The developmental processes underlying in-group-favoring math stereotypes (i.e., beliefs that favor one's own gender over another gender in math ability) and their associated motivational benefits in adolescence remain understudied. Guided by developmental intergroup theory and situated expectancy-value theory, the current study examined (a) the reciprocal relations between in-group–favoring math stereotypes and perceived math teacher support; (b) their associations with math motivational outcomes (i.e., math expectancies, math identity, and math GPA); and (c) gender differences in these associations among adolescents within each of four largest racial/ethnic groups in the United States (i.e., Asian, Black, Latine, and White). Using the High School Longitudinal Study of 2009 (N=18,419; Mage=14.53, SD=0.63 in 9th grade), we found that Asian and White girls' female-favoring math stereotypes and their perceived math teacher support reinforced one another from early to late adolescence, but not among other groups. In-group-favoring math stereotypes positively predicted math motivational beliefs among boys across all racial/ethnic groups, but only among Black and White girls. These stereotypes indirectly predicted math GPA via motivational beliefs only for White adolescents. Findings suggest that gender differences in the development of in-group-favoring math stereotypes and the benefits of stereotype lift are not the same across race/ethnicity. Rather, gender norms that vary across different racial/ethnic groups may shape who develops and benefits from in-group-favoring math stereotypes, underscoring the need to consider intersectionality to better capture motivational processes in real-world contexts.

Although it is well known that Black women are underrepresented in computing, less is known about their pre-college experiences. We hypothesize that inequities at the K-12 level result in Black women's underrepresentation in computing, because Black women have accumulated less social capital and are less embedded in courses and organizations related to computing prior to college. This paper reports the initial findings from the first round of a survey designed to gather the pre-college computing experiences of Black women and their peers. Black women in our sample were less likely to report participating in formal computer science (CS) education in school, slightly more likely to report participation in outside-of-school computing programs, about equally as likely to pursue computing experiences independently, and more likely to have had no pre-college computing experiences at all. We found that Black women were less likely to report that they were told they would be a good computer scientist, especially by friends, teachers, and guidance counselors, thus reflecting weaker social connections and lower levels of social capital. These findings suggest that organizational embeddedness or social ties from pre-college computing experiences may indeed be a factor in Black women's underrepresentation in computing and that access to these experiences outside of the formal classroom may be particularly important. The survey is one part of a study that will feature a second round of data collection in another state, analysis of state-level longitudinal data, and interviews with Black women.

The underrepresentation of Black women in college computing programs is well known but not well understood, as enrollment reports often fail to account for the complexity inherent in this issue. We analyzed longitudinal data for a cohort of public school students from sixth grade through college enrollment in Texas (N=266,062), distinguishing between institution admission and program participation, 2- and 4-year programs, and types of computing and computing-related programs. Results suggest that the underrepresentation of Black women in computing programs may be less of an institutional admissions problem and more of a department culture problem. We leverage these results to examine the tension between expanding Black women's participation in college computing programs, effective affirmative action practices, and the recent rampant endorsement of anti-DEI policies. We conclude by offering practical approaches to addressing race and gender disparities in computing in light of the constraints of anti-DEI legislation.

This experience report shares lessons learned when expanding demographic options on an undergraduate survey. The study is designed to better understand the relationship between pre-college computing experiences and the choice to major in computing, particularly focusing on Black women's experiences. Expansive options for gender (5 plus an open-ended), race (18 non-mutually-exclusive options), and disability (8) gave respondents more opportunity for specificity. Yet we faced unexpected challenges in analysis and interpretation as we hadn't considered the implications of being so expansive ahead of time. This paper presents our lessons learned, analysis choices and plans for future iterations of the survey.