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Understanding Young Children's Learning and Development in the Wake of the Pandemic: Evidence from Acelero Head Start Programs

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Title: Understanding Young Children's Learning and Development in the Wake of the Pandemic: Evidence from Acelero Head Start Programs
Language: English
Authors: Meghan McCormick, Maya Goldberg, Emily Swinth, Cate Smith Todd, Lydia Carlis, Victoria Chavez, Samantha Xia
Source: Early Education and Development. 2025 36(3):515-541.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 27
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: COVID-19, Pandemics, Emergent Literacy, Language Skills, Mathematics Skills, Executive Function, Child Development, Academic Achievement, Age Differences, Gender Differences, Family Structure, Low Income Students, Federal Programs, Social Services, Early Intervention, Socioeconomic Status, Racial Differences, Ethnicity, Language Usage, Individual Characteristics
Geographic Terms: Nevada, New Jersey, Pennsylvania (Philadelphia), Wisconsin (Milwaukee)
Laws, Policies and Program Identifiers: Head Start
Assessment and Survey Identifiers: Peabody Picture Vocabulary Test, Classroom Assessment Scoring System
DOI: 10.1080/10409289.2024.2423384
ISSN: 1040-9289
1556-6935
Abstract: Research Findings: The COVID-19 pandemic had significant negative effects on the learning and development of school-aged children in the United States, with disproportionate impacts on children from marginalized groups. There is less evidence on the extent to which the pandemic affected younger children -- ages 3 to 5 -- from these groups. The current study examined the extent to which children in Acelero Head Start centers (N = 343) made gains in literacy, language, math, and executive functioning 2 years after the start of the pandemic and compared those learning gains to pre-pandemic norms in national Head Start and Acelero comparison samples. Children grew rapidly in all domains, performing and gaining in line with (or faster than) pre-pandemic Acelero Head Start children in language, literacy, and executive functioning. Overall scores were lower and growth was slower in math than pre-pandemic levels. Four-year-old children in the current study generally made larger gains than their younger peers. Boys and children from single parent households made larger gains in language skills compared to girls and children from two-parent households, respectively. Practice or Policy: Results provide evidence on Head Start children's academic and cognitive skills during the pandemic recovery and highlight the need for continued research to support children's resilience.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499534
Database: ERIC
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  Value: <anid>AN0183685179;h4j01apr.25;2025Mar18.05:11;v2.2.500</anid> <title id="AN0183685179-1">Understanding Young Children's Learning and Development in the Wake of the Pandemic: Evidence from Acelero Head Start Programs </title> <p>Research Findings: The COVID-19 pandemic had significant negative effects on the learning and development of school-aged children in the United States, with disproportionate impacts on children from marginalized groups. There is less evidence on the extent to which the pandemic affected younger children – ages 3 to 5 – from these groups. The current study examined the extent to which children in Acelero Head Start centers (N = 343) made gains in literacy, language, math, and executive functioning 2 years after the start of the pandemic and compared those learning gains to pre-pandemic norms in national Head Start and Acelero comparison samples. Children grew rapidly in all domains, performing and gaining in line with (or faster than) pre-pandemic Acelero Head Start children in language, literacy, and executive functioning. Overall scores were lower and growth was slower in math than pre-pandemic levels. Four-year-old children in the current study generally made larger gains than their younger peers. Boys and children from single parent households made larger gains in language skills compared to girls and children from two-parent households, respectively. Practice or Policy: Results provide evidence on Head Start children's academic and cognitive skills during the pandemic recovery and highlight the need for continued research to support children's resilience.</p> <p>Much has been written about the negative effects of the COVID-19 pandemic on the academic achievement of school-aged children (Werner & Woessmann, [<reflink idref="bib79" id="ref1">79</reflink>]). Indeed, there is a general consensus that the pandemic and its associated disruptions to in-person schooling have dramatically reduced average scores on state (Goldhaber et al., [<reflink idref="bib28" id="ref2">28</reflink>]) and national (NCES, [<reflink idref="bib61" id="ref3">61</reflink>]) standardized tests in the United States and internationally (Betthäuser et al., [<reflink idref="bib12" id="ref4">12</reflink>]), with particularly large and deleterious effects on math achievement (Chapman, [<reflink idref="bib17" id="ref5">17</reflink>]). Even more concerning from an equity perspective, students from racially, ethnically, and socioeconomically marginalized groups appear to have been most negatively affected by the crisis (Mitchell, [<reflink idref="bib58" id="ref6">58</reflink>]). Less is known, however, about the learning and development of <emph>preschool-aged</emph> children, those younger than five-years-old, in the wake of the pandemic. There is certainly evidence that their early care and education (ECE) experiences were significantly disrupted, particularly during the spring of 2020 and the 2020–2021 academic year (Weiland & Morris, [<reflink idref="bib77" id="ref7">77</reflink>]), but fewer studies have examined how resilient young children were – or were not – to these challenges.</p> <p>Findings from the handful of studies done on this topic have yielded mixed results. For example, some research has found that even the youngest children have been negatively affected by the crisis, experiencing more challenges with speech and behavior than is typical (Jung & Barnett, [<reflink idref="bib40" id="ref8">40</reflink>]). But other studies have shown that children younger than five have been much <emph>less</emph> affected by the pandemic than older school-aged populations (Dorn et al., [<reflink idref="bib23" id="ref9">23</reflink>]) and may even be performing at a similar level on standardized assessments of cognitive and academic skills to their pre-pandemic, similar aged peers (Meeter, [<reflink idref="bib56" id="ref10">56</reflink>]). There is a clear need for further research examining how the youngest learners, and especially those from marginalized groups, are developing in the wake of the pandemic. Such work can inform efforts to better serve these children as they prepare for the transition to elementary school and determine which groups, if any, may require targeted supports.</p> <p>To address this need, the current study examined growth in Head Start children's language, literacy, math, and executive functioning skills about two years after the start of the pandemic and explicitly considered heterogeneity in gains by race/ethnicity, linguistic background, age, gender, and other indicators of socioeconomic status, like living in a single parent household. The study makes four key contributions to the existing literature. First, it provides needed information on a broad range of 3- and 4-year-old Head Start children's academic and cognitive skills in the years immediately after the pandemic. Although there is a good deal of information on the reading and math skills of <emph>older, elementary school-aged children</emph> during this period, there is a dearth of knowledge on younger children's development because of limited systematic data collection activities taking place in ECE settings. Second, and relatedly, because all assessments used in this study can be compared to pre-pandemic national norms using standardized scores, we will be able to understand whether overall skill levels and growth rates appear typical, stronger, or weaker than would have reasonably been expected in the absence of the pandemic. Third, we have access to information on the overall quality of teacher practices in these Head Start settings and can use those data to compare classrooms to pre-pandemic contexts and also to control for the quality of teacher practices when examining gains in learning outcomes during this period. This approach ensures that we can understand developmental gains in these contexts unique from the quality of instruction occurring in Head Start classrooms in the second full academic year of the pandemic. And finally, we have access to a diverse sample of Head Start students, drawn from four different states, with significant variation in race/ethnicity, age, gender, and home language. These data are well set up to allow us to explore heterogeneity in skill gains for groups across geographic locations and to build evidence on a diverse sample of centers and students. Taken together, the findings stand to provide needed information on how young children from socioeconomically and racially/ethnically marginalized families did or did not experience resilience in the wake of the pandemic and for which learning domains.[<reflink idref="bib1" id="ref11">1</reflink>] Findings will also help identify key demographic and structural factors associated with more equitable developmental outcomes.</p> <hd id="AN0183685179-2">Early Education and Development in the Wake of the Pandemic</hd> <p>The evidence is clear that the pandemic has had significant negative effects on the academic and social-emotional skills of school-aged children (Meherali et al., [<reflink idref="bib57" id="ref12">57</reflink>]). Beginning in kindergarten, children typically start to receive academic instruction that is directly aligned with the types of skills that are eventually assessed using standardized tests for high-stakes accountability (that typically begin in third grade). During the first 2 years of the pandemic, significant disruptions to their schooling – due to constant pandemic-related closures, substantial time spent in remote instruction, and COVID-related absences – reduced the amount of instruction students received, likely affecting their overall achievement. Such deleterious effects have been largest for math achievement (Contini et al., [<reflink idref="bib19" id="ref13">19</reflink>]), the tested learning domain that is most closely linked to the quality and amount of in-school instruction (Crosnoe et al., [<reflink idref="bib20" id="ref14">20</reflink>]).</p> <p>The most concerning trend emerging from the data, however, is that the pandemic has had disproportionately large and negative impacts on school-aged students from families with lower-incomes and for those from racially/ethnically marginalized groups (Panchal et al., [<reflink idref="bib64" id="ref15">64</reflink>]). Recent work by Goldhaber and colleagues (2022) has also found that socioeconomic disparities in academic achievement grew the most in school districts that used more remote instruction in the year after the start of the pandemic (academic year 2020–2021). Although gaps in math achievement did not grow at all in districts that never switched to fully remote instruction, they did grow substantially in the districts that made that shift.