The social sciences can identify social inequalities, while computer science is able to scale the solutions. But scholars lack a framework to sufficiently combine the two, especially in inquiries concerned about race. Without intentionality, as scholars such as Safiya Nobles and Ruha Benjamin note, algorithms exacerbate racism by replicating racist structures and hierarchies. In this text, I propose a new framework, racialized computational thinking, where racial formation theory and critical race theory is used as part of the abstraction skill in computational thinking to identify systemic patterns. I expand Michael Omi and Howard Winant’s advancement of race formation theory to include lessons learned from scholars of Afro-Latinx identity formation, namely the importance of time and place as part of what is absent in computer science framings of race. I then apply this framework, racialized computational thinking, to college admissions to better understand how structural inequalities currently impact access to higher education for students racialized as Black and African American.