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Título
Identifying beliefs about the gender gap in engineering professions among university students using community detection algorithms and statistical analysis
Año del Documento
2024
Editorial
Wiley
Descripción
Producción Científica
Documento Fuente
Computer Applications in Engineering Education, 2024, vol. 32, n.4, e22751
Resumen
Digital societies require professionals in the Technology and Engineering sectors, but their lack, particularly of women, requires a thorough understanding of this gender gap. This research analyzes the beliefs and opinions of university engineering students about the gender gap in their professional fields by means of a community detection algorithm to identify groups of students with similar belief patterns. This study leverages a community detection algorithm to analyze the beliefs of 590 engineering students regarding the gender gap in their field, together with a correlational and explanatory design using a quantitative paradigm. A validated questionnaire focusing on the professional dimension was used. The algorithm identified three student communities, two gender-sensitive and one gender-insensitive. The study uncovered a concerning lack of awareness regarding the gender gap among engineering students. Many participants did not recognize the importance of increasing the representation of professional women, maintained the belief that the gender gap affects only women, and assumed that men and women are equally paid. However, women show a higher level of awareness, while men perceive the gender gap as a passing trend, which is worrying. Students recognize the importance of integrating a gender perspective into university and engineering curricula. It is worrying that many students doubt the existence of the gender gap and that both genders lack knowledge about gender gap issues. Finally, community detection algorithms could efficiently and automatically analyze gender gap issues or other unrelated topics.
Materias Unesco
1203 Ciencia de Los Ordenadores
3304 Tecnología de Los Ordenadores
6306.02 Sociología Educativa
Palabras Clave
community detection algorithms
engineering education
engineering workforce
genderequality
professional dimension
STEM
ISSN
1061-3773
Revisión por pares
SI
Patrocinador
Universida de Valladolid (PID InGenias: Women as a Precursor to Technological and Scientific Vocations)
Version del Editor
Propietario de los Derechos
© 2024 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
Aparece en las colecciones
Ficheros en el ítem
Tamaño:
1.110Mb
Formato:
Adobe PDF
