Navigating Intersecting Identities of Historically Excluded Groups and Post-traditional Students in Engineering
Conference
Long, H, Secules, S, Liu, J et al. (2025). Navigating Intersecting Identities of Historically Excluded Groups and Post-traditional Students in Engineering
. 10.18260/1-2--56992
Long, H, Secules, S, Liu, J et al. (2025). Navigating Intersecting Identities of Historically Excluded Groups and Post-traditional Students in Engineering
. 10.18260/1-2--56992
The purpose of this WIP research is to examine the intersectionality of traditionally examined broadening participation in engineering demographics (i.e., race, socio-economic status) with post-traditional student status and categories. Engineering education has been historically exclusive to racial groups such as Black and Latinx students, and lower socioeconomic status students. While broadening participation often focuses on cultural marginalization of these student groups, there are other broader structural issues and life circumstances that affect their educational access and outcomes. In general, and in this study, we aim to further establish how Black, Latinx, and lower socioeconomic status students are more likely to study part-time, be older, be a parent, and support others while attending school—in short, they are more likely to be “post-traditional” students. While higher education literature has interrogated these post traditional student categories more thoroughly, engineering education has done less to establish and interrogate this intersection. More specifically, in this study, we focus on 1) classifying post-traditional students in terms of categories and extents of post-traditional status, 2) examine the intersectionality of the post traditional population with other historically excluded demographic groups, and 3) assess the educational outcomes for this intersectional and underserved population. We draw on intersectionality theory and Choy’s [1] post-traditional student status classifications to operationalize the analytical categories and procedures for our quantitative study. We utilize the de-identified institutional data from undergraduate engineering students enrolled during the 2023-2024 academic year at a large Hispanic-Serving Institution in the Southeastern United States and employ descriptive statistics, mean difference tests, and linear and logistic regressions to address our research purposes.
