Competencies assessment: Indicators for a covariance structural model for STEM Academic Article in Scopus uri icon

abstract

  • During the last two decades, formal studies focused on explaining the gender gap in science, technology, engineering, and mathematics careers have increased exponentially. In parallel, the efforts and strategies of universities, international organizations e.g., the United Nations Agency for Education, Science and Culture, the European Union, and the World Economic Forum, and women's associations have emphasized the under representation of females in science and engineering professions. However, the efforts to reduce the gender gap remain insufficient, and increasing female underrepresentation in STEM careers continues to be a big challenge. Information technology careers present a much more dramatic gender gap when considering the availability of professionals for the jobs that will be required in the future for this industry. This paper is an evolving work which aims to propose more effective strategies that can positively impact women's recruitment in STEM intensive careers in the medium and long term. As a first approach, we have collected data from a group of young women who chose an engineering major, and we measured their level of proficiency in 10 competencies at the beginning of their undergraduate studies and two years later, after they have followed STEM courses. Using traditional statistical methods such as correlations, cluster analysis, and parametric hypothesis testing, we found that their competencies evolved and clustered following trends and could serve as indicators that measure latent variables of a structural model of covariance. Our goal is to generate enough data to build such a model that explains why a woman might choose or avoid STEM careers. © 2023 IEEE.

publication date

  • January 1, 2023