</p> <p>It has been possible to track these students' learning during the post-pandemic period because school districts and states – typically overseeing K-12 systems – have centralized data systems in place to assess students' reading and math skills as they move across grades. This is particularly true beginning in third grade when almost every state begins administering standardized tests of English/Language Arts (ELA) and math. Researchers have successfully been able to compare average scores on these assessments before and after the start of the pandemic to understand the impact of the crisis on learning for older students (e.g., Goldhaber et al., [<reflink idref="bib28" id="ref16">28</reflink>]; Halloran et al., [<reflink idref="bib31" id="ref17">31</reflink>]). In contrast, there are no centralized assessment systems in place to monitor development in <emph>early childhood</emph>, when, prior to the pandemic, the majority of American children were enrolled in a center-based ECE program during the one or 2 years prior to kindergarten (Friedman-Krauss et al., [<reflink idref="bib27" id="ref18">27</reflink>]; Griffiths, [<reflink idref="bib29" id="ref19">29</reflink>]). As such, the field lacks the ability to understand whether the distressing and troublesome effects of the pandemic for older students are also consistent for young children, or whether differences across these groups have made younger children – and perhaps even those from marginalized groups – more resilient to the crisis.</p> <p>Indeed, there are inklings from the literature suggesting the latter situation may be true. For example, an examination of NWEA (formerly known as Northwest Evaluation Association) data – a widely administered assessment for school-aged children – found that the pandemic recovery has been fastest for younger students (Lewis & Kuhfeld, [<reflink idref="bib45" id="ref20">45</reflink>]). For example, after comparing reading and math assessment data for students in 2019 (pre-pandemic) compared to 2022 (2-year post-pandemic), Lewis and Kuhfeld ([<reflink idref="bib45" id="ref21">45</reflink>] found that students in grades 2–5 recovered between 23% and 36% of the learning they had lost. In contrast, students in middle school had not recovered at all in either domain. Similarly, in a systematic review involving 21 studies from 11 countries, Kauhanen et al. ([<reflink idref="bib42" id="ref22">42</reflink>]) found that the impacts of the pandemic on social-emotional outcomes became larger as children aged and transitioned into adolescence.</p> <p>Again, all of this work examines students in middle childhood and early adolescence, a group that almost exclusively attends some formal school-based program. In contrast, there is much more variation in learning experiences during early childhood. The majority (47% of children in families with incomes <$25,000 and 62% of those in families with incomes ≥$25,000) of American children did attend some formal center-based care or PreK program (including Head Start) before the pandemic (Friedman-Krauss et al., [<reflink idref="bib27" id="ref23">27</reflink>]; Griffiths, [<reflink idref="bib29" id="ref24">29</reflink>]). But as reported in a systematic review Weiland et al. ([<reflink idref="bib75" id="ref25">75</reflink>]), by 63% of ECE centers closed during the initial 3 months of the pandemic. Although many programs did start to reopen in the summer and fall of 2020, many continued to lean on remote instruction – or faced numerous closures – due to COVID-19 exposures and upticks in cases. And there were also negative implications for the take-up of programs even after they re-opened, with ECE enrollment for 3-year-olds falling from 51% before the pandemic to 39% in December 2020 and moving from 71% to 54% for 4-year-old children (Weiland et al., [<reflink idref="bib75" id="ref26">75</reflink>]).</p> <p>Disruptions to children's ECE experiences are generally thought to have negative implications for their learning and development (Almeida et al., [<reflink idref="bib3" id="ref27">3</reflink>]). And there is some early research finding that young children were negatively affected by the pandemic at least in the short-term (Na et al., [<reflink idref="bib60" id="ref28">60</reflink>]). Yet, there is still little evidence on how these trends may have shifted once typical instruction and in-person ECE provision was able to begin again. Drawing on theory from literature on resilience in the context of trauma and disaster, Weiland and Morris ([<reflink idref="bib77" id="ref29">77</reflink>]) argue that children's responses to a crisis are likely to depend on a) the <emph>dose</emph> of exposure to the traumatic event (with those with greater personal exposure to death, trauma, or loss more likely to be affected); b) <emph>developmental timing</emph>; c) <emph>individual differences</emph> in prior skills (Masten, [<reflink idref="bib49" id="ref30">49</reflink>]), prior trauma experiences (Osofsky, [<reflink idref="bib63" id="ref31">63</reflink>]); and biological sensitivity to the environment (Boyce & Ellis, [<reflink idref="bib13" id="ref32">13</reflink>]); and on the d) pre-, during, and post-disaster <emph>context</emph>, with greater challenges in the context of previous risk (Catani et al., [<reflink idref="bib16" id="ref33">16</reflink>]) and when recovery is prolonged (Orengo-Aguayo et al., [<reflink idref="bib62" id="ref34">62</reflink>]).</p> <p>This work offers optimism for the likelihood of adaptive systems emerging to support resilience, including in a multi-system disaster like COVID-19, for young children. Returning to in-person, formal ECE post-pandemic offers the potential for regular routines, consistent exposure to consistent learning activities, peers, and adults that extend children's social relationships, and the opportunity (in some cases) to nurture resilience skills like self-regulation, self-efficacy, and optimism for the future. For example, in contrast to older children, the critical developmental context in early childhood is the family and home microsystem (Tudge et al., [<reflink idref="bib74" id="ref35">74</reflink>]). Even though young children might have experienced significant disruptions in ECE, their dose of exposure to the traumatic event of the pandemic was likely smaller than it was for older students who dramatically shifted their primary learning context and lost critical in-person social ties to same-aged peers (assuming they did not lose a loved one to COVID-19 or something of that nature). Early childhood is also a more heightened time of brain plasticity when children are quickly developing skills and can be highly responsive to different inputs and sources of instruction and investment, making it possible that they could be more resilient to the negative effects of the pandemic once they again returned to the formal structures of consistent, in-person ECE (Cicchetti & Curtis, [<reflink idref="bib18" id="ref36">18</reflink>]).</p> <hd id="AN0183685179-3">Variation in Learning Across Domains</hd> <p>In the wake of a crisis, developmental gains may depend in part on the specific domains of learning (Masten & Cicchetti, [<reflink idref="bib50" id="ref37">50</reflink>]). Bailey et al. ([<reflink idref="bib6" id="ref38">6</reflink>]) argue that ECE programs – and disruptions in ECE – will affect learning domains differently, depending on children's counterfactual experiences. For example, if children were already scoring low on assessments of literacy prior to the pandemic and then received limited exposure to direct literacy instruction when they were at-home rather than enrolled in ECE, we would likely observe less optimal development in that domain. In contrast, if those children stayed home and instead were exposed to a language-rich environment and high levels of vocabulary during the pandemic – perhaps as a result of being at home with multiple older family members – we could observe even more adaptive development and growth in language and receptive vocabulary skills than would have otherwise been expected.</p> <p>Complementary work examining growth in specific domains following enrolment in ECE argues that domains affected by direct instruction – like print knowledge, phonological awareness, and numeracy skills – may have been most affected by the pandemic in the short-term but these are the skills children are most likely to quickly develop again after enrolling back in formal ECE and receiving direct instruction in those domains (McCormick et al., [<reflink idref="bib55" id="ref39">55</reflink>]). In contrast, more unconstrained skills – like vocabulary and executive functioning – are developed more gradually and via a range of learning experiences. These are perhaps less likely to be so proximally affected by a disruption in direct instruction (Paris, [<reflink idref="bib65" id="ref40">65</reflink>]; Snow & Matthews, [<reflink idref="bib72" id="ref41">72</reflink>]). Considering variation in learning and development across distinct domains – like language, literacy, math, and executive functioning – which are all critical to future school success – is important when considering children's learning and development in the wake of the pandemic.</p> <hd id="AN0183685179-4">Variation in Learning for Demographic Groups</hd> <p>Importantly, in line with Weiland and Morris's ([<reflink idref="bib77" id="ref42">77</reflink>]) theoretical framework, there is also likely variation in young children's learning and development depending on their initial skills – and how much they have to grow – as well as their biological sensitivity to the learning environments in which they are embedded. Studies conducted prior to the pandemic confirm that children benefit differentially from ECE programs, with particular evidence in support of compensatory hypotheses asserting that children with the most room to grow tend to benefit most (Puma et al., [<reflink idref="bib68" id="ref43">68</reflink>]; Weiland & Yoshikawa, [<reflink idref="bib78" id="ref44">78</reflink>]). Given evidence of these differential patterns, it is thus important to explore not only general gains that children are making but also to explore how growth varies for key demographic subgroups that may vary on these dimensions. For example, children's age could be a key contributing factor in differentiating response to the pandemic. Children who are in their four-year-old year of an ECE program – and preparing to transition to kindergarten in the following year – likely experienced a significant disruption to in-person learning during what would have been their three-year-old ECE year (2020–2021). This pandemic context could mean that their four-year-old year in a high-quality ECE program represents an opportunity to "catch up" to the skills that they otherwise would have had if not for those disruptions and potentially start kindergarten similarly to how they would have in the absence of the pandemic. Variation in gains could also be observed for children who are Dual Language Learners (DLLs) vs. monolingual English speakers. DLLs were disproportionately negatively affected by early learning and care program closures (Holtzman et al., [<reflink idref="bib38" id="ref45">38</reflink>]). These children were presumably exposed to less English due to the pandemic that they would have been had they been while regularly attending in-person ECE. Thus, they may be scoring lower on assessments done in English but also demonstrating faster gains in domains – specifically those most related to instruction in English – when they return to in-person instruction. Similar variation in counterfactual experiences could differentiate learning gains in specific domains for children of different genders and from different racial/ethnic groups (Panchal et al., [<reflink idref="bib64" id="ref46">64</reflink>]).