Numerous studies have examined “whether” and “why” policies diffuse, or the reasons for the adoption in a given government of a policy that exists in another government. This study explored the “how” of policy diffusion by focusing on college completion policies, especially performance funding. In particular, we examined the roles that intermediaries play in state-level college completion policy diffusion. Data are from 3 states and include observations of policy events, documents, and interviews with 56 participants, including state policy actors and intermediary representatives. This analysis, grounded in conceptual models of policy diffusion, revealed that diffusion occurs at various stages of the policy process, not just adoption. The study also demonstrated the coercive roles that intermediaries can play in promoting policies and revealed how intermediaries facilitate, and sometimes limit, policy learning, which is one of the primary mechanisms by which policies diffuse. By focusing on an underexplored conceptual model of policy diffusion, the national interaction model, this analysis shed light on the role played by intermediaries in state-level college completion policymaking.

Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine how the accuracy of college student success predictions differs between racialized groups, signaling algorithmic bias. We also evaluate the utility of leading bias-mitigating techniques in addressing this bias. Using nationally representative data from the Education Longitudinal Study of 2002 and various machine learning modeling approaches, we demonstrate how models incorporating commonly used features to predict college-student success are less accurate when predicting success for racially minoritized students. Common approaches to mitigating algorithmic bias are generally ineffective at eliminating disparities in prediction outcomes and accuracy between racialized groups.

Extant research neglects to examine how policymakers make decisions regarding funding allocations to higher education institutions. This case study analyzed the policy process surrounding the development of one model for funding higher education in Colorado. The study is anchored in a theory of policy design, which considers how groups targeted by a policy are socially constructed, as well as their levels of political power. Drawing on interviews with 19 policy actors involved in funding-model development and 144 documents, findings reveal that institutions‘ levels of political power are critical, but not deterministic, in explaining allocations under the new model. Access institutions benefited most from the state’s new funding model, primarily due to one policy entrepreneur’s interest in linking funding more tightly to higher education institutions‘ enrollments. Research institutions used their political power to repel burdens that would have been imposed through the funding-model bill. Finally, racial/ethnic minority students were ultimately denied explicit funding benefits. This outcome resulted from legislators’ overt efforts to avoid addressing race in this policy process. This study contributes to literature on higher education finance, policy, and politics by illuminating how target groups’ political power and social constructions influence decisions surrounding state funding for public colleges and universities.

Promise programs are proliferating across the United States, with wide variation in their design. Using national data on 33 Promise programs affecting single, 2-year colleges, this study examines program effects on first-time, full-time college enrollments of students by race/ethnicity and gender classification. Results suggest Promise programs are associated with large percent increases in enrollments of Black and Hispanic students, especially students classified as females, at eligible colleges. Promise programs with merit requirements are associated with higher enrollment of White and Asian, Native Hawaiian, or Pacific Islander female students; those with income requirements are negatively associated with enrollment of most demographic groups. More generous Promise programs are associated with greater enrollment increases among demographic groups with historically higher levels of postsecondary attainment.

Promise programs, or place-based tuition-free college policies, have become increasingly popular among policymakers looking to expand postsecondary attainment. In this article, we examine Tulsa Achieves, a widespread, albeit understudied type of promise program that covers the balance of students’ tuition and fees after other aid is exhausted at a single community college. Utilizing a difference-in-differences and event-study design, we investigate the role Tulsa Achieves eligibility plays in promoting or hindering vertical transfer and bachelor's degree attainment across racial/ethnic groups. We find that Tulsa Achieves eligibility is associated with increases in bachelor's degree attainment within 5 years among Native American and Hispanic students and an increased likelihood of transfer within 4 years for Hispanic students.