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In response to the COVID-19 outbreak, the governments of most countries ordered the closure of schools, potentially exacerbating existing learning gaps. This paper evaluates the effectiveness of an intervention implemented in Italian middle schools that provides free individual tutoring online to disadvantaged students during lock-down. Tutors are university students who volunteer for 3 to 6 hours per week. They were randomly assigned to middle school students, from a list of potential beneficiaries compiled by school principals. Using original survey data collected from students, parents, teachers and tutors, we find that the program substantially increased students’ academic performance (by 0.26 SD on average) and that it significantly improved their socio-emotional skills, aspirations, and psychological well-being. Effects are stronger for children from lower socioeconomic status and, in the case of psychological well-being, for immigrant children.
Teachers are among the most important school-provided determinants of student success. Effective teachers improve students’ test scores as well as their attendance, behavior, and earnings as adults. However, students do not enjoy equal access to effective teachers. This article reviews some of the key challenges associated with teacher policy confronted by school leaders and education policymakers, and how the tools of applied economics can help address those challenges. The first challenge is that identifying effective teachers is difficult. Economists use value-added models to estimate teacher effectiveness, which works well in certain circumstances, but should be just one piece of a multi-measure strategy for identifying effective teachers. We also discuss how different policies, incentives, school characteristics, and professional-development interventions can increase teacher effectiveness; this is important, as schools face the daunting challenge of hiring effective teachers, helping teachers to improve, and removing ineffective teachers from the classroom. Finally, we discuss the supply and mobility of teachers, including the consequences of teacher absenteeism, the distribution of initial teaching placements, and the characteristics and preferences of those who enter the profession.
Food routines play a special role in Latino families. Using a cluster randomized trial with 248 children (M age = 67 months) from 13 schools, this study investigated the impact of a four-week family program designed to capitalize on food routines in improving Latino kindergarteners’ outcomes in the U.S. There were moderate-to-large impacts on child vocabulary (especially food-related) at end-of-treatment and the five-month follow-up, and suggestive evidence of moderate impacts on approaches to learning (including approaches to learning math) and executive function at the five-month follow-up. There were no statistically significant impacts on children’s math or literacy skills. A strengths-based, culturally responsive family intervention that is integrated into Latino family life can improve critical skills needed to succeed in school.
School district consolidation is one of the most widespread education reforms of the last century, but surprisingly little research has directly investigated its effectiveness. To examine the impact of consolidation on student achievement, this study takes advantage of a policy that requires the consolidation of all Arkansas school districts with enrollment of fewer than 350 students for two consecutive school years. Using a regression discontinuity model, we find that consolidation has either null or small positive impacts on student achievement in math and English Language Arts (ELA). We do not find evidence that consolidation in Arkansas results in positive economies of scale, either by reducing overall cost or allowing for a greater share of resources to be spent in the classroom.
This study investigates the influence of principal tenure on the retention rates of the teachers they hire over time. We analyzed the hiring practices and teacher retention rates of 11,717 Texas principals from 1999 to 2017 employing both individual and year fixed effects. Main findings indicate that a principal who stays in the same school for at least three years begins to hire teachers who stay to both three- and five-year benchmarks at increasingly higher rates. However, the average Texas principal leaves a school after four years and while we do find small positive gains in the initial retention rates of teachers at the next school, the majority of principal improvement in teacher retention does not appear to be portable.
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.