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Covid-19 Education Research for Recovery
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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.
COVID-19 upended schooling across the United States, but with what consequences for the state-level institutions that drive most education policy? This paper reports findings on two related research questions. First, what were the most important ways state government education policymakers changed schools and schooling from the moment they began to reckon with the seriousness of COVID-19 through the first full academic year of the pandemic? Second, how deep did those changes go – are there indications the pandemic triggered efforts to make lasting changes in states’ education policymaking institutions? Using multiple-methods research focused on Colorado, Florida, Louisiana, Michigan, and Oregon, we documented policies enacted during the period from March 2020 through June 2021 across states and across sectors (traditional and choice) in three COVID-19-related education policy domains: school closings and reopenings, budgeting and resource allocation, and assessment and accountability systems. We found that states quickly enacted radical changes to policies that had taken generations to develop. They mandated sweeping school closures in Spring 2020, and then a diverse array of school reopening policies in the 2020/2021 school year. States temporarily modified their attendance-based funding systems and allocated massive federal COVID-19 relief funds. Finally, states suspended annual student testing, modified the wide array of accountability policies and programs linked to the results of those tests, and adapted to new assessment methods. These crisis-driven policy changes deeply disrupted long-established patterns and practices in education. Despite this, we found that state education governance systems remained resilient, and that at least during the first 16 months of the pandemic, stakeholders showed little interest in using the crisis to trigger more lasting institutional change. We hope these findings enable state policymakers to better prepare for future crises.
The COVID-19 pandemic disrupted teacher candidates’ capacity to complete licensure requirements. In response, many states temporarily reduced professional entry requirements to prevent a pandemic-induced teacher shortage. Using mixed methods, we examine the role of the emergency teaching license in Massachusetts, which provided an opportunity for individuals to enter the public school teacher workforce with only a bachelor’s degree. Our results show that emergency licenses increased the supply of teachers in two ways by: 1) providing an entry point for individuals who previously wanted to become teachers but could not meet traditional licensure requirements and 2) expanding the pool of individuals interested in the profession. Among those teachers hired with an emergency license, we find that they were substantially more ethnoracially diverse than their peers with traditional licenses, and they overwhelmingly intend to obtain permanent licensure and remain in the profession. These results suggest that rethinking initial entry requirements may be an effective policy tool to increase the supply of teachers, particularly among teachers of color.
We develop a unifying conceptual framework for understanding and predicting teacher shortages at the state, region, district, and school levels. We then generate and test hypotheses about geographic and subject variation in teacher shortages using data on unfilled teaching positions in Tennessee during the fall of 2019. We find that teacher staffing challenges are highly localized, causing shortages and surpluses to coexist. Aggregate descriptions of staffing challenges mask considerable variation between schools and subjects within districts. Schools with fewer local early-career teachers, smaller district salary increases, worse working conditions, and higher historical attrition rates have higher vacancy rates. Our findings illustrate why viewpoints about, and solutions to, shortages depend critically on whether one takes an aggregate or local perspective.
With a goal of contextualizing teacher job dissatisfaction during the first full school year of the COVID-19 pandemic, we contrast teachers’ experiences to the decade and a half leading up to the pandemic. We draw on nationally representative data from the Schools and Staffing Survey and National Teacher and Principal Survey from the 2003-04 to 2020-21 school years. Through descriptive and regression analysis, we show that (1) teacher dissatisfaction has gradually been increasing over time, but did not decrease sharply in the 2020-21 school year, (2) levels of dissatisfaction during the pandemic were not equal across subpopulations of teachers or over time, and (3) positive working conditions consistently predicted lower job dissatisfaction, including in the 2020-21 school year.
The extent to which pandemic-induced public school enrollment declines will persist is unclear. Student-level data from Michigan through fall 2021 yields three relevant findings. First, relative to pre-pandemic trends, fall 2021 enrollment had partially recovered for low-income, Black, and Hispanic students, but had declined further for non-low-income, White, and Asian students. Second, annual public school exit rates remained elevated for elementary students and accelerated further for middle school students. Third, public school exit is sticky and varies by chosen alternative. Only 21 percent of those who left for private schools in fall 2020 had returned by fall 2021, while 50 percent of those who left for homeschooling had returned. These findings suggest that pandemic-driven public school enrollment declines may persist, and more so among higher income families.
Tutoring has emerged as an especially promising strategy for supporting students academically. This study synthesizes 33 articles on the implementation of tutoring, defined as one-to-one or small-group instruction in which a human tutor supports students grades K-12 in an academic subject, to better understand the facilitators and barriers to program success. We find that policies influenced tutoring implementation through the allocation of federal funding and stipulation of program design. Tutoring program launch has often been facilitated by strategic relationships between schools and external tutoring providers and strengthened by transparent assessments of program quality and effectiveness. Successful implementation hinged on the support of school leaders with the power to direct school funding, space, and time. Tutoring setting and schedule, recruitment and training, and curriculum influenced whether students are able to access quality tutoring and instruction. Ultimately, evidence suggests that tutoring was most meaningful when tutors fostered positive student-tutor relationships which they drew upon to target instruction toward students’ strengths and needs.
Four-day school weeks are becoming increasingly common in the United States, but their effect on students’ achievement is not well-understood. The small body of existing research suggests the four-day schedule has relatively small, negative average effects (~-0.02 to -0.09 SD) on annual, standardized state test scores in math and reading, but these studies include only a single state or are limited by using district-level data. We conduct the first multi-state, student-level analysis that estimates the effect of four-day school weeks on student achievement and a more proximal measure of within-year growth using NWEA MAP Growth assessment data. We conduct difference-in-differences analyses to estimate the effect of attending a four-day week school relative to attending a five-day week school. We estimate significant negative effects of the schedule on spring reading achievement (-0.07 SD) and fall-to-spring achievement gains in math and reading (-0.06 SD in both). The negative effects of the schedule are disproportionately larger in non-rural schools than rural schools and for female students, and they may grow over time. Policymakers and practitioners will need to weigh the policy’s demonstrated negative average effects on achievement in their decisions regarding how and if to implement a four-day week.
We analyze the impact of COVID-19 diagnoses on student grades, retention, and on-time graduation at a large public university. Even though COVID-19 rarely causes major health complications for a typical university student, diagnosis and quarantine may cause non-trivial disruptions to learning. Using event study analysis, we find that a COVID-19 diagnosis decreased a student's term grade point average (GPA) modestly by 0.08 points in the semester of diagnosis without significant effects afterward. The results were the most pronounced for male students, individuals with face-to-face instruction, and those with higher GPAs before the pandemic. We do not find a significant increase in the incidence of failing or withdrawing from a course due to diagnosis. In addition, we find no general evidence that the diagnoses delayed graduation or significantly altered first-year retention. However, the University experienced significant grade inflation during the pandemic, which exceeded the estimated effects of any COVID-19 diagnoses.
We study the distributional effects of remote learning. Our approach combines newly collected data on parental preferences with administrative data from Los Angeles. The preference data allow us to account for selection into remote learning while also studying selection patterns and treatment effect heterogeneity. We find a negative average effect of remote learning on reading (–0.14σ) and math (–0.17σ). Notably, we find evidence of positive learning effects for children whose parents have the strongest demand for remote learning. Our results suggest an important subset of students who currently sort into post-pandemic remote learning benefit from expanded choice.