</p> <hd id="AN0183685179-5">Acelero Head Start Program as a Setting to Explore Learning Post-Pandemic</hd> <p>Unfortunately, as noted above, the country lacks systematic national data that can track learning and development in early childhood. However, some ECE program models offer opportunities to address these gaps. Head Start programs in particular are known nationally as offering high-quality ECE to children from families with low-incomes or experiencing poverty. They follow established and federally regulated standards for providing instruction to support learning in the targeted domains of language/literacy, cognitive skills (including math and executive functioning), approaches to learning, as well as social-emotional and perceptual/motor skills. Importantly, they exclusively focus on supporting school readiness and well-being for children from families with lower incomes, the group theorized to be most negatively affected by the pandemic. Children in poverty (defined as those in families with incomes under $25,000 per year) saw the largest declines in overall participation in remote ECE and in-person participation. Only 13% of children experiencing poverty were participating in in-person ECE (defined to include any type of childcare center, preschool, or Head Start) in December 2020 (Jung & Barnett, [<reflink idref="bib40" id="ref47">40</reflink>]).</p> <p>Acelero Learning, Inc. is a national network of Head Start programs that provide Head Start services to 3- and 4-year-old children and their families (as well as Early Head Start services for families with younger children). Acelero Learning Inc. operates delegate sites in four locations across the country: Philadelphia, PA/Camden, NJ; Monmouth/Middlesex County, NJ, Milwaukee/Racine, WI; and Clark County, NV. All Acelero Learning delegates implement a common program model and leverage its corresponding tools and content, but they customize execution of some aspects of the model based on local workforce conditions and differences in state-level policies and funding sources. The organization has received national accolades for providing high-quality instruction and using data to drive decision-making and teacher practices. In a case study report to the federal Department of Education, LiBetti ([<reflink idref="bib46" id="ref48">46</reflink>]) identified Acelero as a national exemplar of an effective approach for ECE, particularly with respect to instruction for Dual Language Learners (DLLs). Like most other ECE programs across the country, Acelero programs closed in the spring of 2020 and then offered students the choice of in-person or home-based instruction during the 2020–2021 year (Groom-Thomas et al., [<reflink idref="bib30" id="ref49">30</reflink>]). All students returned to in-person learning in the fall of 2021 although some centers experienced intermittent closures due to COVID exposures or other pandemic-related issues (e.g., low staffing due to teachers needing to isolate).</p> <hd id="AN0183685179-6">Current Study</hd> <p>There is a need for further empirical evidence on whether and how the pandemic influenced learning across distinct academic domains for 3- and 4-year-old children from racially/ethnically and socioeconomically marginalized groups. These findings can inform efforts to strengthen early learning systems during the pandemic recovery and to provide important information to elementary schools to implement appropriate supports as PreK children transition into the early grades. To this end, the current study aims to answer the following research questions:</p> <p></p> <ulist> <item> Two years after pandemic onset, to what extent did children enrolled in Acelero Head Start programs make gains in language, literacy, math, and executive functioning skills over three months of spring learning?</item> <p></p> <item> How do overall skills and gains for these children compare to data from national samples of Head Start and from Acelero Head Start programs <emph>before the pandemic</emph>? How do they compare to skills and gains observed in Acelero programs 1 year <emph>after the start of the pandemic</emph>? and</item> <p></p> <item> To what extent did these gains vary by key characteristics (age, race/ethnicity, gender, linguistic background, single parent household) within a demographically and geographically diverse sample of Head Start students?</item> </ulist> <p>Taken together, results will provide nuanced information on how Head Start students enrolled in Acelero programs across the country are learning in the first full year of in-person instruction after the start of the pandemic, shed light on the areas in need of targeted supports, and help determine whether there are subgroups that are demonstrating particular resilience to these challenges.</p> <hd id="AN0183685179-7">Method</hd> <p></p> <hd id="AN0183685179-8">Participants</hd> <p>The sample for the current study consists of 343 students attending Head Start programs operated by Acelero Learning, Inc. during the 2021–2022 school year. There were four Acelero delegate sites involved in the study: 1) Clark County, NV; 2) Camden, NJ/Philadelphia, PA; 3) Monmouth and Middlesex Counties, NJ; and 4) Milwaukee, WI. The research team randomly selected 37 Acelero centers, 81 classrooms, and 473 students from the broader population of all Acelero centers (<emph>N =</emph> 49), classrooms (<emph>N =</emph> 214), and children (<emph>N =</emph> 4,174) enrolled during the 2021–2022 academic year. The sample sizes were roughly similar across each of the four sites. This analytic sample of <emph>N =</emph> 343 – about three quarters of the original group of students enrolled in the study – represents the non-attrited students with valid assessment data in both the winter and spring of 2022.</p> <p>Demographic data on the study sample – including parent and family characteristics – is displayed in Table 1. As illustrated, 31% of students are non-Hispanic Black, 11% are non-Hispanic White, 52% are Hispanic, and 6% are non-Hispanic Multiracial/other. Forty-three percent of students are Dual Language Learners and 47% are female. More than half (56%) of students were three years old at the start of the school year, while 38% were four and a smaller percentage (6%) were two (but turned three during the school year). Levels of parental education were fairly diverse; at the beginning of the academic year 14% of parents had less than a high school degree, 45% graduated high school or had a GED, 30% had completed some college, and 11% had a bachelor's degree or higher. Children came from families with low incomes (<130% of the federal poverty line) or were eligible for Head Start due to being in foster care, experiencing homelessness, or receiving public assistance. As illustrated in more detail in Appendix A, the students enrolled in the current study were demographically representative of the broader population of students who attended one of the Acelero Head Start centers during the 2021–2022 school year. They were also representative of the broader pool of students (<emph>N =</emph> 473) that were originally sampled to participate in the study and were assessed in the winter of 2022 (see more on data collection in the procedure section below).</p> <p>Table 1. Student sample sociodemographic characteristics.</p> <p> <ephtml> <table><thead><tr><td>Characteristic (%)</td><td>Analytic sample</td></tr></thead><tbody><tr><td>Race/Ethnicity</td><td /></tr><tr><td> Hispanic</td><td>52.48</td></tr><tr><td> Non-Hispanic White</td><td>10.50</td></tr><tr><td> Non-Hispanic Black</td><td>31.20</td></tr><tr><td> Non-Hispanic Multiracial/Other</td><td>5.83</td></tr><tr><td>Female</td><td>46.65</td></tr><tr><td>Age at district Kindergarten cutoff</td><td /></tr><tr><td> Two</td><td>5.54</td></tr><tr><td> Three</td><td>55.98</td></tr><tr><td> Four</td><td>38.48</td></tr><tr><td>Dual language learner</td><td>43.44</td></tr><tr><td>Enrollment year</td><td /></tr><tr><td> Enrolled PY 2020 and 2021</td><td>29.74</td></tr><tr><td> Enrolled only PY 2021</td><td>70.26</td></tr><tr><td>Family Head Start eligibility</td><td /></tr><tr><td> Foster care</td><td>2.04</td></tr><tr><td> Homeless</td><td>6.41</td></tr><tr><td> Receives public assistance</td><td>5.83</td></tr><tr><td> Income ≤100% FPL</td><td>63.56</td></tr><tr><td> Income 101% − 130% FPL</td><td>13.99</td></tr><tr><td> Income >130% FPL</td><td>7.87</td></tr><tr><td>Family highest educational attainment</td><td /></tr><tr><td> Less than high school degree</td><td>13.70</td></tr><tr><td> High school diploma or GED</td><td>45.48</td></tr><tr><td> Associate's degree or some college</td><td>29.74</td></tr><tr><td> Bachelor's degree or more</td><td>11.08</td></tr><tr><td>Parent/guardian employed or enrolled in education/job training</td><td>72.01</td></tr><tr><td>Single parent household</td><td>55.39</td></tr><tr><td>Sample size</td><td>343</td></tr></tbody></table> </ephtml> </p> <p>1 The analytic sample only includes students with at least one valid assessment in both the winter and spring.</p> <p>We compared the attrited students (<emph>N =</emph> 130) to students in the analytic sample (<emph>N =</emph> 343) in Appendix A. Attrited students and students in the analytic sample were broadly similar, although there were some significant differences in the demographic composition of the groups. The proportion of non-Hispanic Black students was significantly higher among attrited students and the proportion of students with family incomes 100% or less of the FPL and with a bachelor's degree or higher was significantly lower. In terms of delegate representation, the analytic sample included a significantly greater proportion of children from Clark County and Camden/Philadelphia and a significantly smaller proportion from Monmouth/Middlesex and Wisconsin than the attrited sample.</p> <p>On average, teachers in participating classrooms had taught for 13.30 (<emph>SD</emph> = 8.82) years total, had worked at their center for 5.30 (<emph>SD</emph> = 3.82) years, and had served as a Head Start teacher for an average of 7.55 (<emph>SD</emph> = 8.05) years. Eighty-seven percent of teachers reporting receiving coaching and training in the past year, and almost all teachers − 98% – reported receiving some feedback from instructional coaches in the past year.</p> <hd id="AN0183685179-9">Setting</hd> <p>All students in the current study were enrolled in a Head Start program operated by Acelero Learning during the 2021–2022 year. Founded in 2001 (LiBetti, [<reflink idref="bib46" id="ref50">46</reflink>]), all Acelero Head Start centers implement a similar core, sequenced curriculum – called Ready to Shine – and use the same content, materials, and activities. Ready to Shine provides teaching staff with foundations for delivering content-rich themes of instruction and differentiating instruction to meet the needs of all children in the classroom. Acelero Learning also created specific materials, such as sample lesson plans and sequences, to support teachers in implementing the curriculum. The model's professional development approach includes workshop-style trainings, individual coaching, Professional Learning Communities (PLCs), and digital professional development. All teachers receive training on components of an established Teacher Success Rubric and are assigned to training on different topics (e.g., "conducting read-alouds" or "language modeling techniques") based on self-assessment scores and feedback from coaches. Throughout the year, teachers and center directors complete a minimum of one coaching cycle each month, though new or struggling teachers may receive more frequent sessions.