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High rates of teacher turnover in child care settings have negative implications for young children’s learning experiences and for efforts to improve child care quality. Prior research has explored the prevalence and predictors of turnover at the individual teacher level, but less is known about turnover at the center level – specifically, how turnover varies across child care centers or whether staffing challenges persist year after year for some centers. This study tracks annual turnover rates for all publicly funded child care centers that were continuously operating in Louisiana from the 2015-16 to 2018-19 school years (n=575 centers). We document high and variable turnover rates across centers throughout the state: The annual mean turnover rate was 40%, and each year nearly one-third of centers experienced high turnover, that is, lost more than half of their teachers. About 27% of centers experienced high turnover for multiple years in our panel, while 44% of centers did not experience high turnover in any year. Our findings underscore concerns that sustained staffing challenges may hinder efforts to provide high-quality child care.
Despite policy relevance, longer-term evaluations of educational interventions are relatively rare. A common approach to this problem has been to rely on longitudinal research to determine targets for intervention by looking at the correlation between children’s early skills (e.g., preschool numeracy) and medium-term outcomes (e.g., first-grade math achievement). However, this approach has sometimes over—or under—predicted the long-term effects (e.g., 5th-grade math achievement) of successfully improving early math skills. Using a within-study comparison design, we assess various approaches to forecasting medium-term impacts of early math skill-building interventions. The most accurate forecasts were obtained when including comprehensive baseline controls and using a combination of conceptually proximal and distal short-term outcomes (in the nonexperimental longitudinal data). Researchers can use our approach to establish a set of designs and analyses to predict the impacts of their interventions up to two years post-treatment. The approach can also be applied to power analyses, model checking, and theory revisions to understand mechanisms contributing to medium-term outcomes.
New York City’s Pre-K for All (PKA) is the Nation’s largest universal early childhood initiative, currently serving some 70,000 four-year-olds. Stemming from the program’s choice architecture as well as the City’s stark residential segregation, PKA programs are extremely segregated by child race/ethnicity. Our current study explores the complex forces that influence this segregation, including the interplay between family choices, seat availability, site-level enrollment priorities, and the PKA algorithm that weighs these and other considerations. We find that a majority of PKA segregation lies within rather than between local communities, suggesting that reducing segregation would not necessarily require families to choose programs far from home. On a more troubling note, areas with increased options and greater racial/ethnic diversity also exhibit the most extreme segregation.
This study assesses the effects of two text messaging programs for parents that aim to support the development of math skills in prekindergarten students. One program focuses purely on math, while the other takes an identical approach but focuses on a combination of math, literacy, and social-emotional skills. We find no evidence that the math-only program benefits children’s math development. However, the combination program shows greater promise, particularly for girls. Quantile regressions indicate that the effects are concentrated in the lower half of the outcome distribution. We discuss and provide evidence for various hypotheses that could explain these differences.
- We test the effects of two text messaging curricula that leverage behavioral economics principles to help parents support the math development of prekindergarteners in the home.
- We find that a program that cycles through literacy, mathematics, and social-emotional skills increases math achievement for girls, while a program focusing solely on mathematics has no effects.
- Benefits for girls are concentrated on those with weaker performance on mathematics assessments.
- We posit potential mechanisms based on the literature.
Many preschool agencies nationwide continue to experience closures and/or conversions to virtual or hybrid instruction due to the ongoing COVID-19 pandemic. Despite the importance of understanding young children’s learning and development during the COVID emergency, limited knowledge exists on adaptable practices of assessing young children during the pandemic. We detail practices used to assess learning in 336 Head Start children across four states during three different time periods in the 2020-21 school year, using adaptation of traditionally in-person assessments of early numeracy, early literacy, and executive functioning. In doing so, we distill early lessons for the field from the application of a novel, virtual assessment method with the early childhood population. The paper describes adaptations of assessment administration for virtual implementation and incorporation of feedback into continued virtual delivery of assessments. Applications and limitations in broader contexts are discussed.
In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching ones understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. Machine-based text analytic and data mining tools offer one potential avenue to help facilitate research in this domain. For instance, we could augment a traditional impact analysis that examines a single human-coded outcome with a suite of automatically generated secondary outcomes. By analyzing impacts across a wide array of text-based features, we can then explore what an overall change signifies, in terms of how the text has evolved due to treatment. In this paper, we propose several different methods for supplementary analysis in this spirit. We then present a case study of using these methods to enrich an evaluation of a classroom intervention on young children’s writing. We argue that our rich array of findings move us from “it worked” to “it worked because” by revealing how observed improvements in writing were likely due, in part, to the students having learned to marshal evidence and speak with more authority. Relying exclusively on human scoring, by contrast, is a lost opportunity.
We documented (1) the use of strategies, beyond suspensions and expulsions, that exclude young students from learning opportunities and (2) how teacher-reported use of these strategies varied according to student racial/ethnic composition. In a sample of 2,053 teachers and 40,771 kindergarten students, teachers reported on their use of five exclusionary strategies including isolated seating, removal from an activity, and loss of recess. Teachers reported substantive use of all exclusionary strategies and use varied depending on strategy. Teachers reported using certain exclusionary practices (break outside of classroom, loss of recess or free time, and limit talking) more frequently when they rated more Black versus White students to be lowest on self-regulation and social skills. Findings illustrate the value of looking beyond suspensions and expulsions in the early years to advance equity in young children’s opportunities to engage with teachers, peers, and learning tasks at school.
This paper provides a longitudinal examination of teacher turnover across all publicly-funded, center-based early childhood sites in Louisiana. We follow 4,465 early educators teaching in fall 2016 up to seven times through the fall of 2019. We provide the first statewide estimates of within-year turnover in ECE, as well as the first statewide study tracking turnover rates in ECE over multiple years. We find high within-year turnover: about 10% of teachers observed in the fall are not teaching the following spring. We also show that over 60% of fall 2016 teachers are no longer teaching at the same site in fall 2019. Turnover is particularly high among child care teachers, teachers of toddlers, and new teachers.
Third grade is oftentimes the first year standardized literacy assessments are mandated. In turn many policies aimed at improving literacy have focused on third-grade test scores as a key indicator. Yet literacy struggles begin well before third grade, as do racial and socioeconomic disparities in children’s literacy skills. Kindergarten readiness assessments provide a unique opportunity to better understand the emergence of literacy disparities. We use unique kindergarten literacy data from nearly every school district in Virginia to document the relationship between children’s early literacy skills and their later reading proficiency. Comparing children with similar literacy skills at kindergarten entry, we find significant racial and socioeconomic differences in the likelihood a child is proficient on their third-grade reading assessment.
Education has faced unprecedented disruption during the COVID-19 pandemic; evidence about the subsequent effect on children is of crucial importance. We use data from an oral reading fluency (ORF) assessment—a rapid assessment taking only a few minutes that measures a fundamental reading skill—to examine COVID’s effects on children’s reading ability during the pandemic in more than 100 U.S. school districts. Effects were pronounced, especially for Grades 2–3, but distinct across spring and fall 2020. While many students were not assessed in spring 2020, those who were seemed to have experienced relatively limited or no growth in ORF relative to gains observed in other years. In fall 2020, a far more representative set of students was observed. For those students, growth was more pronounced and seemed to approach levels observed in previous years. Worryingly, there were also signs of stratification such that students in lower-achieving districts may be falling further behind. However, at the level of individual students, those who were struggling with reading prior to the pandemic were not disproportionately impacted in terms of ORF growth. This data offers an important window onto how a foundational skill is being affected by COVID-19 and this approach can be used in the future to examine how student abilities recover as education enters a post-COVID paradigm.