Women remain problematically underrepresented in computing fields. Guided by the STEM momentum framework, we leverage Florida administrative data to investigate the relationship between community college students' gender, advanced high school STEM coursework, and their pathways to postsecondary degrees. We find that advanced high school coursework functions differently by gender and STEM field. Notably, completing computing courses and advanced mathematics courses especially promotes community college women's selection of STEM majors and completion of computing degrees. Implications for advanced high school course offerings are discussed with respect to research and policy, especially with respect to enhancing opportunities for community college students.

Gender disparities persist in postsecondary computing fields, despite improvements in postsecondary equity overall and STEM fields as an aggregate. The entrenchment of this issue requires a comprehensive, longitudinal lens. Building on expectancy-value theory, the present study examines the relationships among students’ gender-ability stereotypes, attainment values, course-taking, and major choices. Using data from the High School Longitudinal Study of 2009 (HSLS: 2009), we applied weighted t-tests and multiple-group structural equation modeling to investigate how motivational beliefs (i.e., gender-ability stereotypes, attainment values) and course-taking patterns in math and science may predict major choice in computing. Overall, we find gender differences in identity-based mathematics and science motivational beliefs have long-term effects. Gender-ability stereotypes in math and science shape attainment values in each domain, whereby stereotypes suppress girls’ attainment values and enhance boys? attainment values (p < 0.001), in turn shaping course-taking and major decisions. Math- and sciencerelated motivational and curricular factors affect “other” STEM more than computing major outcomes. Specifically, computer science course-taking is completed more by boys (d = 0.21), but girls’ chances of declaring computing majors are especially enhanced by completing these courses in high school. Advanced science course-taking and science attainment value positively predict boys’ but not girls’ likelihood of declaring computing majors. We discuss the implications of these findings for research, policy, and practice. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.

Integrating microintervention strategies and the bystander intervention model, we examined social cognitive predictors (i.e., moral disengagement, empathy, and self-efficacy) of the five steps of the bystander intervention model (i.e., Notice, Interpret, Accept, Know, and Act) to address racial microaggressions in a sample of 452 racially diverse college students. Data were collected using an online survey. Path analyses showed that moral disengagement was significantly and negatively related to each step of the model for White students, but for students of color, it was only significantly negatively associated with Act. Empathy was significantly and positively associated with Interpret, Accept, and Act for White students. For student of color, however, there was a significant and positive association solely between Empathy and Act. For both White students and students of color, self-efficacy was positively associated with Notice, Interpret, Accept, Know, and Act. Finally, race did not significantly moderate any relationships. Strengths, limitations, future directions for research, and implications of the study findings are discussed.

The role of social media in the global society and economy has increased substantially over the years across different populations, age groups, and contexts. Using social media can be both beneficial and risky. People's ability to navigate it safely and effectively largely depends on their social media literacy skills, which has significant implications for individuals and society. Drawing on the critical literacy and affordances-in-practice frameworks, we explore the concept of critical social media literacy (CSML) through a systematic literature review to determine whether and how its components—users' goals, use context, inquiry, reflection, and action—have been addressed in the literature. Twenty out of the 50 publications identified reflected all the components of the CSML framework at different levels. We focused on empirical studies to identify instructional approaches and recommendations to support the development of CSML skills. Implications for research and practice, including curriculum connections, are also discussed.

While it is well understood that gender and racial stratification in computing fields continues to exist, we too often approach this workforce problem from a deficit perspective and look to typical sites (where status quo norms are reproduced) to investigate potential solutions. Using an intersectional descriptive analysis on state administrative data on Florida state colleges, we prioritize centering the academic journeys of Asian, Black, Indigenous, Latina/e/o/x, and Multiracial community college women and men, addressing the often-overlooked effects of holding multiple marginalized identities in quantitative research. Analytic results from this study suggest that women of color completed high school computing courses significantly less often than men. Further, our findings reveal that these disparities in high school computing coursetaking were even greater among those who enrolled in Advanced Placement Computer Science. Employing an intersectional approach enabled this study to dive into these and other nuanced effects. Implications offer directions for future research, practice, and policy that may enhance efforts to broaden participation in computing in high school and among community college students.

To contribute to the scant yet growing literature on Southeast Asian students within college STEM, this quantitative study investigated factors that were most salient in science self-efficacy development of these students, which prior scholarship suggests is significant for various STEM success outcomes. Using descriptive and predictive statistical approaches on a multi-institution and nationwide longitudinal sample of first-year STEM students, results suggest that a unique set of predictors are associated with Southeast Asian students' science self-efficacy development when compared to Asian American and Pacific Islander (AAPI) students in the aggregate. Further, results point to the importance of disaggregating AAPI racial/ethnic data in research to reveal factors that are distinct to each AAPI ethnic subgroup and to dismantle the narrative that AAPI students are a monolithic group. Findings suggest that pedagogical approaches that employ community building among faculty and students within the classroom are associated with Southeast Asian students' science self-efficacy. Guided by these findings, implications for theory, practice, and policy that center Southeast Asian students and their development within STEM are offered to inform relevant future research, program plans, and policy recommendations.

Although gender parity has been achieved in some STEM fields, gender disparities persist in computing, one of the fastest-growing and highest-earning career fields. In this systematic literature review, we expand upon academic momentum theory to categorize computing interventions intended to make computing environments more inclusive to girls and women and consider how those characteristics vary by the success of the intervention. Particular attention is given to the efficacy of broadening participation and success for women in computer science, information technology, and related fields. After scrutinizing 168 relevant studies, 48 met the inclusion criteria and were included. We introduce a framework for gender equity in computing, expanding on existing research on academic and STEM momentum to encompass new domains representing social and structural momentum. Our analysis reveals the complex roles of intervention domains, strategies, goals, levels, and duration in shaping their efficacy. Implications for theory, research, and practice are discussed.

Limited literature has investigated the effects of state and institutional merit-based financial aid on student choice of science, technology, engineering, and mathematics (STEM) major fields, an unintended consequence with important implications. By leveraging nationally representative longitudinal data from the Beginning Postsecondary Students, we examined these effects-respectively and jointly-with logistic regressions (LR) and propensity score matching (PSM). Both the LR and PSM results showed that students who receive both state- and institution-awarded merit aid were more likely to major in STEM. For students who only received state-awarded merit aid, the PSM presented significant and positive effects while the LR did not. Institution-only merit aid had no statistically measurable effect. We discuss implications for research, policy, and practice for state- and institution-based financial aid.