</p> <hd id="AN0183685179-10">Procedures</hd> <p>The Institutional Review Boards at the lead organization for this study approved the human subjects plan prior to the commencement of study activities. The project name is Acelero Learning: A Research-Practice Partnership and the IRB approval number is 860745-02.</p> <hd id="AN0183685179-11">Center, Classroom, and Student Enrollment</hd> <p>The research team worked closely with Acelero leadership to create a sampling plan for the study. We randomly selected 10 Acelero centers within each of the four delegate sites for a total of 40 centers across sites. Centers had to have at least two Head Start classrooms to be eligible for the study. A total of 37 eligible centers agreed to participate in data collection activities. Within each of those centers we then randomly selected two classrooms to participate in the study. Classrooms needed to have at least 8 students enrolled to be eligible for the project. A total of 81 classrooms across the four delegate sites participated in the study in both winter and spring. All the selected classrooms and lead teachers agreed to participate in data collection activities, consisting of student assessments and videotaped observations of classroom instruction and teacher practices. Finally, we randomly selected 5–6 students within each of the participating classrooms to participate in the child assessment activities in the winter of 2022. This approach yielded a total target sample size of 473 students who participated in direct child assessments in the winter of 2022. All students' parents agreed to participate in research and program improvement activities conducted by Acelero when they enrolled their child in their Head Start center.</p> <hd id="AN0183685179-12">Direct Assessments</hd> <p>Acelero staff and the research team trained data collectors before starting each data collection period. Data collectors participated in a 1.5-day training and then had to pass a mock reliability assessment with a senior member of the research team to ensure that they were able to follow all required procedures when conducting assessments in the field. In addition, all assessors completed 1–2 quality assurance checks during each data collection period where they recorded themselves completing an assessment and submitted it for feedback.</p> <p>Our study uses assessment information collected during two data collection periods – winter 2022 (February–March 2022) and spring 2022 (May–June 2022). The team assessed all students in English to provide the Acelero program with comparable information across the full sample. However, we also examined data from complementary preLAS (preLAS; De Avila & Duncan, [<reflink idref="bib21" id="ref51">21</reflink>]) Simon Says and Art Show tests collected by Acelero programs to understand whether children had sufficient English skills to complete the battery of assessments in that language (Barrueco et al., [<reflink idref="bib8" id="ref52">8</reflink>]). Acelero administered the preLAS to 144 students (41.98% of all students and to 95.97% of the dual language learners) in the analytic sample who had been identified by their parents as speaking a language other than English at home. We re-analyzed the data collected by Acelero and applied screening procedures used in similar field-based ECE studies to determine the number of students who would have been screened out of an English assessment in other studies (Mattera et al., [<reflink idref="bib51" id="ref53">51</reflink>]; McCormick, Weiland, Hsueh, Pralica, Moffett, et al., [<reflink idref="bib55" id="ref54">55</reflink>]; Weiland et al., [<reflink idref="bib76" id="ref55">76</reflink>]). We found that 65 children in the current study would not have passed the preLAS when used as a language screener. We decided to retain all these children in our analyses to ensure that the data were generalizable to the population. However, as a robustness check (see Appendix C) we removed these 65 children from the analyses and refit all our models to understand how sensitive findings were to the decision to include valid scores on English assessments for these students.</p> <hd id="AN0183685179-13">Videotaped Classroom Observations</hd> <p>Acelero staff also collected one videotaped observation of instruction (Mean = 58.32 min of total time observed; SD = 6.12 min) during the spring of 2022. Trained and reliable coders – certified by the measure developer – coded these videotapes on the Classroom Assessment Scoring System PreK (CLASS; Pianta et al., [<reflink idref="bib67" id="ref56">67</reflink>]). We report descriptive statistics from the CLASS data and use the overall CLASS score as a covariate in our final set of models. All coders working on the study had participated in a 2-day CLASS training led by a certified trainer and then established reliability on a set of master codes created by the developers. Coding of each videotape started once the instructional time began. Coders used cycles of 15 min for observing and 10 min for scoring (they stopped the tape during the coding period to maximize observational time), which they repeated up to 4 times for each videotape. We averaged scores across the 4 segments to generate overall scores for each classroom. We double-coded 20% of the observations to assess interrater reliability. Of the 10 dimensions, two reached a moderate level of interrater reliability (κ = 0.60), six reached a substantial level (κ = 0.63–0.70), and the remaining two demonstrated strong reliability (κ = 0.82, 0.98).</p> <hd id="AN0183685179-14">Administrative Data from Acelero</hd> <p>Parents provided a good deal of information to Acelero on their demographic characteristics when they enrolled their child in Head Start. Much of this information and data collection is required by Head Start and helps to determine families' eligibility for the program. The current study was able to leverage that information and access demographic data on children and parents with these data sources being used as both covariates and key predictors in analytic models.</p> <hd id="AN0183685179-15">Measures</hd> <p>The research team collected the same battery of student-level assessments at both data collection time points. Importantly, for four of the assessments (PPVT-V, WJAP, TOPEL-Print, TOPEL-PA; described in detail below), we calculated and used both raw and standardized scores to answer our research questions. For each measure, the raw score represents the total number of items that the child answered correctly. We examined raw scores to calculate children's gains in terms of months of learning (see more below). The standardized score represents how the child performed on the assessment relative to the national average for children of their same age. A standardized score of 100 (range of 86–114) represents the national average while a score of 85 or less indicates a below average score and a score of 115 or higher represents an above average score (Adeyemi, [<reflink idref="bib1" id="ref57">1</reflink>]; Andrade, [<reflink idref="bib4" id="ref58">4</reflink>]; Hunter & Hamilton, [<reflink idref="bib39" id="ref59">39</reflink>]). We used standardized scores to understand how Acelero children were performing relative to a national sample, to compare children across ages, and to understand gains relative to pre-pandemic norms.</p> <hd id="AN0183685179-16">Receptive Language Skills</hd> <p>We used the Peabody Picture Vocabulary Test-V (PPVT V; Dunn, [<reflink idref="bib24" id="ref60">24</reflink>]) to directly assess children's receptive language skills in the winter and spring of 2022. The PPVT-V is a nationally normed measure that has been used widely in diverse samples of young children. It requires children to choose (verbally or nonverbally) which of four pictures best represents a stimulus word. The test has excellent split-half and test—retest reliability estimates, as well as strong qualitative and quantitative validity properties (Dunn, [<reflink idref="bib24" id="ref61">24</reflink>]).</p> <hd id="AN0183685179-17">Math Skills</hd> <p>We used the Woodcock Johnson Applied Problems-IV (WJAP-IV) (Schrank et al., [<reflink idref="bib70" id="ref62">70</reflink>]) subtest to directly assess children's math skills in the winter and spring of the calendar school year. The WJAP-IV direct assessment is a numeracy and early mathematics measure that requires children to perform calculations and to analyze and solve arithmetic problems. The split-half reliability for the Applied Problems test is.93 (Woodcock et al., [<reflink idref="bib80" id="ref63">80</reflink>]).</p> <hd id="AN0183685179-18">Literacy Skills</hd> <p>We measured print knowledge and phonological awareness in the winter and spring by assessing children on two subtests of the <emph>Test of Preschool Early Literacy</emph> (TOPEL; Hayward et al., [<reflink idref="bib36" id="ref64">36</reflink>]). This assessment provides valid and reliable raw scores, standard scores, and percentiles (Lonigan et al., [<reflink idref="bib48" id="ref65">48</reflink>]). We first administered the 36-item <emph>print knowledge</emph> subtest, which measures a child's knowledge of the alphabet and written language conventions. The child is asked to identify letters and words, point to them, and name them. We then administered the 27-item <emph>phonological awareness</emph> subset, which measures word elision and blending abilities. The child is asked to say a word, and then say what is left after dropping certain sounds. Finally, he or she is asked to listen to certain sounds and combine them to form a word. As noted above, we report both raw and standardized scores for both subtests.</p> <hd id="AN0183685179-19">Executive Functioning Skills</hd> <p>Lastly, we used the Minnesota Executive Function Scale (MEFS; Carlson, [<reflink idref="bib15" id="ref66">15</reflink>]) to assess children's executive functioning skills in the winter and spring. The MEFS is a tablet-based version of the Dimensional Change Card Sort (DCCS; Zelazo, [<reflink idref="bib81" id="ref67">81</reflink>]) with increasing levels of difficulty to extend its sensitivity. Children must flexibly use rules to guide their decision-making on a trial-by-trial basis, following increasingly complex rules. The MEFS engages working memory, inhibition, and cognitive flexibility (Perone et al., [<reflink idref="bib66" id="ref68">66</reflink>]), the three critical domains of executive functioning (EF; Diamond, [<reflink idref="bib22" id="ref69">22</reflink>]). It is standardized from 2 years to adulthood (Carlson, [<reflink idref="bib15" id="ref70">15</reflink>]) with initial difficulty based on age and adjustments based on performance. The MEFS has been normed in the United States on over 51,000 children with a test–retest reliability of 0.86 (Carlson, [<reflink idref="bib15" id="ref71">15</reflink>]). The MEFS is automatically scored by the testing application, using a proprietary algorithm and taking both accuracy and response time into account to calculate the total score. That score – which ranges from 0 to 100 can also be used to compare to national norms of same-age children in months (Carlson, [<reflink idref="bib15" id="ref72">15</reflink>]). The research team collected the MEFS in both the winter and spring. It is the only assessment collected that has only a standardized form (all others have both raw and standardized scores).