [Google Scholar] [CrossRef]
- Zuboff, Shoshana. 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. London: Profile Books. [Google Scholar]
Questionnaire | Plenary | Course |
---|---|---|
No. Answers | 353 | 9 |
Question | Score (0–5) | |
Adaptation of content, time, and media to the attendee’s needs | 3.89 | 3.88 |
Mastery of the subject taught | 4.38 | 4.38 |
Communication skills | 3.93 | 4.14 |
Methodological adequacy and resources used | 3.90 | 3.71 |
Average | 4.03 | 4.02 |
Deviation | 0.24 | 0.29 |
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Cernadas, E.; Aguayo, E.; Fernández-Delgado, M.; Calvo-Iglesias, E. STEM, a Non-Place for Women? Evidences and Transformative Initiatives. Soc. Sci. 2025, 14, 384. https://doi.org/10.3390/socsci14060384
Cernadas E, Aguayo E, Fernández-Delgado M, Calvo-Iglesias E. STEM, a Non-Place for Women? Evidences and Transformative Initiatives. Social Sciences. 2025; 14(6):384. https://doi.org/10.3390/socsci14060384
Chicago/Turabian StyleCernadas, Eva, Eva Aguayo, Manuel Fernández-Delgado, and Encina Calvo-Iglesias. 2025. "STEM, a Non-Place for Women? Evidences and Transformative Initiatives" Social Sciences 14, no. 6: 384. https://doi.org/10.3390/socsci14060384
APA StyleCernadas, E., Aguayo, E., Fernández-Delgado, M., & Calvo-Iglesias, E. (2025). STEM, a Non-Place for Women? Evidences and Transformative Initiatives. Social Sciences, 14(6), 384. https://doi.org/10.3390/socsci14060384