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Covid-19 Education Research for Recovery

Suchitra Akmanchi, Kelli A. Bird, Benjamin L. Castleman.

Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We compare algorithmic and human predictions in the context of a national college advising program, focusing in particular on predicting high-achieving, lower-income students’ college enrollment quality. College advisors slightly outperform a prediction algorithm; however, greater advisor accuracy is concentrated among students with whom advisors had more interactions. The algorithm achieved similar accuracy among students lower in the distribution of interactions, despite advisors having substantially more information. We find no evidence that the advisors or algorithm exhibit bias against vulnerable populations. Our results suggest that, especially at scale, algorithms have the potential to provide efficient, accurate, and unbiased predictions to target scarce social services and resources.

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David M. Houston, Matthew P. Steinberg.

In spring 2020, nearly every U.S. public school closed at the onset of the Covid-19 pandemic. Existing evidence suggests that local political partisanship and teachers union strength were better predictors of fall 2020 school re-opening status than Covid case and death rates. We replicate and extend these analyses using data collected over the 2020-21 academic year. We demonstrate that Covid case and death rates were meaningfully associated with initial rates of in-person instruction. We also show that all three factors—Covid, partisanship, and teachers unions—became less predictive of in-person instruction as the school year continued. We then leverage data from two nationally representative surveys of Americans’ attitudes toward education and identify an as-yet undiscussed factor that predicts in-person instruction: public support for increasing teacher salaries. We speculate that education leaders were better able to manage the logistical and political complexities of school re-openings in communities with greater support for educators.

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Cassandra M. D. Hart, Michael Hill, Emily Alonso, Di Xu.

While the COVID-19 pandemic necessitated the short-term use of online courses, colleges’ experiences with COVID-era online course delivery may also affect the way that they offer and approach online courses going forward. We draw on interviews with 35 distance education leaders from the California Community Colleges system to provide insights into how the use of online education may change in the system going forward. Leaders predicted that post-pandemic, colleges would increase their online course offerings, and that many instructional innovations to online courses from the pandemic—such as the use of synchronous courses—would persist. They hoped that a more prominent position for online education within the system would be matched by more resources to provide supports for online learning.

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Matthew A. Kraft, Melissa Arnold Lyon.

We examine the state of the U.S. K-12 teaching profession over the last half century by compiling nationally representative time-series data on four interrelated constructs: professional prestige, interest among students, preparation for entry, and job satisfaction. We find a consistent and dynamic pattern across every measure: a rapid decline in the 1970s, a swift rise in the 1980s, relative stability for two decades, and a sustained drop beginning around 2010. The current state of the teaching profession is at or near its lowest levels in 50 years. We identify and explore a range of factors that might explain these historical patterns including education funding, teacher pay, outside opportunities, unionism, barriers to entry, working conditions, accountability, autonomy, and school shootings.  

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Alvin Christian, Brian Jacob, John D. Singleton.

The COVID-19 pandemic drew new attention to the role of school boards in the U.S. In this paper, we examine school districts' choices of learning modality -- whether and when to offer in-person, virtual, or hybrid instruction -- over the course of the 2020-21 pandemic school year. The analysis takes advantage of granular weekly data on learning mode and COVID-19 cases for Ohio school districts. We show that districts respond on the margin to health risks: all else equal, a marginal increase in new cases reduces the probability that a district offers in-person instruction the next week. Moreover, this negative response is magnified when the district was in-person the prior week and attenuates in magnitude over the school year. These findings are consistent with districts learning from experience about the effect of in-person learning on disease transmission in schools. We also find evidence that districts are influenced by the decisions of their peers.

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Jackie Eunjung Relyea, Patrick Rich, James S. Kim, Joshua B. Gilbert.

