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This study examines the effects of the MATC Promise, a public-private partnership that offered to pay tuition at Milwaukee Area Technical College (MATC) for local high school graduates. The MATC Promise exemplifies the most common type of college promise program, a last-dollar community college tuition promise. If students completed academic milestones, applied for state and federal aid, and qualified based on low family income, then the Promise would cover any remaining tuition charges. In practice, the message of a promise was the main treatment, since most eligible students would not have any tuition charges remaining for the program to cover after applying state and federal aid. We evaluate the effects of the Promise on increasing college enrollment and degree completion after its introduction in 2016. Milwaukee is unique within the Wisconsin, making it difficult to find relevant comparison groups in statewide data. Examining the interrupted time series within the city’s school districts shows an increase in enrollment at MATC from 10 percent of high school graduates to 15 percent after the Promise was introduced. About half of the increase came from students who would not have enrolled at all, with the rest diverting from enrolling at other colleges and universities. These effects were concentrated among lower-income students and those in the inner city. These results indicate that the Promise positively influenced college attainment by encouraging students to access state and federal aid they already qualified for. We conclude that the message of college affordability was effective at encouraging students to overcome application barriers and enroll in college.
Teachers affect a wide range of students’ educational and social outcomes, but how they contribute to students’ involvement in school discipline is less understood. We estimate the impact of teacher demographics and other observed qualifications on students’ likelihood of receiving a disciplinary referral. Using data that track all disciplinary referrals and the identity of both the referred and referring individuals from a large and diverse urban school district in California, we find students are about 0.2 to 0.5 percentage points (7% to 18%) less likely to receive a disciplinary referral from teachers of the same race or gender than from teachers of different demographic backgrounds. Students are also less likely to be referred by more experienced teachers and by teachers who hold either an English language learners or special education credential. These results are mostly driven by referrals for defiance and violence infractions, Black and Hispanic male students, and middle school students. While it is unclear whether these findings are due to variation in teachers’ effects on actual student behavior, variation in teachers’ proclivities to make disciplinary referrals, or a combination of the two, these results nonetheless suggest that teachers play a central role in the prevalence of, and inequities in, office referrals and subsequent student discipline.
Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource intensive in most educational contexts. We develop an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. We conduct a randomized controlled trial as part of an online computer science course, Code in Place (n=1,136 instructors), to evaluate the effectiveness of the feedback tool. We find that the tool improves instructors’ uptake of student contributions by 27% and present suggestive evidence that our tool also improves students’ satisfaction with the course and assignment completion. These results demonstrate the promise of our tool to complement existing efforts in teachers’ professional development.
The impact of school resources on student outcomes was first raised in the 1960s and has been controversial since then. This issue enters into the decision making on school finance in both legislatures and the courts. The historical research found little consistent or systematic relationship of spending and achievement, but this research frequently suffers from significant concerns about the underlying estimation strategies. More recent work has re-opened the fundamental resource-achievement relationship with more compelling analyses that offer stronger identification of resource impacts. A thorough review of existing studies, however, leads to similar conclusions as the historical work: how resources are used is key to the outcomes. At the same time, the research has not been successful at identifying mechanisms underlying successful use of resources or for ascertaining when added school investments are likely to be well-used. Direct investigations of alternative input policies (capital spending, reducing class size, or salary incentives for teachers) do not provide clear support for such specific policy initiatives.
We used Critical Discourse Analysis to examine the racial discourse within recent attempts to reauthorize the Higher Education Act. Specifically, we interrogated congressional markup hearings to understand how members frame student debt and the racialized dynamics embedded within. Our findings highlight three types of discourse: “All Students” Matter, Paternalistic, Race-Evasive, and Explicit Racial Discourse. We offer recommendations for research and policymaking.
What happens when employers screen their employees but only observe a subset of output? We specify a model with heterogeneous employees and show that their response to the screening affects output in both the probationary period and the post-probationary period. The post-probationary impact is due to their heterogeneous responses affecting which individuals are retained and hence the screening efficiency. We show that the impact of the endogenous response on both the unobserved outcome and screening efficiency depends on whether increased effort on one task increases or decreases the marginal cost of effort on the other task. If the response decreases unobserved output in the probationary period then it increases the screening efficiency, and vice versa. We then assess these predictions empirically by studying a change to teacher tenure policy in New York City, which increased the role that a single measure -- test score value-added -- played in tenure decisions. We show that in response to the policy teachers increased test score value-added and decreased output that did not enter the tenure decision. The increase in test score value-added was largest for the teachers with more ability to improve students' untargeted outcomes, increasing their likelihood of getting tenure. We estimate that the endogenous response to the policy announcement reduced the screening efficiency gap -- defined as the reduction of screening efficiency stemming from the partial observability of output -- by 28%, effectively shifting some of the cost of partial observability from the post-tenure period to the pre-tenure period.
A stable learning environment is critical to high school reforms aimed at promoting postsecondary educational success. High teacher attrition can disrupt stable learning environments by uprooting student-teacher relationships and harming school climate. Educational leaders need greater understanding of how college readiness reforms alter learning environments generally, and teacher retention in particular. We study teacher turnover in two Texas College and Career Readiness School Models (CCRSM), called Early College High Schools and inclusive Science, Technology, Engineering, and Math Academies. We find (a) CCRSM schools have lower teacher turnover compared to traditional public high schools, (b) charter versions of CCRSM schools have higher turnover, but (c) non-CCRSM charters have the highest overall teacher turnover. We discuss implications for improving high school-based college readiness reforms.
The prevalence of school-based healthcare has increased markedly over the past decade. We study a modern mode of school-based healthcare, telemedicine, that offers the potential to reach places and populations with historically low access to such care. School-based telemedicine clinics (SBTCs) provide students with access to healthcare during the regular school day through private videoconferencing with a healthcare provider. We exploit variation over time in SBTC openings across schools in three rural districts in North Carolina. We find that school-level SBTC access reduces the likelihood that a student is chronically absent by 2.5 percentage points (29 percent) and reduces the number of days absent by about 0.8 days (10 percent). Relatedly, access to an SBTC increases the likelihood of math and reading test-taking by between 1.8-2.0 percentage points (about 2 percent). Heterogeneity analyses suggest that these effects are driven by male students. Finally, we see suggestive evidence that SBTC access reduces violent or weapons-related disciplinary infractions among students but has little influence on other forms of misbehavior.
Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We compare algorithmic and human predictions in the context of a national college advising program, focusing in particular on predicting high-achieving, lower-income students’ college enrollment quality. College advisors slightly outperform a prediction algorithm; however, greater advisor accuracy is concentrated among students with whom advisors had more interactions. The algorithm achieved similar accuracy among students lower in the distribution of interactions, despite advisors having substantially more information. We find no evidence that the advisors or algorithm exhibit bias against vulnerable populations. Our results suggest that, especially at scale, algorithms have the potential to provide efficient, accurate, and unbiased predictions to target scarce social services and resources.