</p> <hd id="AN0183685179-20">Classroom Teacher Practices</hd> <p>We measured global classroom teacher practices using the Classroom Assessment Scoring System (CLASS) PreK (Pianta et al., [<reflink idref="bib67" id="ref73">67</reflink>]). CLASS includes ten dimensions that map onto three domains of teacher–child interactions: Emotional Support, Classroom Organization, and Instructional Support. All dimensions are directly scored on a 7-point scale, where a score of 7 represents high quality except for negative climate which is reverse-coded (Burchinal et al., [<reflink idref="bib14" id="ref74">14</reflink>]). Given that we aimed to use the CLASS only as a covariate in our final models – to understand whether findings were robust after controlling for teacher practices – we created one overall quality score (ranging from 1 to 7) by averaging across the three CLASS domains.</p> <hd id="AN0183685179-21">Student Characteristics from Administrative Data</hd> <p>Using administrative data from Acelero we first created four binary, mutually exclusive race/ethnicity indicators: Hispanic, non-Hispanic White, non-Hispanic Black, and non-Hispanic Multiracial/Other, coding 1 if the children was a member of that group and 0 otherwise. Non-Hispanic White students served as the reference group in our predictive models. We also created a dummy to describe whether a child was a Dual Language Learner (DLL), coding 1 if the child had a primary language that was not English or communicated with a primary or secondary guardian in a language other than English, and 0 otherwise. We also created covariates for gender (female = 1; non-female = 0), whether students were in their second (<reflink idref="bib1" id="ref75">1</reflink>) or first (0) year participating in an Acelero program, and whether their birthdate made them eligible to enroll in Head Start as a four-year old (<reflink idref="bib1" id="ref76">1</reflink>) or at an age younger than four (0).[<reflink idref="bib2" id="ref77">2</reflink>]</p> <hd id="AN0183685179-22">Family Characteristics</hd> <p>Finally, we used information on Head Start eligibility (since Head Start is a Means-tested program) to create blunt indicators (coding 1 for the presence of the characteristic and 0 otherwise) of families' socioeconomic status and economic need within four mutually exclusive categories: 1) student experienced foster care, homelessness, or received public assistance in prior year; 2) student's family income was at or below the federal poverty line (FPL); 3) student's family income was between 101% and 130% of the FPL; and 4) student's family income was greater than 130% of the FPL.[<reflink idref="bib3" id="ref78">3</reflink>] In our predictive models, students with families in the highest income group (income >130% FPL) made up the reference category. We then described each student's level of parental education as less than a high school diploma/GED, high school graduate, some college/Associate's degree, or a four-year college degree or higher. This final group served as the reference category in our models. Finally, we created a binary indicator to describe whether the parent was currently enrolled in an education or job training program (yes = 1; no = 0).</p> <hd id="AN0183685179-23">Analytic Approach</hd> <p></p> <hd id="AN0183685179-24">Missing Data</hd> <p>As noted above, we restricted this analysis to children who had valid assessment data for both the winter and spring – a group demographically representative of the winter assessment sample – because our goal was to examine learning gains during this period. After taking these criteria into account, no students were missing any student or family-level covariates. Given the lack of missingness, we present results using complete case analysis in the main set of results.</p> <hd id="AN0183685179-25">Research Questions 1 and 2: Descriptive Analysis</hd> <p>To respond to our first research question – what are students' observed gains? – we used summary statistics (Mean, SDs) to describe students' raw and standardized scores on each of the assessments in the winter and spring. By subtracting the winter score from the spring score, we were able to calculate a change score for each of the assessments. We then used paired samples <emph>t</emph>-tests to determine whether winter and spring assessment scores differed significantly.</p> <p>In order to compare the magnitude of the change in the raw scores across outcomes (which were on different scales and then impossible to compare directly), we divided each change score by the standard deviation of the outcome. This allowed us to calculate the gain in standard deviation units (Belsky et al., [<reflink idref="bib9" id="ref79">9</reflink>]). Finally, we divided each of those standardized change scores by the average amount of time (in months) between the winter and spring assessment (2.90 months) to express change as months of learning and determine whether the gains were larger, similar, or smaller than expected given the aggregate amount of time between assessments. This process also allowed us to compare months of learning against empirical benchmarks identified by Lipsey et al. ([<reflink idref="bib47" id="ref80">47</reflink>]) and Hill et al. ([<reflink idref="bib37" id="ref81">37</reflink>]) over the last 15 years.</p> <p>We then examined observed gains in students' standardized assessment scores – or the extent to which they changed relative to national norms established with children assessed before the pandemic. Standardized scores adjust for age and typical skill development such that a score of 100 always represents the national average for each age level. Since standardized scores for the assessments used in this study were normed normed to children sampled and assessed before the pandemic, observed changes in standardized scores allow us to understand Acelero students' rate of skill growth relative to pre-pandemic same-aged peers. Increases in sample standardized scores indicate that children are gaining <emph>faster</emph> than the average child in the norming sample. Because standardized scores take into account age, they also address concerns that raw score skill gains may solely be a function of expected development.</p> <p>We then built on the current sample's observed standardized score gains to answer research question 2. We make comparisons to the standardized scores of three prior samples: 1) a representative and demographically similar sample of Acelero Head Start students enrolled in the program during the 2011–2012 academic year (Barnett & Jung, [<reflink idref="bib7" id="ref82">7</reflink>]),[<reflink idref="bib4" id="ref83">4</reflink>] 2) a nationally representative sample of Head Start students participating in the 2014–2015 wave of Head Start FACES data collection effort (Aikens et al., [<reflink idref="bib2" id="ref84">2</reflink>]); and 3) a demographically similar sample of students drawn from the four sites enrolled in in-person instruction in 2020–2021, during the first full post-pandemic academic year (Groom-Thomas et al., [<reflink idref="bib30" id="ref85">30</reflink>]). Demographics (where available) of the three samples are compared to the current analytic sample in Appendix B. Demographics of the current sample and prior Acelero samples are generally similar, although the current sample has notably fewer 4-year-olds and more students in their first year of pre-K. All three Acelero samples had higher proportions of DLLs than the FACES sample and served larger proportions of Hispanic and Non-Hispanic Black students. We examined the overall standardized scores and the growth between assessments of prior samples to contextualize the gains observed in our current sample. Additionally, Barnett and Jung ([<reflink idref="bib7" id="ref86">7</reflink>]) described variation in performance by a similar set of subgroups to our study, including by age, race/ethnicity, and delegate, allowing for comparison of standardized scores differences by subgroup in our own sample. We note that each of these samples collected data in the fall and spring of their academic years, as opposed to the winter and spring data collection in our study. We would therefore expect greater growth in the other samples, as compared to in our sample. By assuming linear growth between datapoints, we were able to understand where we might expect children's skills to be in winter in the other samples, as well as were able to directly compare magnitudes of skill levels in the spring period during which all studies collected data.</p> <hd id="AN0183685179-26">Research Question 3: Multi-level Modeling</hd> <p>We then used multi-level modeling to answer our third research question. Our intention with this approach was to build up to a robust model structure with covariates for key demographic factors and an indicator of classroom quality (Model 4). Given that the model controls for winter scores, coefficients on the binary characteristics in the model therefore represent differences in expected spring scores between those with and without the characteristic who have the same baseline scores. We used these coefficients and p-values to describe the differences in the growth of subgroups of interest. As a final step, we standardized significant coefficients by dividing by the winter standard deviation of the outcome, allowing us to express subgroup differences in standard deviation units and as months of learning.</p> <p>Because students (<emph>N =</emph> 343) in our sample were nested within classrooms (<emph>N =</emph> 80) nested within schools (<emph>N =</emph> 37), we first fit unconditional models for the spring assessment raw scores to calculate intraclass correlations (ICCs) and examine the extent to which observations were non-independent at these levels. When we fit two-level models examining variation between schools/centers and classrooms <emph>separately</emph> we found that there was more variation explained by classroom-level membership (ICCs ranged from.14 to.24) than by school/center-level membership (ICCs ranged from.05 to.14). We compared those results to a three-level model where we found very limited variation at the center/school-level (0% for three of the five outcomes; 4% and 6% for print knowledge and phonological awareness, respectively), after accounting for classroom membership (.12 to.18). Given these results, we fit two-level models with random intercepts for classrooms and fixed effects for sites when exploring how gains varied for key subgroups (Snijders & Bosker, [<reflink idref="bib71" id="ref87">71</reflink>]).