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Covid-19 Education Research for Recovery
Temporary college closures in response to the COVID-19 pandemic created an exodus of students from college towns just as the decennial census count was getting underway. We use aggregate cellular mobility data to evaluate if this population movement affected the distributional accuracy of the 2020 Census. Based on the outflow of devices in late March 2020, we estimate that counties with a college were undercounted by two percent, likely affecting Congressional apportionment. For college towns, student populations can impact government funding allocations, policy program decisions, and planning for infrastructure, public health, and more. The Census Bureau is allowing governmental entities to request count reviews through June 2023. Colleges should cooperate with state and local government efforts to ensure an accurate count.
In response to the Covid-19 pandemic and school disruption, both the federal and state government have sought to allocate needed funding to schools so they can provide adequate instruction and safe learning spaces to students in North Carolina. These funds, particularly the ESSER III funding through the American Rescue Plan Act of 2021, were provided to individual Public School Units (PSUs) based on applications identifying spending plans. These spending plans, submitted to the state before November 2021, include applications from 112 traditional Local Education Agencies (LEAs) and 177 charter PSUs. Using a mixed methods approach, both the quantitative and qualitative portions of this study independently attempt to identify patterns in the funding priorities of individual PSUs through the narrative in their funding applications, then compare the results to develop a more holistic understanding of these funding priorities. Overall, PSUs prioritized spending plans in the areas of technology, personnel, academic Covid-19 mitigation efforts (combating effects of lost opportunities to learn), and safety Covid-19 mitigation efforts (reducing viral spread and other projects to protect physical health and safety). In reviewing these applications and the results of both studies, we have developed several recommendations for mitigating the impact of the most recent school disruption and preparing for the next school disruption. These recommendations include re-evaluating enrichment programs, addressing the unique needs of various student populations, investing in quality education materials, developing research-based practices and strategies, growing professional networks between PSUs, and prioritizing the physical health and safety of students through regular maintenance of school structures.
Teachers' levels of stress and burnout have been high throughout the COVID-19 pandemic, raising concerns about a potential increase in teacher turnover and future teacher shortages. We examine how the COVID-19 pandemic affected teacher turnover in Arkansas from 2018-19 to 2022-23 using administrative data. We find no major changes in turnover entering the first two pandemic years, but a large increase of 5.3 percentage points (26%) entering the third year, with variation by teacher and student characteristics. We also find that increases in teacher turnover are related to instructional mode and that this turnover may partially be explained by the use of COVID-19 relief funds. Additionally, we find evidence that more effective teachers became more likely to leave the education sector after the pandemic as compared to before the pandemic. Our results suggest increased strain and reduced diversity and quality in the Arkansas teacher workforce and raise concerns about the long-term impacts that COVID-19 may have on its stability and quality.
Purpose: Nearly all schools in the United States closed in spring 2020, at the onset of the COVID-19 pandemic. We analyze traditional public and charter school reopenings for the 2020-21 school year in five urban districts. We provide a rich and theoretically grounded description of how and why educational leaders made reopening decisions in each of our case districts.
Research Methods: We used data from a multiple-case study from March 2020 to July 2021. The research team conducted 56 interviews with school, district, and system-level leaders; triangulated with publicly available data; and also drew on interview data from a subsample of parents and guardians in each of our sites. We analyzed these data through qualitative coding and memo writing, and conducted detailed single- and cross-case analyses.
Findings: School system leaders in our case sites generally consulted public health authorities, accounted for state-level health and educational guidance, and engaged with and were responsive to the interests of different stakeholders. Districts’ adherence to and strategic uses of public health guidance, as well as a combination of union-district relations and labor market dynamics, influenced reopening. Parents, city and state lawmakers, and local institutional conditions also played a role, helping to explain differences across cases.
Implications: In contrast to the “politics or science” framing that has dominated research and public discourse on school reopening, we show that local pandemic conditions and local political dynamics both mattered and in fact were interrelated. Our findings have some implications for how educational leaders might navigate future crises.
The effects of the COVID-19 pandemic on students’ experiences in school were widespread. Early research show reductions in test scores across grade levels and student groups. This study extends research evidence to additional student outcomes – absences, course grades, and grad retention – and to examine how pandemic effects are distributed across students. Using a combination of descriptive and regression analyses, we find negative average impacts on all outcomes. These effects are largest at the high end of the absence distribution and the low end of the grade distribution. Effects are also largest in middle school for most outcomes and are typically larger among historically marginalized groups of students. These findings reflect widening achievement gaps and the need for targeted supports.
We provide evidence from a randomized controlled trial on the effectiveness of a novel, 100-percent online math tutoring program, targeted at secondary school students from highly disadvantaged neighborhoods. The intensive, eight-week-long program was delivered by qualified math teachers in groups of two students during after-school hours. The intervention significantly increased standardized test scores (+0.26 SD) and end-of-year math grades (+0.48 SD), while reducing the probability of repeating the school year. The intervention also raised aspirations, as well as self-reported effort at school.
The factors that influenced school districts’ decisions to offer virtual, hybrid, or in-person instruction during the 2020-21 school year—the first full school year after the emergence of the COVID-19 pandemic—have been the focus of a large body of research in recent years. Some of this research examines the influence of school spending, among other factors; however, these studies do not consider spending in relation to cost, “cost” being the amount needed for a school district to achieve a given outcome. This paper uses a measure of adequacy, which is the amount of spending under or over estimated cost, to determine whether spending correlates with the amount of time a school district offered virtual instruction. We find spending adequacy significantly and substantially predicts time spent in virtual instruction: for every $1,000 positive change in adequacy (closing a gap and/or adding to a surplus), the time spent in virtual schooling decreases 0.6%. A one standard deviation positive change in adequacy, therefore, results in 7.5 fewer days of virtual instruction. While our findings are descriptive, they do require future researchers to consider school spending adequacy, as much as any other factor, as a predictor of pandemic instructional models.
During the 2020-21 school year, Black and Hispanic students were less likely to attend school in-person than white students. Prior research indicated multiple factors helped explain this gap. In this study, we revise these observed racial gaps in in-person learning to examine whether the relationship between these gaps and explanatory factors observed earlier in the pandemic changed during the 2021-2022 school year. We find that, while in-person gaps decreased, Black respondents continued to be less likely to report in-person learning than white respondents. Political leanings and COVID-19 health risks, which helped explain observed gaps in 2020-2021, lose explanatory power. But the availability of learning options remains an important factor in helping explain the observed in-person gaps. In this respect, our results suggest the presence of a mismatch between the preferences that Black families have and what they are being offered.
The effect of school closures in the spring of 2020 on the math, science, and reading skills of secondary school students in Poland is estimated. The COVID-19-induced school closures lasted 26 weeks in Poland, one of Europe's longest periods of shutdown. Comparison of the learning outcomes with pre- and post-COVID-19 samples shows that the learning loss was equal to more than one year of study. Assuming a 45-year working life of the total affected population, the economic loss in future student earnings may amount to 7.2 percent of Poland’s gross domestic product.
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.