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K-12 Education
Targeted instruction is one of the most effective educational interventions in low- and middle-income countries, yet reported impacts vary by an order of magnitude. We study this variation by aggregating evidence from prior randomized trials across five contexts, and use the results to inform a new randomized trial. We find two factors explain most of the heterogeneity in effects across contexts: the degree of implementation (intention-to-treat or treatment-on-the-treated) and program delivery model (teachers or volunteers). Accounting for these implementation factors yields high generalizability, with similar effect sizes across studies. Thus, reporting treatment-on-the-treated effects, a practice which remains limited, can enhance external validity. We also introduce a new Bayesian framework to formally incorporate implementation metrics into evidence aggregation. Results show targeted instruction delivers average learning gains of 0.42 SD when taken up and 0.85 SD when implemented with high fidelity. To investigate how implementation can be improved in future settings, we run a new randomized trial of a targeted instruction program in Botswana. Results demonstrate that implementation can be improved in the context of a scaling program with large causal effects on learning. While research on implementation has been limited to date, our findings and framework reveal its importance for impact evaluation and generalizability.
Public schools are currently a source of major political conflict, specifically with regard to issues related to LGBT representation in the curriculum. We report on a large nationally representative survey of American households focusing on their views on what LGBT topics are and should be taught, and what LGBT-themed books should be assigned and available. We report results overall and broken down by demographic, partisan, and geographic variables. We find that Americans report that they largely do not know what topics are being taught in schools, but they do not think LGBT topics are being taught to elementary children. There is widespread opposition to teaching about LGBT issues in elementary school, with more mixed support in high school. Voters are much more opposed to LGBT-themed books being assigned to students than available to them. There are very large splits in attitudes toward LGBT issues in schools, especially along political and religious lines and across states and counties based on partisan lean. We discuss implications of these findings for education policy and urge greater understanding of Americans' views about controversial topics in the curriculum.
‘QuantCrit’ (Quantitative Critical Race Theory) is a rapidly developing approach that seeks to challenge and improve the use of statistical data in social research by applying the insights of Critical Race Theory. As originally formulated, QuantCrit rests on five principles; 1) the centrality of racism; 2) numbers are not neutral; 3) categories are not natural; 4) voice and insight (data cannot ‘speak for itself); and 5) a social justice/equity orientation (Gillborn et al, 2018). The approach has quickly developed an international and interdisciplinary character, including applications in medicine (Gerido, 2020) and literature (Hammond, 2019). Simultaneously, there has been ferocious criticism from detractors outraged by the suggestion that numbers are anything other than objective and scientific (Airaksinen, 2018). In this context it is vital that the approach establishes some common understandings about good practice; in order to sustain rigor, make QuantCrit accessible to academics, practioners, and policymakers alike, and resist widespread attempts to over-simplify and pillory. This paper is intended to advance an iterative process of expanding and clarifying how to ‘QuantCrit’.
Teachers’ attitudes and classroom management practices critically affect students’ academic and behavioral outcomes, contributing to the persistent issue of racial disparities in school discipline. Yet, identifying and improving classroom management at scale is challenging, as existing methods require expensive classroom observations by experts. We apply natural language processing methods to elementary math classroom transcripts to computationally measure the frequency of teachers’ classroom management language in instructional dialogue and the degree to which such language is reflective of punitive attitudes. We find that the frequency and punitiveness of classroom management language show strong and systematic correlations with human-rated observational measures of instructional quality, student and teacher perceptions of classroom climate, and student academic outcomes. Our analyses reveal racial disparities and patterns of escalation in classroom management language. We find that classrooms with higher proportions of Black students experience more frequent and more punitive classroom management. The frequency and punitiveness of classroom management language escalate over time during observations, and these escalations occur more severely for classrooms with higher proportions of Black students. Our results demonstrate the potential of automated measures and position everyday classroom management interactions as a critical site of intervention for addressing racial disparities, preventing escalation, and reducing punitive attitudes.
