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Covid-19 Education Research for Recovery
We examine the fundamental and complex role that time plays in the learning process. We begin by developing a conceptual framework to elucidate the multiple obstacles schools face in converting total time in school into active learning time. We then synthesize the causal research and document a clear positive effect of time on student achievement of small to medium magnitude, but also with likely diminishing marginal returns. Further descriptive analyses reveal how large differences in the length of the school day and year across public schools are an underappreciated dimension of educational inequality in the United States. Finally, our case study of time loss in one urban district demonstrates the potential to substantially increase learning time within existing constraints.
The COVID-19 pandemic’s impact on preschool children’s school readiness skills remains understudied. This research investigates Head Start preschool children’s early numeracy, literacy, and executive function outcomes during a pandemic-affected school year. Study children (N = 336 assessed at fall baseline; N = 237-250 assessed in spring depending on outcome; fall baseline sample: mean age = 51 months; 46% Hispanic; 36% Black Non-Hispanic; 52% female) in a network of Head Start centers in four states (Nevada, New Jersey, Pennsylvania, and Wisconsin) experienced low in-person preschool exposure compared to national pre-pandemic norms. Children experienced fall to spring score gains during the pandemic-affected year of 0.05 SD in executive function, 0.27 SD in print knowledge, and 0.45-0.71 SD in early numeracy skills. Descriptively, for two of the three early numeracy domains measured, spring test score outcomes were stronger among children who attended more in-person preschool. We discuss implications for future research and policy.
Media reports suggest that parent frustration with COVID school policies and the growing politicization of education have increased community engagement with local public schools. However, there is no evidence to date on whether these factors have translated into greater engagement at the ballot box. This paper uses a novel data set to explore how school board elections changed following the start of the COVID-19 pandemic. I find that school board elections post-COVID were more likely to be contested, and that voter turnout in contested elections increased. These changes were large in magnitude and varied with several district characteristics.
How much school students attend is a powerful indicator of their well-being and a strong predictor of their future success in school. Prior research has documented the myriad in-school and out-of-school factors that contribute to high levels of student absenteeism, many emerging from the root causes of poverty and disengagement. The shift to online learning during the COVID-19 pandemic likely disrupted prior barriers to attendance and may have created new ones. This sequential explanatory mixed-methods study examined student absenteeism during the 2020–2021 school year in Detroit. We used administrative data to show whether and how attendance patterns changed, and we linked family survey and interview data to explain those patterns. We found that 70% of students were chronically absent, with 40% of parents reporting that computer problems contributed to absenteeism. While measures of socioeconomic disadvantage and computer/internet issues were associated with lower attendance and higher probability of chronic absenteeism, reported levels of hardship during the pandemic were not. Despite significant investment in technology, the district’s strategies for engaging students were not sufficient in overcoming economic hardships and the new challenges of online learning.
The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from all rounds of the Programme for International Student Assessment. Scores declined by an average of 14 percent of a standard deviation, roughly equal to seven months of learning. Losses were greater for students in schools that faced relatively longer closures, boys, immigrants, and disadvantaged students. Educational losses may translate into significant national income losses over time.
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.
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.