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Despite its increasing importance for educational practices, broadband is not equally accessible among all students. In addition to oft-noted last-mile barriers faced by rural students, there can be wide variation in in-home access between proximate urban and suburban neighborhoods ostensibly covered by the same telecommunications infrastructure. In this paper, we investigate the connection between these disparities and earlier redlining practices by spatially joining two current measures of broadband access with Depression-era residential security maps that graded neighborhoods for real estate investment risk from “Best” to “Hazardous” based in part on racist and classist beliefs. We find evidence that despite internet service providers reporting similar technological availability across neighborhoods, access to broadband in the home generally decreases in tandem with historic neighborhood risk classification. We further find differences in in-home broadband access by race/ethnicity and income level, both across and within neighborhood grades. Our results demonstrate how federally developed housing policies from the prior century remain relevant to the current digital divide and should be considered in discussions of educational policies that require broadband access for success.
Many novice teachers learn to teach “on-the-job,” leading to burnout and attrition among teachers and negative outcomes for students in the long term. Pre-service teacher education is tasked with optimizing teacher readiness, but there is a lack of causal evidence regarding effective ways for preparing new teachers. In this paper, we use a mixed reality simulation platform to evaluate the causal effects and robustness of an individualized, directive coaching model for candidates enrolled in a university-based teacher education program, as well as for undergraduates considering teaching as a profession. Across five conceptual replication studies, we find that targeted, directive coaching significantly improves candidates’ instructional performance during simulated classroom sessions, and that coaching effects are robust across different teaching tasks, study timing, and modes of delivery. However, coaching effects are smaller for a sub-population of participants not formally enrolled in a teacher preparation program. These participants differed from teacher candidates in multiple ways, including by demographic characteristics, as well as by their prior experiences learning about instructional methods. We highlight implications for research and practice.
How have changes in the costs of enrolling for full-time study at public 2-year and 4-year colleges have affected the decisions about whether and where to enroll in college? We exploit local differences in the growth of tuition at community colleges and public 4-year colleges to study the impact of public higher education costs on the postsecondary enrollment decisions of high school graduates over three decades. We model prospective students’ decisions about whether to attend community college, a public 4-year university in their state of residence, other colleges, or no college at all as relative costs change. Unlike institutional analyses, our contribution is not to model how enrollment changes at a particular college or type of college as costs change. But, we draw from the institutional literature to help identify enrollment impacts by instrumenting college costs using policy variation imposed by state appropriations and tuition caps. We estimate that in counties where local community college tuition doubled (about average for the study period), the likelihood of post-secondary enrollment fell by about 0.06, on a mean of about 0.80. In addition to reducing college enrollment overall, rising costs at community colleges diverted other students to 4-year colleges. Rising relative costs of 4-year public colleges similarly diverted some students toward community colleges, but did not limit college attendance in the aggregate. We also find evidence of endogeneity in cost setting at the institution level. Our preferred estimates rely on a control function approach that instruments intertemporal changes in institutional costs using state and local appropriations and state policies to restrict tuition growth.
We investigate how the presence of a college affects local educational attainment using historical natural experiments in which "runner-up" locations were strongly considered to become college sites but ultimately not chosen for as-good-as-random reasons. While runner-up counties have since had opportunity to establish their own colleges, winners are still more likely to have a college today. Using this variation, we find that winning counties today have college degree attainment rates 58% higher than runner-up counties and have larger shares of employment in high human capital sectors. These effects are not driven primarily by college employees, migration, or local development.
Many prior studies have examined whether there are average differences in levels of teaching effectiveness among graduates from different teacher preparation programs (TPPs); other studies have investigated which features of preparation predict graduates’ average levels of teaching effectiveness. This is the first study to examine whether there are average differences between TPPs in terms of graduates’ average growth, rather than levels, in teaching effectiveness, and to consider which features predict this growth. Examining all graduates from Tennessee TPPs from 2010 to 2018, we find meaningful differences between TPPs in terms of both levels and growth in teaching effectiveness. We also find that different TPP features, including areas of endorsement, program type, clinical placement type and length, program size, and faculty composition explain part of these differences. Yet, the features that predict initial teaching effectiveness are not the same features that predict growth.
This paper examines how financial aid reform based on postsecondary institutional performance impacts student choice. Federal and state regulations often reflect concerns about the private, for-profit sector's poor employment outcomes and high loan defaults, despite the sector's possible theoretical advantages. We use student level data to examine how eliminating public subsidies to attend low-performing for-profit institutions impacts students' college enrollment and completion behavior. Beginning in 2011, California tightened eligibility standards for their state aid program, effectively eliminating most for-profit eligibility. Linking data on aid application to administrative payment and postsecondary enrollment records, this paper utilizes a differences-in-differences strategy to investigate students' enrollment and degree completion responses to changes in subsidies. We find that restricting the use of the Cal Grant at for-profit institutions resulted in significant state savings but led to relatively small changes in students' postsecondary trajectories. For older, non-traditional students we find no impact on enrollment or degree completion outcomes. Similarly, for high school graduates, we find that for-profit enrollment remains strong. Unlike the older, non-traditional students, however, there is some evidence of declines in for-profit degree completion and increased enrollment at community colleges among the high school graduates, but these results are fairly small and sensitive to empirical specification. Overall, our results suggest that both traditional and non-traditional students have relatively inelastic preferences for for-profit colleges under aid-restricting policies.
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy; and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, impacts model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and most complex models.
Using administrative data from Georgia, we provide the first study of the full set of college entrance exam-taking strategies, including who takes the ACT and the SAT (or both), when they take the exams, and how many times they take each exam. We have several main findings. First, one-third of exam takers take both the ACT and SAT. Second, we see pronounced disparities in several measures of exam-taking strategy by free- and reduced-price lunch status, even after including a rich set of controls, but not by underrepresented minority status. Third, we find evidence that taking more total exams leads to higher admissions-relevant test scores and a higher likelihood of enrolling in colleges with relatively high graduation rates and earnings. However, these relationships with test scores and college enrollment are smaller for those who take both the ACT and SAT, as opposed to retaking the same exam multiple times.
Over the past four decades, income inequality grew significantly between workers with bachelor’s degrees and those with high school diplomas (often called “unskilled”). Rather than being unskilled, we argue that these workers are STARs because they are skilled through alternative routes—namely their work experience. Using the skill requirements of a worker’s current job as a proxy of their actual skill, we find that though both groups of workers make transitions to occupations requiring similar skills to their previous occupations, workers with bachelor’s degrees have dramatically better access to higher wage occupations where the skill requirements exceed the workers’ observed skill. This measured opportunity gap offers a fresh explanation of income inequality by degree status and reestablishes the important role of on-the-job-training in human capital formation.
Online courses provide flexible learning opportunities, but research suggests that students may learn less and persist at lower rates compared to face-to-face settings. However, few research studies have investigated more distal effects of online education. In this study we analyzed six years of institutional data for three cohorts of students in thirteen large majors (N=10,572) at a public research university to examine distal effects of students’ online course participation. Using online course offering as an instrumental variable for online course taking, we find that online course taking of major-required courses leads to higher likelihood of successful four-year graduation and slightly accelerated time-to-degree. These results suggest that offering online course-taking opportunities may help students to more efficiently graduate college.