Partisanship influenced learning modality after the pandemic’s onset, but it is unknown whether partisanship predicted other aspects of educational operations. We study the role of partisanship, race, markets, and public health in predicting a range of operations—from modality to family engagement to social-emotional support to teacher PD—throughout 2020-21 in the context of Virginia. Districts’ partisan makeup and racial composition were similarly predictive of in-person offerings throughout 2020-21 but partisanship was less predictive over time. District characteristics explained limited variation in other aspects of operations, though districts with larger private school sectors provided more supports. Results emphasize the role of partisanship, race, and markets in reopening but also suggest school operational decisions were less politicized than choice of modality.
We examine the impact of local labor market shocks and state unemployment insurance (UI) policies on student discipline in U.S. public schools. Analyzing school-level discipline data and firm-level layoffs in 23 states, we find that layoffs have little effect on discipline rates overall. However, effects differ across the UI benefit distribution. At the lowest benefit level ($265/week), a mass layoff increases out-of-school suspensions by 4.5%, with effects dissipating as UI benefits increase. Effects are consistently largest for Black students - especially in predominantly White schools - resulting in increased racial disproportionality in school discipline following layoffs in low-UI states.
We estimate the effect of universal free school meal access through the Community Eligibility Program (CEP) on child BMI. Through the CEP, schools with high percentages of students qualified for free or reduced-priced meals can offer freebreakfast and lunchto all students. With administrative data from a large school district in Georgia, we use student-level BMI measures from the FitnessGram to compare within-student outcomes before and after CEP implementation across eligible and non-eligible schools. We find one year of CEP exposure increased expected BMI percentile by about 0.085 standard deviations, equivalent to a nearly 1.88- pound weight increase for a student of average height. We also find that the program led to a small increase in the likelihood of overweight and limited evidence of a small decrease in the likelihood of underweight. We do not find that the program increased student obesity risk. Examining the effects of CEP on child BMI by grade suggests that the overall effect is largely driven by students in middle schools, highlighting potential heterogeneity in the program’s impact across grades. The findings of this paper are relevant for researchers and policymakers concerned with the effects of universal free school meals on student health.
We document that recent generations of elementary school teachers are significantly more effective in raising student test scores than those from earlier generations. Measuring teachers’ value-added for Black and white students separately, the improvements in teaching for Black students are significantly larger than those seen for white students. The race-specific improvements in teacher quality are driven by white teachers. Analyses of mechanisms suggest that changing teachers’ biases may be one potential channel. Our results suggest reason for optimism since these teacher quality differences should lead to improved student learning and a narrowing of the Black-white test score gap over time.
Congestion is a persistent and expensive problem, costing the nation collectively over $300 billion each year. Cities have generally attempted to address congestion using an unoriginal set of expensive strategies, like building new roads or expanding public transit, and many cities are considering implementing congestion pricing. Expanding school bus service may be a palatable solution because it provides a service instead of involving lengthy and costly construction or charging a new fee. School travel is also a sizeable portion of total daily tra c. Indeed, over 50 million children travel to and from school each day and their commutes account for about one-quarter of total daily commuter trips. School travel and school-provided transportation is generally the domain of school districts and not city governments and the school districts in most large cities are independent from city governments. This may lead to a coordination problem if school districts ignore congestion caused, or exacerbated by, school travel. To determine whether pupil transportation reduces congestion, I exploit the interaction of pupil transportation provision (variation in pupil transportation spending and school bus use within districts) and idiosyncratic, within-city and within-month variation in the percentage of weekdays that are instructional school days in a month. I build a rich, monthly, longitudinal data set for congestion, school days, and transportation policy for 51 cities from 2013 to 2019 and find congestion is significantly higher on school days and pupil transportation alleviates congestion caused by school children’s travel. A back-of-the-envelope calculation suggests cities should subsidize the additional spending needed by the school district to transport more students and lower congestion.