Trajectories of math expectancies for success and values in Latinx and Asian students.

Abstract

The heterogeneity in the developmental trajectories of math motivational beliefs (i.e., expectancies for success and subjective task value beliefs) was examined among Asian and Latinx male and female students from Southern California across Grades 8 through 10 (n = 2,710; 50% female; 85% Latinx; 15% Asian; M age = 13.77). By conducting growth mixture modeling, we identified two classes of stable trajectories for expectancies for success; five classes of stable, decreasing, or increasing trajectories for interest and utility value; and three classes of stable, decreasing, or increasing trajectories for attainment value. The group comparisons demonstrated that variability exists in adolescents' motivational belief development at the intersection of their race/ethnicity and gender for some trajectories. For example, Latina adolescents were more likely to maintain moderate expectancies for success than high expectancies for success compared to Latino and Asian male adolescents, but Asian female adolescents did not differ in their level of expectancies for success from the two male groups. Also, we found Latina adolescents displayed smaller decreases in interest compared to Asian female adolescents and in utility value compared to Latino adolescents. The findings from the present study challenge traditional stereotypes in math and highlight positive motivational belief development in students who are marginalized in math (e.g., Latina adolescents). Public Significance StatementThis study suggests that there are multiple, distinct patterns of students' math motivational belief development during the transition from middle to high school and that Asian/Latinx male/female students do not always display decreases in their motivational beliefs across adolescence. Our findings suggest that interventional efforts employed during middle and high school have the potential to foster students' motivational beliefs. Additionally, our findings help guide applied efforts to address the societal and systematic challenges in science, technology, engineering, and math by displaying the issues of marginalization and privilege based on race/ethnicity and gender.