Trajectories of Advanced Math Taking for Low-income Students of Color in Middle and High School

Courtney Ricciardi

Major Professor: Adam Winsler, PhD, Department of Psychology

Committee Members: Olga Kornienko, Toya Frank

Online Location, Virtual -
April 16, 2021, 10:00 AM to 12:00 PM


Differential access to and enrollment in advanced mathematics for historically underrepresented groups is a pervasive problem in education. Access to advanced coursework is associated with positive postsecondary outcomes including college acceptance, retention, graduation and more. However, current research examining this problem primarily focuses on achievement in this domain rather than access. This necessitates an examination of who is accessing advanced coursework and what differentiates the course trajectories students follow across middle and high school.

This dissertation sought to explore what pathways students were following in advanced math coursework across middle and high school and how demographics, school readiness, and prior academic achievement related to pathway followed. These questions were answered using a large-scale (N = 18,841), majority Latinx (57.6% Latinx, 35.5% Black, 6.1% White/Other, and .6% Asian/Pacific Islander) and low-income sample (77.3% on free or reduced lunch in 6th grade) from the Miami School Readiness Project. Latent class analysis (LCA) was used to categorize students into 6 classes, which in this study represented the commonly followed pathways of advanced math taking. The 6 class pathways which emerged were Never Advanced (standard math in middle and high school), Early Advanced Tryer (advanced in middle school, standard in high school), Late Honors Tryer (standard in middle school, honors in high school), Consistently Mid-level Advanced (advanced in middle school, honors in high school), Primarily Advanced with some College Level (advanced in middle school, standard with some college level in high school), and Most/Earliest Advanced (honors in middle school, standard and some college level in high school).
Following this, multinomial logistic regression was used to connect individual demographics, school readiness, and prior academic achievement to the likelihood of being assigned to a particular class trajectory. Results revealed that prior academic performance was most strongly related to advanced math pathway assignment, but even when controlling for this variance, gender, special education status, and cognitive and fine motor skills at age 4 also impacted what math pathway a student was likely to follow in middle and high school. Race/ethnicity was a significant differentiator when comparing the two most advanced pathways. These findings highlight the importance of early school readiness skills and demonstrate how early opportunity gaps impact later student outcomes. Furthermore, this project illustrates how tailored intervention and supports are necessary to ensure equitable access to coursework and programming which expands a student’s opportunities and chances for postsecondary success.