RT info:eu-repo/semantics/article
T1 Identifying beliefs about the gender gap in engineering professions among university students using community detection algorithms and statistical analysis
A1 Merayo Álvarez, Noemí
A1 Ayuso Lanchares, Alba
K1 community detection algorithms
K1 engineering education
K1 engineering workforce
K1 genderequality
K1 professional dimension
K1 STEM
K1 1203 Ciencia de Los Ordenadores
K1 3304 Tecnología de Los Ordenadores
K1 6306.02 Sociología Educativa
AB 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.
PB Wiley
SN 1061-3773
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/75166
UL https://uvadoc.uva.es/handle/10324/75166
LA eng
NO Computer Applications in Engineering Education, 2024, vol. 32, n.4, e22751
NO Producción Científica
DS UVaDOC
RD 07-abr-2025