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Covid-19 Education Research for Recovery

Yusuf Canbolat, Rebeca Arndt.

A concerning number of middle and high school students lack fundamental reading skills in the United States. One common way schools address this issue is by supporting those students with computer-assisted instruction. This study evaluates the causal effect of one such computer-assisted instruction intervention on English Language Arts achievement for middle and high school students in a large urban Southeast school district. The district uses a computer-based online learning platform as part of its multi-tiered system of support. The study benefits the usage data in the learning platform from about ten thousand students by exploiting difference in differences and event study estimations. Particularly, it offers a novel method by utilizing the time of initial platform usage and dates of within-year tests for each student. Our results indicate that, on average, the intervention increases test scores by 0.14 SD—a modest but important magnitude given the scale of the intervention. The magnitude of the effect is relatively larger for students who use the platform consistently and among English Language learners. Results are robust against several sensitivity tests including inverse probability weighting, and type of aggregated treatment effect parameter. These results suggest that effective computer-assisted instruction can help schools narrow the achievement gap among students, particularly for English Language learners.

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Jeremy Singer.

Educational policymakers, leaders, and researchers are paying increasing attention to student attendance and chronic absenteeism, especially in the wake of the COVID-19 pandemic. Though researchers have documented the consequences and causes of absenteeism, there is limited empirical evidence about what schools and districts are actually doing to improve attendance. This study presents evidence about the types of attendance practices that forty-seven high-absenteeism districts in Michigan are planning and implementing. I draw on a combination of quantitative and qualitative data from principal surveys, case studies, observations, and school improvement plans. In the 2022-23 school year, principals reported using communication practices, incentives, and to an extent providing resources to address barriers to attendance. In improvement plans, districts planned to create new organizational infrastructure and hire new personnel, with less emphasis on specific practices. These findings highlight a reliance on communication-based strategies and limited existing organizational infrastructure for addressing attendance. Though these districts have planned to develop new attendance systems and practices, it is unclear whether they will substantially reduce absenteeism, since they do not substantially address social and economic inequalities at the root of high absenteeism rates. I conclude with recommendations for monitoring new attendance practices, addressing root causes, and avoiding counterproductive practices.

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Alvin Makori, Patricia Burch, Susanna Loeb.

High-impact tutoring has emerged as a primary school district investment for addressing learning loss that occurred during the COVID-19 pandemic. While existing research shows that high-impact tutoring is effective for accelerating student learning, this study examined the school-level facilitators and barriers to scaling high-impact tutoring. Situated in an urban traditional school district and an urban charter management organization, we collected survey and interview data from teachers and administrators to identify scaling challenges. Major barriers to scaling included time and space constraints, tutor supply and quality, updated data systems, and school level costs, while a key facilitator was teacher buy-in. We end the paper with recommendations for how districts can strategically grow their high-impact tutoring efforts.

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Christopher D. Brooks, Matthew G. Springer.

We analyzed the proposed spending data for the American Recovery Plan’s Elementary and Secondary Emergency Relief III (ESSER III) fund from the spring of 2021 of nearly 3,000 traditional public-school districts in the United States to (1) identify trends in the strategies adopted and (2) to test whether spending strategies were observably heterogeneous across district characteristics. We found that districts proposed a breadth of spending patterns with ESSER III. Moreover, there was a clear prioritization on spending related to academic learning recovery and facilities and operations spending, with the latter being particularly emphasized in higher-poverty districts. This divergent spending pattern may have important equity implications for short-term academic learning recovery for students affected by the COVID-19 pandemic.

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Matthew A. Kraft, Sarah Novicoff.

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.

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Kathleen Lynch, Monica Lee, Susanna Loeb.

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.

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Brian A. Jacob.

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.

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Sarah Winchell Lenhoff, Jeremy Singer.

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.

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Maciej Jakubowski, Tomasz Gajderowicz, Harry Anthony Patrinos.

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.

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Kelli A. Bird, Benjamin L. Castleman, Yifeng Song.

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.

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