Work in Progress: Intersectionality, Competences and Engineering: An Exploratory Study in Tec 21

Intersectionality, the convergence of social categories leading to unique forms of oppression, has significant implications, particularly in STEM fields, where factors like gender, race, and social status intersect. However, there is a notable research gap in Latin America regarding the impact of intersectionality on educational achievement. This study focuses on unraveling the intersectionality of social categories among Engineering students at Tecnologico de Monterrey and its influence on academic performance. To achieve this, advanced AI techniques, including hierarchical clustering and K-means clustering, will be employed to discern key characteristics within the student population. Additionally, several regression models will be developed to predict academic grades, considering factors such as race, gender, and socioeconomic background-the elements of our social categorization. Longitudinal data from an Artifact Design Course will be analyzed. This research promises valuable insights into how intersectionality shapes student academic achievement, potentially benefiting educational institutions worldwide.