Identifying beliefs about the gender gap in engineering professions among university students using community detection algorithms and statistical analysis

• Published in: Computer applications in engineering education 2024-07, Vol.32 (4), p.n/a
• Main Authors: Merayo, Noemi, Ayuso, Alba
• Format: Article
• Language: English
• Subjects: Algorithms; Colleges & universities; community detection algorithms; Engineering; Engineering education; Engineering profession; engineering workforce; Gender; Gender aspects; gender equality; Men; professional dimension; Statistical analysis; STEM; Students; Technology assessment; Women
• ISSN: 1061-3773; 1099-0542
• DOI: 10.1002/cae.22751

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.