</p> <p>We built up to an increasingly complex model structure to ensure our results were robust to the addition of controlling demographic factors. To start, we regressed each outcome in the spring of the Head Start year on the level of the outcome measured in the winter, adjusting for delegate site fixed effects and for the number of days between assessments (Model 1). The coefficient on the winter level of the outcome represents the association between winter and spring scores on average, net of delegate site. We then added our demographic variables of interest (four-year-old eligibility age in fall, indicators for race/ethnicity with white as the reference group, DLL, and female; Model 2) to the base model. This model also controlled for the number of days that the child was present in the Head Start program during the academic year, and whether he or she had enrolled in Acelero during the prior academic year. The coefficient on each of these variables represents the difference in the gain in that skill between winter and spring for that particular group compared to the reference group. The associated standard error and <emph>p</emph>-value indicate whether the change was statistically significant. We then added a further set of confounding covariates (indicator of Head Start eligibility, with income >130% FPL as the reference group, parental education with BA as the reference group, whether the parent was currently enrolled in education or job training, and whether the child had a single parent). Model 3 allowed us to examine how the point estimates changed after we accounted for further confounding demographic characteristics. Finally, in Model 4 we included one additional control for the overall quality of teacher practices to understand how robust findings were to this confounding factor. Descriptive analyses revealed that CLASS scores for this sample ranged from 5.91 (SD = 0.59) for emotional support, to 5.56 (SD = 0.80) for classroom organization, to 2.69 (SD = 0.80) for instructional support. These indicators of quality were similar to those found in recent but pre-pandemic national studies of Head Start (Aikens et al., [<reflink idref="bib2" id="ref88">2</reflink>]). The instructional quality domain was lower than the one reported by Barnett and Jung ([<reflink idref="bib7" id="ref89">7</reflink>]) in their work in Acelero programs done before the pandemic (Mean = 3.34, SD = 1.14). However, classroom organization in the current study was higher than this measure collected in that earlier work (Mean = 4.91, SD = 1.00). Emotional support was almost exactly the same across the studies. For any statistically significant results, we calculated a standardized association by dividing the coefficient on the variable of interest by the standard deviation of the outcome. This allowed us to express differences in gains in standard deviation units and months of learning (aligned with benchmarks from Lipsey et al. ([<reflink idref="bib47" id="ref90">47</reflink>]) discussed above).</p> <hd id="AN0183685179-27">Results</hd> <p></p> <hd id="AN0183685179-28">Research Question 1: Gains in Skills from Winter to Spring</hd> <p>Assessment scores used to answer the first research question are presented in Table 2. We discuss the raw scores first, followed by the standardized scores. As expected – and illustrated in the right panel of Table 2 – children made statistically significant gains in all four of the assessments with raw scores. Put into laymen's terms, this means that children had more knowledge in each of these four domains in the spring than they did in the winter. On average students improved by about a fifth of a standard deviation during this time with the smallest gain (.18 SDs) being in print knowledge – the domain where students had generally higher scores to start – and the largest gain being in phonological awareness (.30 SDs), the domain where they had the lowest scores to start.[<reflink idref="bib5" id="ref91">5</reflink>]</p> <p>Table 2. Gains in raw and standardized scores on academic and cognitive skills assessments: Winter 2022 – Spring 2022.</p> <p> <ephtml> <table><thead><tr><td /><td /><td>Standardized scores</td><td>Raw Scores</td></tr><tr><td>Assessment</td><td><italic>n</italic></td><td>Winter</td><td>Spring</td><td>Diff.</td><td /><td>Winter</td><td>Spring</td><td>Diff.</td><td /><td>Std. diff.</td><td>Months of learning</td><td>Learning relative to time</td></tr></thead><tbody><tr><td>Language (PPVT)</td><td>337</td><td>86.05</td><td>88.20</td><td>2.15</td><td>***</td><td>61.81</td><td>69.34</td><td>7.54</td><td>***</td><td>0.27</td><td>2.89</td><td>Same</td></tr><tr><td>Math (WJAP)</td><td>342</td><td>83.84</td><td>85.16</td><td>1.32</td><td /><td>7.029</td><td>8.216</td><td>1.19</td><td>***</td><td>0.26</td><td>2.82</td><td>Same</td></tr><tr><td>Print knowledge (TOPEL-PK)</td><td>342</td><td>94.88</td><td>95.23</td><td>0.35</td><td /><td>12.33</td><td>14.32</td><td>1.99</td><td>***</td><td>0.18</td><td>1.96</td><td>Slower</td></tr><tr><td>Phonological awareness (TOPEL-PA)</td><td>342</td><td>83.93</td><td>86.37</td><td>2.43</td><td>**</td><td>9.684</td><td>11.45</td><td>1.76</td><td>***</td><td>0.30</td><td>3.17</td><td>Faster</td></tr><tr><td>Executive functioning (MEFS)</td><td>333</td><td>96.02</td><td>96.65</td><td>0.64</td><td /><td>36.50</td><td>43.04</td><td>6.54</td><td>***</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note:</emph> Table only summarizes scores for students assessed at both time points and with valid assessments. The mean time between winter and spring assessments was 2.90 months. p-values are from paired t-tests. ***indicates <emph>p</emph> <.001, **indicates <emph>p</emph> <.01, *indicates <emph>p</emph> <.05.</p> <p>Over a three-month period in the spring of 2022 (Mean = 2.9 months, SD = 0.4 representing the period of time between the winter and spring assessment), Acelero Head Start children made statistically significant gains in their overall language, print knowledge, phonological awareness, executive functioning, and math knowledge (see raw scores in right panel in Table 2). Gains in raw scores are developmentally expected, even without exposure to center-based ECE. Thus, disentangling the impact of Acelero programming from expected developmental growth is impossible.</p> <p>However, we examined raw scores to draw comparisons to prior research using raw scores to estimate skill growth per month of learning (e.g., Lipsey et al., [<reflink idref="bib47" id="ref92">47</reflink>]). Translated into months of learning, children's gains in phonological awareness were faster than would be expected given the amount of time between assessments (3.19 months). Children also made statistically significant standardized score improvements in phonological awareness and language, thus gaining relative to pre-pandemic national norms (see standardized scores in left panel of Table 2). Gains were aligned with what would be expected in language (2.89 months) and math (2.82 months) and somewhat slower than expected in print knowledge (1.96 months). This latter result may reflect children generally scoring relatively high on this domain and having less room to grow by spring.</p> <p>The left panel of Table 2 summarizes students' standardized scores in the winter and spring 2022. As illustrated, in the winter, students scored the highest relative to national pre-pandemic norms on executive functioning (Mean = 96.02; SD = 8.70) and print knowledge (Mean = 94.88; SD = 15.03). Scores were generally in the average range for these domains. Scores were lower – and in the below average range – for language, math, and phonological awareness in the winter. After comparing these to spring scores, we found that students in Acelero Head Start programs made statistically significant gains – relative to national pre-pandemic norms – in language (change = 2.15, <emph>p</emph> <.001) and phonological awareness (change = 2.43, <emph>p</emph> <.01) skills. Relative to pre-pandemic national norms, they scored similarly in the spring on assessments of print knowledge, math, and executive functioning as they did in the winter.</p> <hd id="AN0183685179-29">Research Question 2: Comparing Results to Pre- and Post-Pandemic Scores from Similar Samples</hd> <p>In general, Acelero Head Start children in the current study scored similarly (or higher) on assessments of language (see Figure 1) when compared to other Acelero Head Start children assessed before the pandemic (current sample PPVT spring score = 86.05 points, pre-pandemic PPVT spring score = 84.67 points; Barnett & Jung, [<reflink idref="bib7" id="ref93">7</reflink>]). Compared to a pre-pandemic nationally representative sample of Head Start children (see Figure 1; Aikens et al., [<reflink idref="bib2" id="ref94">2</reflink>]), overall language scores were lower in the post-pandemic sample relative to pre-pandemic national norms (FACES 2014–2015 mean PPVT spring score = 91.8 points). However, <emph>gains</emph> in language were consistent with those observed in pre-pandemic samples (current sample winter – spring gains = 2.15 points vs. FACES fall – spring gains = 1.9 points). Overall math skills for the current sample, relative to pre-pandemic norms, were significantly lower than both the pre-pandemic national Head Start sample and the pre-pandemic Acelero Head Start sample (current sample spring WJAP score = 85.15 points, FACES spring score = 95.9 points, pre-pandemic Acelero spring score = 97.14 points); gains in math appeared similar across groups (current sample winter – spring gains = 1.31 points, FACES fall – spring gains = 2.0 points, pre-pandemic Acelero fall – spring gains = 4.12 points).</p> <p>Graph: Figure 1. Gains in language and math for current sample and pre-pandemic comparison groups.</p> <p>It is also helpful to compare gains to children who were enrolled in Acelero during the prior school year, when schools continued to experience significant disruptions and closures during the pandemic (Groom-Thomas et al., [<reflink idref="bib30" id="ref95">30</reflink>]). Comparison of the scores across study domains suggested that Acelero Head Start children in the spring of 2022 made comparable or faster gains in language and math skills compared to Acelero Head Start children who were assessed virtually during the prior (2020–2021) academic year but smaller gains in print knowledge skills.</p> <hd id="AN0183685179-30">Research Question 3: Variation in Gains for Key Demographic Groups</hd> <p>In our fully controlled models, we found somewhat limited evidence that the gains children made in key academic and cognitive domains varied for subgroups of Acelero Head Start students. We summarize the gains in raw – or overall – scores for all assessments but the MEFS in Table 3 and include findings for standardized scores in Appendix D. As illustrated, we found that gains in language (three-year-olds = 70.92; four-year-olds = 75.75; γ = 4.83, SE = 2.18, <emph>p</emph> <.05; Std. association =.17 or 1.85 months of learning), phonological awareness (three-year-olds = 11.01; four-year-olds = 13.19; γ = 2.18, SE =.61, <emph>p</emph> <.001; Std. association =.36 or about four months of learning), and math (three-year-olds = 6.86; four-year-olds = 7.94; γ = 1.08, SE =.37, <emph>p</emph> <.01; Std. association =.24 or about 2.5 months of learning) were larger for four-year-olds compared to younger students, even after controlling for the overall CLASS score. Children who had also enrolled in Acelero in 2020 (a group mostly made up by four-year-olds) also experienced larger gains in language than peers who were not enrolled in Acelero during the prior year (enrolled in 2020–2021 = 75.88; not enrolled = 79.80; γ = 5.08, SE = 2.21, <emph>p</emph> <.05; Std. association =.18 or about 2 months of learning).</p> <p>Table 3. Gains in academic and cognitive skills across demographic groups (raw scores).