The current study aimed to explore the COVID-19 impact on the reading achievement growth of Grade 3-5 students in a large urban school district in the U.S. and whether the impact differed by students’ demographic characteristics and instructional modality. Specifically, using administrative data from the school district, we investigated to what extent students made gains in reading during the 2020-2021 school year relative to the pre-COVID-19 typical school year in 2018-2019. We further examined whether the effects of students’ instructional modality on reading growth varied by demographic characteristics. Overall, students had lower average reading achievement gains over the 9-month 2020-2021 school year than the 2018-2019 school year with a learning loss effect size of 0.54, 0.27, and 0.28 standard deviation unit for Grade 3, 4, and 5, respectively. Substantially reduced reading gains were observed from Grade 3 students, students from high-poverty backgrounds, English learners, and students with reading disabilities. Additionally, findings indicate that among students with similar demographic characteristics, higher-achieving students tended to choose the fully remote instruction option, while lower-achieving students appeared to opt for in-person instruction at the beginning of the 2020-2021 school year. However, students who received in-person instruction most likely demonstrated continuous growth in reading over the school year, whereas initially higher-achieving students who received remote instruction showed stagnation or decline, particularly in the spring 2021 semester. Our findings support the notion that in-person schooling during the pandemic may serve as an equalizer for lower-achieving students, particularly from historically marginalized or vulnerable student populations.

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Joshua Bleiberg, Matthew A. Kraft.

The COVID-19 pandemic upended the U.S. education system and the economy in ways that dramatically affected the jobs of K-12 educators. However, data limitations have led to considerable uncertainty and conflicting reports about the nature of staffing challenges in schools. We draw on education employment data from the Bureau of Labor Statistics (BLS) and State Education Agencies (SEA) to describe patterns in K-12 education employment and to highlight the limitations of available data. Data from the BLS suggest overall employment in the K-12 labor market declined by 9.3 percent at the onset of the pandemic and remains well below pre-pandemic levels. SEA data suggest that teachers have not (yet) left the profession in mass as many predicted, but that turnover decreased in the summer of 2020. We explore possible explanations for these patterns including (1) weak hiring through the summer of 2020 and (2) high attrition among K-12 instructional support staff. State vacancy data also suggest that schools are facing substantial challenges filling open positions during the 2021-22 academic year. Our analyses illustrate the imperative to build more timely, detailed, and nationally representative data systems on the K-12 education labor market to better inform policy.  

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Jeremy Singer.

After near-universal school closures in the United States at the start of the pandemic, lawmakers and educational leaders made plans for when and how to reopen schools for the 2020-21 school year. Educational researchers quickly assessed how a range of public health, political, and demographic factors were associated with school reopening decisions and parent preferences for in-person and remote learning. I review this body of literature, to highlight what we can learn from its findings, limitations, and influence on public discourse. Studies consistently highlighted the influence of partisanship, teachers’ unions, and demographics, with mixed findings on COVID-19 rates. The literature offers useful insight and requires more evidence, and it highlights benefits and limitations to rapid research with large-scale quantitative data.

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Claire McMorris, David S. Knight.

The Coronavirus Aid, Relief and Economic Security (CARES) Act passed by Congress in 2020 included significant aid to state education systems. These included direct aid to K-12 districts and higher education institutions, and funds to be used at the discretion of Governors through the Governor’s Emergency Education Relief Fund (GEER). We examine the factors influencing where and how GEER funding was distributed across state K-12 systems and what inequities were introduced in its spending. Using a mixed methods analysis of state GEER spending plans and district-level finance data, we focus specifically on how governors sought to target schools serving disadvantaged student groups. We find that several state leaders decided to send their GEER funds to school districts via funding formulas, and that some Governors made decisions to direct their GEER funds towards certain student groups. State spending patterns were not strongly related to governor political ideology or the states’ existing funding formulas or inter-district resource allocation patterns. We discuss the implications of this policy related to two state case examples, California and New York, and provide insight for future education stimulus funding proposals.

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Robert W. Fairlie, George Bulman.

This paper examines how the pandemic impacted the enrollment patterns, fields of study, and academic outcomes of students in the California Community College System, the largest higher-education system in the country. Enrollment dropped precipitously during the pandemic – the total number of enrolled students fell by 11 percent from fall 2019 to fall 2020 and by another 7 percent from fall 2020 to fall 2021. The California Community College system lost nearly 300,000 students over this period. Our analysis reveals that enrollment reductions were largest among Black/African-American and Latinx students, and were larger among continuing students than first-time students. We find no evidence that having a large online presence prior to the pandemic protected colleges from these negative effects. Enrollment changes were substantial across a wide range of fields and were large for both vocational courses and academic courses that can be transferred to four-year institutions. In terms of course performance, changes in completion rates, withdrawal rates, and grades primarily occurred in the spring of 2020. These findings of the effects of the pandemic at community colleges have implications for policy, impending budgetary pressures, and future research.

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