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Post-secondary education
Award displacement occurs when one type of financial aid award directly contributes to the change in the quantity of another award. We explore whether postsecondary institutions displaced awards in response to the Pittsburgh Promise scholarship by capitalizing on the doubling of the maximum Promise amount in 2012. We use de-identified student-level data on each Promise recipient’s actual cost of attendance, grants, and scholarships, as well as demographic and academic characteristics from school district administrative files to examine whether and how components of students’ financial aid packages and total costs of attendance changed after the Promise award increase. To account for overall trends in pricing and financial aid, we compare Promise recipients to the average first-time, full-time freshman entering the same institutions in the same year as reported by the Integrated Postsecondary Education Data System (IPEDS). With these two data sources, we assess differences in costs and awards between Promise students and their peers, on average, and examine whether and in what ways these differences changed after the increase in Promise funding. We refer to this strategy as a “quasi-difference-in-differences” design. We do not find evidence that institutions are responding to the Promise increase through aid reductions.
An increasing body of robust evidence concludes that corequisite remediation in math and English is a cost-effective alternative to traditional developmental education, offering improved immediate course progression and potentially better persistence and completion. This is the first study to disentangle the impacts of the two main elements of the corequisite model: accelerated college course placement and concurrent academic support. Utilizing a fuzzy regression discontinuity design and variation in Texas colleges' implementation of math corequisites, the study shows that college-level math course placement without additional support increases passing rates by 22 percentage points. This effect rises to 36 percentage points with concurrent developmental support. These findings bolster a growing consensus around the benefits of accelerated developmental education and suggest that a corequisite approach may have significant advantages over removing developmental education requirements entirely.
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models -- one predicting course completion, the second predicting degree completion. Our results show that algorithmic bias in both models could result in at-risk Black students receiving fewer success resources than White students at comparatively lower-risk of failure. We also find the magnitude of algorithmic bias to vary within the distribution of predicted success. With the degree completion model, the amount of bias is nearly four times higher when we define at-risk using the bottom decile than when we focus on students in the bottom half of predicted scores. Between the two models, the magnitude and pattern of bias and the efficacy of basic bias mitigation strategies differ meaningfully, emphasizing the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being less useful at predicting Black student success compared with White student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.
Using data from nearly 1.2 million Black SAT takers, we estimate the impacts of initially enrolling in an Historically Black College and University (HBCU) on educational, economic, and financial outcomes. We control for the college application portfolio and compare students with similar portfolios and levels of interest in HBCUs and non-HBCUs who ultimately make divergent enrollment decisions - often enrolling in a four-year HBCU in lieu of a two-year college or no college. We find that students initially enrolling in HBCUs are 14.6 percentage points more likely to earn a BA degree and have 5 percent higher household income around age 30 than those who do not enroll in an HBCU. Initially enrolling in an HBCU also leads to $12,000 more in outstanding student loans around age 30. We find that some of these results are driven by an increased likelihood of completing a degree from relatively broad-access HBCUs and also relatively high-earning majors (e.g., STEM). We also explore new outcomes, such as credit scores, mortgages, bankruptcy, and neighborhood characteristics around age 30.
This study leverages national data and a quasi-experimental design to examine the influence of enrolling in an exclusively online degree program on students’ likelihood of completing their degree. We find that enrolling in an exclusively online degree program had a negative influence on students’ likelihood of completing their bachelor’s degree or any degree when compared to their otherwise-similar peers who enrolled in at least some face-to-face courses. The negative relationship between exclusively online enrollment and students’ likelihood of bachelor’s degree completion was relatively consistent among White, Black, Hispanic, Asian, low-income, and military students. Findings focused solely on those students enrolled in exclusively online degree programs revealed that the negative influence of exclusively online enrollment was exacerbated when the student attended a for-profit four-year institution.
Teacher preparation programs are increasingly expected to use data on pre-service teacher (PST) skills to drive program improvement and provide targeted supports. Observational ratings are especially vital, but also prone to measurement issues. Scores may be influenced by factors unrelated to PSTs’ instructional skills, including rater standards and mentor teachers’ skills. Yet we know little about how these measurement challenges play out in the PST context. Here we investigate the reliability and sensitivity of two observational measures. We find measures collected during student teaching are especially prone to measurement issues; only 3-4% of variation in scores reflects consistent differences between PSTs, while 9-17% of variation can be attributed to the mentors with whom they work. When high scores stem not from strong instructional skills, but instead from external circumstances, we cannot use them to make consequential decisions about PSTs’ individual needs or readiness for independent teaching.
News media plays a crucial role in the student loan policy ecosystem by influencing how policymakers and the public understand the “problem” of student loans. Prior research emphasizes the causal impact of the media on the social construction of policy issues and the lack of knowledge about the authors of news articles. Theory also suggests that it is more difficult for new information to reach people in the core of a social network given their insular relationships. Therefore, we used social network analysis to investigate the college backgrounds for authors of student loan articles published in eight prominent newspapers between 2006 and 2021. We found evidence of a stark status hierarchy among the colleges attended (e.g., over half of the authors attended an Ivy Plus or Public Flagship institution). Our findings also identified a negative relationship between that hierarchy and an innovative practice, the use of racialized language in student loan news articles. We discuss how this status hierarchy might explain current patterns of racialized language in student loan policy and the implications of this relationship for the intersection of status and novel practices.
Despite documented benefits to college completion, more than a third of students who initially enroll in college do not ultimately earn a credential. Completing college requires students to navigate both institutional administrative tasks (e.g., registering for classes) and academic tasks within courses (e.g., completing homework). In postsecondary education, several promising interventions have shown that text-based outreach and communication can be a low-cost, easy to implement, and effective strategy for supporting administrative task navigation. In this paper, we report on two randomized controlled trials testing the effect of a text-based chatbot with artificial intelligence (AI) capability on students' academic task navigation in introductory courses (political science and economics). We find the academic chatbot significantly shifted students’ final grades, increasing the likelihood students received a course grade of B or higher by 5-6 percentage points and reduced the likelihood students dropped the course.
Decisions to invest in human capital depend on people’s time preferences. We show that differences in patience are closely related to substantial subnational differences in educational achievement, leading to new perspectives on longstanding within-country disparities. We use social-media data – Facebook interests – to construct novel regional measures of patience within Italy and the United States. Patience is strongly positively associated with student achievement in both countries, accounting for two-thirds of the achievement variation across Italian regions and one-third across U.S. states. Results also hold for six other countries with more limited regional achievement data.
Generally, need-based financial aid improves students’ academic outcomes. However, the largest source of need-based grant aid in the United States, the Federal Pell Grant Program (Pell), has a mixed evaluation record. We assess the minimum Pell Grant in a regression discontinuity framework, using Kentucky administrative data. We focus on whether and how year-to-year changes in aid eligibility and interactions with other sources of aid attenuate Pell’s estimated effects on post-secondary outcomes. This evaluation complements past work by assessing explanations for the null or muted impacts found in our analysis and other Pell evaluations. We also discuss the limitations of using regression discontinuity methods to evaluate Pell—or other interventions with dynamic eligibility criteria—with respect to generalizability and construct validity.