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Abstract This study investigates the racial-mathematical identity profiles of Black American adolescents. Survey data were collected in five schools across one U.S. urban school district at two time points (spring 2018 [n = 197] and spring 2019 [n = 210]). Based on extant research regarding psychological response patterns to racialized school stress, we investigated the existence of an identity negotiation pattern in which students were motivated to resist negative stereotypes about Black people by achieving well in mathematics. We conducted a latent profile analysis combining students’ self-beliefs across five indicators: racial centrality, racial public regard, mathematics attainment value, mathematics mastery experiences, and resistance motivation. Three distinct racial-mathematical identity profiles emerged: (1) Mathematics Devalued/Ambivalent, (2) Moderately Math Attained, and (3) Resistors. We found associations between profile membership and students’ gender, negative math emotions, and their receipt of cultural and critical mathematics instruction. We also found an association between the identity profiles and school type (academically selective “magnet” schools vs. open-enrollment neighborhood schools), but not in the direction that might be assumed. Moreover, we found that certain school environment factors (i.e., racial stereotyping and cultural and critical mathematics instruction) were significantly different in racially diverse magnet schools than in the neighborhood schools. Overall, our data reveal the existence of a highly motivated Resistor profile among Black students, that is predicted by cultural and critical mathematics instruction but underrepresented within this district’s selective magnet schools. 

Abstract We present a classroom‐ready activity for high school or college students involving an investigation of a rich, multivariate data set concerning educational and demographic characteristics of K‐12 schools at the state level in the United States. The data set includes educational characteristics such as per‐pupil revenue and graduation rate along with demographic characteristics such as poverty rate and economic segregation. Analysis of the data set sparks conversations regarding educational disparities for students in the United States and could prompt curiosity about educational disparities in other locations as well.