</p> <p> <ephtml> <table><thead><tr><td /><td>Language</td><td>Math</td><td>Print knowledge</td><td>Phonological awareness</td><td>Executive functioning (std)</td></tr><tr><td>Predictor</td><td>γ</td><td /><td>SE</td><td>γ</td><td /><td>SE</td><td>γ</td><td /><td>SE</td><td>γ</td><td /><td>SE</td><td>γ</td><td /><td>SE</td></tr></thead><tbody><tr><td>Intercept</td><td>63.81</td><td>***</td><td>8.67</td><td>9.62</td><td>***</td><td>1.67</td><td>12.76</td><td>***</td><td>3.23</td><td>16.11</td><td>***</td><td>3.11</td><td>100.04</td><td>***</td><td>5.89</td></tr><tr><td>Winter assessment score</td><td>0.79</td><td>***</td><td>0.04</td><td>0.76</td><td>***</td><td>0.04</td><td>0.91</td><td>***</td><td>0.03</td><td>0.53</td><td>***</td><td>0.05</td><td>0.29</td><td>***</td><td>0.06</td></tr><tr><td>Days between assessments</td><td>0.17</td><td>*</td><td>0.08</td><td>0</td><td /><td>0.02</td><td>0.03</td><td /><td>0.03</td><td>0.01</td><td /><td>0.03</td><td>0.03</td><td /><td>0.05</td></tr><tr><td>Age 4 eligibility</td><td>4.83</td><td>*</td><td>2.18</td><td>1.08</td><td>**</td><td>0.37</td><td>1.12</td><td /><td>0.73</td><td>2.18</td><td>***</td><td>0.61</td><td>1.39</td><td /><td>1.2</td></tr><tr><td>Race/ethnicity</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Non-Hispanic Black</td><td>4.15</td><td /><td>3.04</td><td>−0.47</td><td /><td>0.55</td><td>−0.36</td><td /><td>1.06</td><td>−0.14</td><td /><td>0.89</td><td>−2.8</td><td /><td>1.85</td></tr><tr><td> Hispanic</td><td>2.55</td><td /><td>3.06</td><td>−1.04</td><td /><td>0.55</td><td>−0.83</td><td /><td>1.06</td><td>−0.93</td><td /><td>0.89</td><td>−2.41</td><td /><td>1.85</td></tr><tr><td> Other race/Multiracial</td><td>3.9</td><td /><td>4.33</td><td>−1.18</td><td /><td>0.76</td><td>−3.59</td><td>*</td><td>1.47</td><td>−0.54</td><td /><td>1.22</td><td>−2.02</td><td /><td>2.55</td></tr><tr><td>DLL</td><td>1.48</td><td /><td>2.37</td><td>0.01</td><td /><td>0.41</td><td>0.1</td><td /><td>0.78</td><td>−1.05</td><td /><td>0.65</td><td>0.78</td><td /><td>1.38</td></tr><tr><td>Female</td><td>−5.89</td><td>***</td><td>1.66</td><td>−0.16</td><td /><td>0.29</td><td>1.05</td><td /><td>0.57</td><td>0.49</td><td /><td>0.46</td><td>1.88</td><td /><td>0.99</td></tr><tr><td>Total days present</td><td>−0.03</td><td /><td>0.04</td><td>0</td><td /><td>0.01</td><td>0.01</td><td /><td>0.01</td><td>0.01</td><td /><td>0.01</td><td>0.01</td><td /><td>0.02</td></tr><tr><td>Acelero enrollment in 2020</td><td>5.08</td><td>*</td><td>2.21</td><td>0.51</td><td /><td>0.4</td><td>−0.65</td><td /><td>0.77</td><td>0.54</td><td /><td>0.66</td><td>0.02</td><td /><td>1.37</td></tr><tr><td>Delegate</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Clark County, NV</td><td>4.88</td><td /><td>2.67</td><td>0.21</td><td /><td>0.52</td><td>−1.01</td><td /><td>0.99</td><td>0.8</td><td /><td>0.96</td><td>5.02</td><td>**</td><td>1.81</td></tr><tr><td> Camden/Philadelphia</td><td>3.95</td><td /><td>2.69</td><td>−0.92</td><td /><td>0.52</td><td>−0.56</td><td /><td>1.01</td><td>−1.62</td><td /><td>0.98</td><td>2.53</td><td /><td>1.87</td></tr><tr><td> Wisconsin</td><td>−2.42</td><td /><td>2.77</td><td>−0.34</td><td /><td>0.54</td><td>−0.76</td><td /><td>1.03</td><td>0.73</td><td /><td>0.98</td><td>1.39</td><td /><td>1.87</td></tr><tr><td>Head Start eligibility</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Categorical: foster care, homeless, or receives public assistance</td><td>5.57</td><td /><td>3.98</td><td>0.91</td><td /><td>0.71</td><td>1.04</td><td /><td>1.37</td><td>−0.74</td><td /><td>1.14</td><td>−0.1</td><td /><td>2.37</td></tr><tr><td> Income ≤100% FPL</td><td>2.99</td><td /><td>3.23</td><td>0.69</td><td /><td>0.57</td><td>0.93</td><td /><td>1.12</td><td>−0.63</td><td /><td>0.94</td><td>−1.24</td><td /><td>1.94</td></tr><tr><td> Income 101–130% FPL</td><td>1.19</td><td /><td>3.8</td><td>0.79</td><td /><td>0.67</td><td>0.68</td><td /><td>1.31</td><td>1.13</td><td /><td>1.1</td><td>−2.89</td><td /><td>2.28</td></tr><tr><td>Highest level of parental education</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Less than high school</td><td>−6.05</td><td /><td>3.45</td><td>−1</td><td /><td>0.6</td><td>1.14</td><td /><td>1.19</td><td>0.5</td><td /><td>0.99</td><td>−3.36</td><td /><td>2.05</td></tr><tr><td> High school or GED</td><td>−3.76</td><td /><td>2.77</td><td>−0.7</td><td /><td>0.48</td><td>1.26</td><td /><td>0.97</td><td>−0.21</td><td /><td>0.78</td><td>−1.07</td><td /><td>1.63</td></tr><tr><td> Associate's degree or some college</td><td>0.04</td><td /><td>2.89</td><td>−0.49</td><td /><td>0.5</td><td>0.47</td><td /><td>0.99</td><td>0.86</td><td /><td>0.81</td><td>−0.19</td><td /><td>1.71</td></tr><tr><td>Employed or enrolled in education/job training</td><td>6.02</td><td>**</td><td>2.04</td><td>0.5</td><td /><td>0.36</td><td>−0.27</td><td /><td>0.69</td><td>−0.13</td><td /><td>0.57</td><td>2.23</td><td /><td>1.2</td></tr><tr><td>Single-parent family</td><td>6.65</td><td>***</td><td>1.91</td><td>0.09</td><td /><td>0.34</td><td>−0.77</td><td /><td>0.65</td><td>0.33</td><td /><td>0.55</td><td>−0.04</td><td /><td>1.15</td></tr><tr><td>CLASS dimension average</td><td>−1.83</td><td /><td>1.55</td><td>−0.3</td><td /><td>0.31</td><td>0.12</td><td /><td>0.59</td><td>−0.99</td><td /><td>0.58</td><td>−0.84</td><td /><td>1.09</td></tr><tr><td>Random effects</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td> Between-classroom variance (SD)</td><td>0</td><td /><td /><td>0.61</td><td /><td /><td>1.12</td><td /><td /><td>1.65</td><td /><td /><td>2.49</td><td /><td /></tr><tr><td> Residual variance (SD)</td><td>14.39</td><td /><td /><td>2.5</td><td /><td /><td>4.86</td><td /><td /><td>3.91</td><td /><td /><td>8.27</td><td /><td /></tr><tr><td>Sample size</td><td>320</td><td /><td /><td>325</td><td /><td /><td>325</td><td /><td /><td>325</td><td /><td /><td>316</td><td /><td /></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note:</emph> Reference groups for characteristics with multiple subgroups are as follows: for race/ethnicity, Non-Hispanic White students; for delegate, students from Monmouth/Middlesex, NJ; for Head Start eligibility, family income >130% of the FPL; for highest level of educational attainment, Bachelor's degree or higher. ***<emph>p</emph> <.001, **<emph>p</emph> <.01, *<emph>p</emph> <.05.</p> <p>There was also evidence that girls actually made smaller language gains than boys during the academic year (girls = 70.39; boys = 76.28; γ = −5.89, SE = 1.66, <emph>p</emph> <.001; Std. association =.21 or about 2.3 months of learning). Follow-up analyses revealed that girls (Mean = 64.27; SD = 28.00) did outscore boys (Mean = 59.61; SD = 26.99) in the winter of 2022, so they perhaps had less room to grow through the spring. Students in the other race/multiracial group made significantly smaller gains in print knowledge than Non-Hispanic White students (Other/Multiracial = 12.08; Non-Hispanic White = 15.67; γ = −3.59, SE = 1.47, <emph>p</emph> <.05; Std. association =.33 or about 3.5 months of learning). There were no differential gains for any of the other subgroups of interest; a review of the covariates (see bottom panel of Table 3), however, revealed that children with single parents made larger gains in language skills (single parent family = 76.66; multi parent family = 70.01; γ = 6.65, SE = 1.91, <emph>p</emph> <.001; Std. association =.24 or about 2.5 months of learning) between winter and spring as did children whose parents were employed or had enrolled recently in education or job training (parent employed/in edu. = 76.35; not employed/in edu. = 70.33; γ = 6.02, SE = 2.04, <emph>p</emph> <.01; Std. association =.22 or about 2.3 months of learning).</p> <hd id="AN0183685179-31">Robustness Checks</hd> <p>Despite the descriptive nature of the study, the team did conduct a few robustness checks to examine the sensitivity of the results. Specifically, the team queried the robustness of model results after excluding students who failed their preLAS assessment. After the removal of these 65 students, model results largely persisted with a few exceptions. After students who failed the preLAS were removed, DLL students gained faster in executive functioning than monolingual students (γ = 3.09, SE = 1.41, <emph>p</emph> <.05) and the faster gains made by students enrolled in Acelero in 2020 in language were no longer significant.</p> <p>As a second robustness check (Appendix E), we added the flag for preLAS failure to our models. Again, results were consistent between our main model and the model with preLAS flag. Students who failed the preLAS made significantly smaller gains on all assessments than passing students. Additionally, DLL students made significantly faster gains on executive functioning than monolingual students (γ = 3.49, SE = 1.50, <emph>p</emph> <.05) after controlling for preLAS failure.</p> <hd id="AN0183685179-32">Discussion</hd> <p>Findings provide some of the first evidence on Head Start children's learning in the wake of the pandemic. There are a number of bright spots and reasons to be optimistic; children were learning rapidly in all domains and performing and gaining in line (or faster) with similar pre-pandemic Acelero Head Start children in language, literacy, and executive functioning (Barnett & Jung, [<reflink idref="bib7" id="ref96">7</reflink>]). These findings are encouraging because they suggest that younger children may have been less negatively affected by the challenges of the pandemic – and ECE closures (Weiland et al., [<reflink idref="bib75" id="ref97">75</reflink>]) – than school-aged children. Although it is impossible to do a direct comparison of how developmental stage or period of schooling affected academic and cognitive outcomes, findings from this work indicate that young children are generally making progress aligned with what would have been expected in the absence of the pandemic and that overall scores in language, literacy, and executive functioning have not been significantly negatively affected by the pandemic.</p> <p>Findings stand somewhat in contrast to other recent research – primarily done in labs and outside of ECE and Head Start centers – showing significant negative effects of the pandemic on children's development, but primarily in the behavioral and social-emotional domains (e.g., Benton et al., [<reflink idref="bib10" id="ref98">10</reflink>]; Hanno et al., [<reflink idref="bib34" id="ref99">34</reflink>]). However, the period and setting of the current study – 2 years after the start of the pandemic in Head Start centers that were able to provide regular, in-person instruction – help to contextualize the current results. Ford et al. ([<reflink idref="bib26" id="ref100">26</reflink>]) conducted a descriptive study at the start of the pandemic and found that virtual ECE instruction appeared to be particularly challenging; teachers reported that children and families participated at low levels and struggled to engage children in instruction. There were particular barriers to virtual instruction for families with low-incomes who struggled to access technology. Kim et al. ([<reflink idref="bib43" id="ref101">43</reflink>]) conducted a study of ECE in the post-pandemic period in California and found similar trends but also reported that <emph>Head Start programs</emph> in particular were better able to support staff well-being during the pandemic and experienced fewer challenges than family childcare homes or non-Head Start centers serving families with lower-incomes. Hanno et al. ([<reflink idref="bib35" id="ref102">35</reflink>]) similarly found that Head Start teachers in Massachusetts reported having greater access to mental health supports and experiencing less financial instability than those in other types of ECE programs. It is very possible that because of consistent federal funding available, Head Start programs like those run by Acelero were better able to support students' development once children were able to return to in-person instruction. Assessment data on young children collected before the pandemic have also found that at-home learning environments play a significant role in the development of children's language skills (McCormick et al., [<reflink idref="bib54" id="ref103">54</reflink>]; Son & Morrison, [<reflink idref="bib73" id="ref104">73</reflink>]). Even more, school-based programs do not typically appear to have as large effects on cognitive processes like executive functioning than more standard academic skills, like foundational literacy skills (Moffett et al., [<reflink idref="bib59" id="ref105">59</reflink>]), suggesting that findings from this paper collected post-pandemic mirror what likely would have happened to children in the absence of center-based instruction pre-pandemic as well. Even so, further research replicating these findings and making more direct comparisons to children's gains in other types of ECE programs (e.g., public schools, family childcare centers) is clearly needed.