Gender Inclusivity from the Perspective of Non-Cisgender Students

Abstract

Background. There are an estimated 13 Million individuals in the United States who identify as a LGBTQ+ (Conron & Goldberg, 2020). Yet, statistics textbooks and curricula remain largely heteronormative (Parise, 2021). While the statistics education community discusses ways to foster inclusivity, the experiences of queer and non-cisgender students in mathematics and statistics courses are critically understudied (Pinheiro, 2022). To wit, what pedagogical and curricular practices do queer and non-cisgender students find most and least inclusive? Methods. We conducted interviews and focus groups with non-cisgender identifying individuals to specifically ascertain their thoughts on the role that datasets and activities in statistics courses can serve to foster gender inclusivity. We analyzed all transcripts utilizing a grounded theory approach (Glaser & Strauss, 1967), along with member checks to ensure credibility of the results (Morse et al., 2002). Findings. Our results suggest that participants believed that gender inclusivity is fundamentally about humanizing data. They believed that humanizing should be done not by comparing cisgender and non-cisgender individuals, but by including all persons in an analysis, and through this, coming to appreciate the variability in gender identity. Implications. In this poster, we present the full framework generated by our study, and discuss how it can be utilized by teachers in their classrooms. Furthermore, based on this framework, we designed and are currently piloting classroom activities to promote gender inclusivity. More details about the activities and their classroom implementation will be separately presented in a beyond session at the USCOTS main session.