2023 ASEE Annual Conference & Exposition

The number of neurodivergent students enrolling in undergraduate engineering programs is increasing, yet they still remain underrepresented; and their retention and graduation rates remain lower than their non-neurodiverse peers. As modern society continues to contend with global challenges like climate change and population growth, the field of engineering must comprise a diverse group of engineers equipped to find innovative ways to address these challenges (The National Science Foundation [NSF], 2019). One way for this to occur is for accredited institutions of higher education to ensure they are producing a diverse set of graduates that enter the engineering workforce. Federal agencies such as The National Science Foundation (NSF), have reported that women, racial/ethnic minorities, and persons with dis/abilities are underrepresented in science and engineering fields, both within education and within industry (NSF, 2019). Further, these federal agencies often issue calls for institutions of higher learning to diversify their programs by increasing the enrollment numbers of these groups of students (Thurston et al., 2017; Wulf, 2002). While the enrollment numbers of women, racial/ethnic minorities, and persons with disabilities in postsecondary science, technology, engineering, and mathematics (STEM) programs have indeed increased, the persistence and graduation rates of certain underrepresented groups remain low in comparison to their peers (National Science Academies Engineering [NSA], 2016; Thurston, 2017;). Students of color, women, and neurodivergent students, in particular, face many barriers within STEM programs that shape their educational experiences and outcomes. Interestingly, there is a robust set of literature that examines the experiences and factors that impact academic outcomes for women and students of color pursuing undergraduate degrees in STEM fields; however, there is little work that focuses on neurodivergent students (Lee, 2014). In this explanatory sequential mixed methods work in progress, we seek to expand the current literature through understanding neurodivergent students' sense of belonging at the university, engineering major, and course level. In this two-phase design, quantitative data was collected in phase one to determine if there is a difference between neurodivergent students' sense of belonging at each level and students with other dis/abilities and students who identified as having no dis/ability. Univariate and descriptive analysis provided insight that informs phase two of this study where we will conduct qualitative data collection and analysis to holistically understand neurodivergent students’ sense of belonging within each level and the factors that might shape their belonging. We discuss data integration following the analysis of quantitative and qualitative data and additional study considerations, such as potential ethical issues and summary of resources needed.