Toward “Inclusifying” the Underrepresented Minority in STEM Education Research

Abstract

Research in undergraduate STEM education often requires the collection of student demographic data to assess outcomes related to diversity, equity, and inclusion. Unfortunately, this collection of demographic data continues to be constrained by socially constructed categories of race and ethnicity, leading to problematic panethnic groupings such as “Asian” and “Latinx.” Furthermore, these all-encompassing categories of race and ethnicity exasperate the problematic “underrepresented minority” (URM) label when only specific races and ethnicities are categorized as URMs.Research in undergraduate STEM education often requires the collection of student demographic data to assess outcomes related to diversity, equity, and inclusion. Unfortunately, this collection of demographic data continues to be constrained by socially constructed categories of race and ethnicity, leading to problematic panethnic groupings such as “Asian” and “Latinx.” Furthermore, these all-encompassing categories of race and ethnicity exasperate the problematic “underrepresented minority” (URM) label when only specific races and ethnicities are categorized as URMs. We have long seen calls for improved outcomes related to URMs in undergraduate STEM education, but seldom have we seen our own understanding of what it means to be a URM go beyond socially constructed categories of race and ethnicity. If we aim to not only improve diversity outcomes but also make undergraduate STEM education more equitable and inclusive, we must reevaluate our use of the term “URM” and its implications for demographic data collection. The classifications of “underrepresented” and “minority” are more nuanced than simple racial categories. Though there has been development of alternative terms to URM, each with their own affordances, the main goal of this article is not to advocate for one term over another but rather to spark a much-needed dialogue on how we can “inclusify” our collection of racial and ethnic demographic data, particularly through data disaggregation and expanding our definition of what it means to be both “underrepresented” and a “minority” within STEM.