An analysis of institutional data to understand the outcome of obstacles faced by students from historically disadvantaged backgrounds is important in order to work toward promoting equity and inclusion. We use 10 years of institutional data at a large public research university to investigate the grades earned by students categorized on four demographic characteristics: gender, race/ethnicity, low-income status, and first-generation college student status. We find that on average across all years of study, underrepresented minority (URM) students experience a larger penalty to their mean overall and STEM GPA than even the most disadvantaged non-URM students. Moreover, the URM students with additional disadvantages due to socioeconomic status or first-generation college status were further penalized in their average GPA. These inequitable outcomes point to systemic inequities in higher education for students with historically disadvantaged backgrounds and the need to dismantle institutional inertia to support them.
A recent paper used structural equation modeling to infer effects of gender-dependent student attitudes on several outcomes of introductory physics courses. The model used is precisely Markov equivalent to an equally plausible approximation in which a key gender-dependent coefficient in determining attitudes is indistinguishable from zero. I also argue that the qualitative framing may attribute to students motivations that actually belong to their teachers.
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