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A survey targeting education researchers conducted in November, 2020 provides predictions of how much achievement gaps between low- and high-income students in U.S elementary schools will change as a result of COVID-related disruptions to in-class instruction and family life. Respondents were asked to suppose that the pre-COVID achievement gap was 1.00 standard deviations. The median forecast for the jump in math achievement in elementary school by spring, 2021 was very large – a change from 1.00 to 1.30 standard deviations. The predicted increase in reading achievement gaps (a change from 1.00 to 1.25 standard deviations) was nearly as large. This implies that many teachers will face classrooms of students with much more heterogeneous learning needs in the fall of the 2021-22 school year than usual. We gauged predictions for the success of efforts by teachers and other educators to make up for lost ground by asking for predictions of achievement gaps in the spring of 2022. Few of the respondents to our survey thought that achievement gaps would revert to their pre-COVID levels. In fact, median predictions of achievement gaps fell very modestly– from 1.30 to 1.25 standard deviations for math and from 1.25 to 1.20 standard deviations for reading. We discuss some implications of these predictions for school district strategies (e.g., tutoring and other skill- building programs focused on individual students) to reduce learning gaps exacerbated by the pandemic.
We study an early effort amid the Covid-19 pandemic to develop new approaches to virtually serving students, supporting teachers, and promoting equity. This five-week, largely synchronous, summer program served 11,769 rising 4th-9thgraders. “Mentor teachers” provided PD and videos of themselves teaching daily lessons to “partner teachers” across the country. We interviewed a representative sample of teachers and analyzed educator, parent, and student surveys. Stakeholders perceived that students made academic improvements, and the content was rigorous, relevant, and engaging. Teachers felt their teaching improved and appreciated receiving adaptable curricular materials. Participants wanted more relevant math content, more differentiated development, and less asynchronous movement content. Findings highlight promising strategies for promoting online engagement and exploiting virtual learning to strengthen teacher development.
In this thought experiment, we explore how tutoring could be scaled nationally to address COVID-19 learning loss and become a permanent feature of the U.S. public education system. We outline a blueprint centered on ten core principles and a federal architecture to support adoption, while providing for local ownership over key implementation features. High school students would tutor in elementary schools via an elective class, college students in middle schools via federal work-study, and full time 2- and 4-year college graduates in high schools via AmeriCorps. We envision an incremental, demand-driven expansion process with priority given to high-needs schools. Our blueprint highlights a range of design tradeoffs and implementation challenges as well as estimates of program costs. Our estimates suggest that targeted approaches to scaling school-wide tutoring nationally, such as focusing on K-8 Title I schools, would cost between $5 and $15 billion annually.
Researchers often include covariates when they analyze the results of randomized controlled trials (RCTs), valuing the increased precision of the estimates over the potential of inducing small-sample bias when doing so. In this paper, we develop a sufficient condition which ensures that the inclusion of covariates does not cause small-sample bias in the effect estimates. Using this result as a building block, we develop a novel approach that uses machine learning techniques to reduce the variance of the average treatment effect estimates while guaranteeing that the effect estimates remain unbiased. The framework also highlights how researchers can use data from outside the study sample to improve the precision of the treatment effect estimate by using the auxiliary data to better model the relationship between the covariates and the outcomes. We conclude with a simulation, which highlights the value of using the proposed approach.
Political scientists have largely overlooked the democratic challenges inherent in the governance of U.S. public education—despite profound implications for educational delivery and, ultimately, social mobility and economic growth. In this study, we consider whether the interests of adult voters who elect school boards in each community are likely to be aligned with the educational needs of local students. Specifically, we compare voters and students in four states on several policy-relevant dimensions. Using official voter turnout records and rich microtargeting data, we document considerable demographic differences between voters who participate in school board elections and the students attending the schools that boards oversee. These gaps are most pronounced in majority nonwhite jurisdictions and school districts with the largest racial achievement gaps. Our novel analysis provides important context for understanding the political pressures facing school boards and their likely role in perpetuating educational and, ultimately, societal inequality.
Aspirations shape important future-oriented behaviors, including educational investment. Higher family aspirations for children predict better educational outcomes in multiple developing countries. Unfortunately, aspirations sometimes outstrip people's ability to pursue them. We study the relationship between family aspirations for children and later child educational outcomes in an extremely poor context. We observe caregivers' educational and career aspirations for thousands of rural Gambian children about to begin schooling. While higher aspirations predict subsequent educational investment and, three years later, better child performance on reading/math tests, these gains are small in terms of skills learned, and high-aspirations children remain far from achieving literacy/numeracy. In contrast, a bundled supply-side intervention generated large literacy/numeracy gains in these areas. Since unobserved correlates of aspirations and educational outcomes likely bias our estimates upwards, the true aspirations-learning relationship may be even smaller. We conclude higher aspirations alone are insufficient to achieve literacy/numeracy in this, and perhaps similar contexts.
This study examines the effects of internal migration driven by severe natural disasters on host communities, and the mechanisms behind these effects, using the large influx of migrants into Florida public schools after Hurricane Maria. I find adverse effects of the influx in the first year on existing student test scores, disciplinary problems, and student mobility among high-performing students in middle and high school that also persist in the second year. I also find evidence that compensatory resource allocation within schools is an important factor driving the adverse effects of large, unexpected migrant flows on incumbent students in the short-run.
Virtual charter schools provide full-time, tuition-free K-12 education through internet-based instruction. Although virtual schools offer a personalized learning experience, most research suggests these schools are negatively associated with achievement. Few studies account for differential rates of student mobility, which may produce biased estimates if mobility is jointly associated with virtual school enrollment and subsequent test scores. We evaluate the effects of a single, large, anonymous virtual charter school on student achievement using a hybrid of exact and nearest-neighbor propensity score matching. Relative to their matched peers, we estimate that virtual students produce marginally worse ELA scores and significantly worse math scores after one year. When controlling for student mobility during the outcome year, estimates of virtual schooling are slightly less negative. These findings may be more reliable indicators of the independent effect of virtual schooling if matching on mobility proxies for otherwise unobservable negative selection factors.
Researchers have noted the importance of equity-based approaches to social and emotional learning (SEL), which emphasize the role of school environment, including adult beliefs, in student well-being. This article builds on this work by examining 129 teachers’ perceptions of efficacy in SEL. While participants worked in urban schools, were selected from national fellowship programs, and had similar years of experience and preparation, survey data found that teachers in one program reported higher levels of efficacy in SEL. Interviews and observations with a purposeful sample of these teachers found that despite common challenges with exclusionary discipline practices and limited resources, efficacious teachers described a “social justice learning community,” geared for teachers of color, that enhanced their capacities to enact SEL in their schools. Discussion includes the need for critical professional development opportunities in SEL that are race-conscious, context-specific, and asset-based, as well as opportunities for teachers from historically marginalized groups to form specialized learning communities.
To evaluate how Advanced Placement courses affect college-going, we randomly assigned the offer of enrollment into an AP science course to over 1,800 students in 23 schools that had not previously offered the course. We find no substantial AP course effects on students’ plans to enroll in college or on their college entrance exam scores. Yet AP course-takers enroll in less selective colleges than their control group counterparts. Negative treatment effects on college selectivity appear to be driven more by low student preparation than teacher inexperience and by students’ matriculation decisions rather than institutional admissions decisions.