</p> <p>In addition, despite the trend that we observed in language, literacy, and executive functioning, there are other areas for further work; similar to data (Chapman, [<reflink idref="bib17" id="ref106">17</reflink>]) and simulations from school-aged children (Santibanez & Guarino, [<reflink idref="bib69" id="ref107">69</reflink>]), overall scores were lower and growth slower in math than pre-pandemic levels (Barnett & Jung, [<reflink idref="bib7" id="ref108">7</reflink>]; Kuhfeld et al., [<reflink idref="bib44" id="ref109">44</reflink>]). Importantly, however, growth in math in this sample was slightly faster than data collected during the 2021–2022 head Start year with a similar population of children attending Acelero programs in-person (Groom-Thomas et al., [<reflink idref="bib30" id="ref110">30</reflink>]). This comparison suggests that, with more exposure to in-person instruction over time, overall math skills may be improving post-pandemic. The difference in trends across domains may reflect the fact that parents of young children, regardless of race/ethnicity or family income, generally report spending less time supporting math skills than language/literacy skills at home (McCormick et al., [<reflink idref="bib54" id="ref111">54</reflink>]). For example, Berkowitz et al. ([<reflink idref="bib11" id="ref112">11</reflink>]) have reported that parents of all socioeconomic levels are consistently less likely to participate in math and complex problem-solving activities with children than they are to read to their them. As such, during periods where young children were spending almost all of their time at home with their family, they likely did continue to receive exposure to reading, print knowledge, and letters/letter sounds, supporting foundation language and literacy skills even in the absence of formal ECE. In contrast, they likely experienced less exposure to activities to support math instruction, like counting, adding blocks, exploring shapes, and completing puzzles (McCormick et al., [<reflink idref="bib54" id="ref113">54</reflink>]) although more research about the home learning environment during the pandemic is needed. Taken together, the results suggest that further investigation into how teachers are working to support math – a domain that tends to be more influenced by in-school, direct instruction – may be important for continuing to strengthen Head Start programming during the pandemic recovery.</p> <p>There are also inklings from this study that the children perhaps most negatively affected by the pandemic are bouncing back. For example, most studies find that children who attend PreK for two consecutive years – at ages 3 and 4 – make bigger learning gains in their first year and then slow down as 4-year-olds (Ansari et al., [<reflink idref="bib5" id="ref114">5</reflink>]). In the current study, however, the team found that 4-year-olds made faster progress than their younger peers in language, phonological awareness, and math skills during the Head Start year. Gains were substantial, ranging from about 1.1–3.9 months of learning depending on domain. This finding could reflect these children catching up to where they should be after experiencing significant disruptions to center-based PreK during the first one and a half years of the pandemic. Indeed, a large-scale meta-analysis of international studies (Betthäuser et al., [<reflink idref="bib12" id="ref115">12</reflink>]) examining learning loss in school-aged samples recently concluded that on average, students lost about a third of a year of learning due to the pandemic. Findings from our work, though with younger children, suggest that larger gains among four-year-olds may be in line with what is needed to catch up to typical pre-pandemic norms before kindergarten. It is also possible that these children − 43.2% of whom did not attend Acelero as 3-year-olds – could simply be making the gains they missed out on if they did not get a chance to attend formal early care and education as a 3-year-old during the 2020–2021 academic year. Either way, the pattern is suggestive of resilience for these children, an important finding given the disproportionately negative toll that the pandemic had on families experiencing poverty.</p> <p>Similarly, we saw resilience for other groups of interest, including boys gaining faster in language skills after having scored significantly lower than girls on a language assessment about halfway through the year. This finding stands in contrast to other work findings that girls were actually more negatively affected by the pandemic than boys (Hamilton & Gross, [<reflink idref="bib32" id="ref116">32</reflink>]). However, that work focused mostly on social-emotional well-being and mental health. In contrast, research examining academic skills has consistently found that girls tend to outperform boys on assessments of language in early and middle childhood (Justice et al., [<reflink idref="bib41" id="ref117">41</reflink>]; McCormick & O'Connor, [<reflink idref="bib53" id="ref118">53</reflink>]). Boys may have fallen even further behind their peers in the absence of formal center-based care and Head Start programming and this paper shows them developing those skills more rapidly once they are able to return to largely in-person instruction. Relatedly, there was a similar differential pattern for children who lived in single-parent households. They exhibited faster growth in language skills than their peers perhaps because they had more room to growth (Hamre & Pianta, [<reflink idref="bib33" id="ref119">33</reflink>]) and were able to gain more from the consistent exposure to in-person language and literacy instruction. Importantly, this finding stands in contrast from the data collected in 2020–2021 showing that children from single parent households actually grew more slowly in academic skills than their peers from two-parent families. Taken together, the findings suggest an upward trajectory in the academic and cognitive skills of young children in Head Start, with even greater resilience observed among children who would have been most negatively affected by the crisis.</p> <hd id="AN0183685179-33">Limitations</hd> <p>Although this study makes a number of contributions to the literature, there are some key limitations that are important to note. First, this study is descriptive, and it is impossible to draw any causal conclusions from the findings. Second, the team collected the data in the winter and spring of the Head Start year, and it is unclear how much children gained prior to February when the bulk of the assessments were conducted. Work that is currently underway in the same Acelero programs aims to collect information on a much larger sample of children and follow them from the start of the year (August/September) with further data collected in winter and spring. This new research will allow the team to understand the broader pattern of development and to identify the key periods when children are making critical gains in these different learning domains. Third, although it can be informative to compare the 2021–2022 sample of Acelero Head Start students to earlier samples, it is critical to acknowledge that these are very much different groups of children. Contextual and demographic differences across time could be influencing comparisons. Although our approach is the best one available to us given limitations in the data on young children, comparisons should be interpreted with caution. Relatedly, this study focuses on children enrolled in Head Start and thus findings may not be generalizable to children in other types of pre-K programs or who were not enrolled in pre-K. Fourth, there was some differential attrition in the assessment sample from winter to spring and the analytic sample has a smaller proportion of Black students than we would have expected given the winter sample. Finally, this study only focuses on academic and cognitive skills; the team wanted to limit teacher burden and decided not to collect information on children's social-emotional skills, the area where other studies have identified negative effects of the pandemic on young children's development (Egan et al., [<reflink idref="bib25" id="ref120">25</reflink>]). Future work should consider a broader set of outcomes that are critical to children's health and well-being.</p> <hd id="AN0183685179-34">Implications and Directions for Future Research</hd> <p>Despite these limitations, the data provides some hope that young children from marginalized groups enrolled in high-quality early care and education are exhibiting resilience in the wake of the pandemic. Findings highlight three areas for Head Start and other ECE programs to consider as they continue to recover from the pandemic. First, it is critical to continue investing in systematic data collection on young children's skills prior to elementary school in order to compare within and across years and build stronger early care and education systems. Acelero programs are only able to continue tracking students' development and learning how best to consider activities to strengthen programs because of their continued investment in data collection across centers and across time. Second, young children enrolled in formal ECE programs like Head Start may be experiencing more resilience in their language and literacy development than in the development of math skills. Continuing to strengthen math in ECE settings will be important to the pandemic recovery. Programs may consider investments in early math curricula, which have recently been shown in experimental work to promote better learning outcomes through third grade in a sample of children from families with low-incomes (Mattera et al., [<reflink idref="bib52" id="ref121">52</reflink>]). Third, young children are by and large continuing to grow and develop and those potentially most affected by the unique circumstances of the pandemic have demonstrated that they are catching up to what would have been expected in the absence of the crisis. It may be important for ECE programs to recognize this and avoid exposing children to basic and remedial instruction and instead continue to support their development of a diverse and robust set of foundational and higher-order skills (McCormick et al., [<reflink idref="bib55" id="ref122">55</reflink>]).</p> <hd id="AN0183685179-35">Disclosure Statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <hd id="AN0183685179-36">Appendix A. Characteristics of Analytic Sample Compared to all Acelero Students and Initial S...</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Characteristic (%)</td><td>All students</td><td>Initial sample</td><td>Analytic sample</td><td>Attrited sample</td><td>Sig. Diff</td></tr></thead><tbody><tr><td>Race/Ethnicity</td></tr><tr><td> Hispanic</td><td>44.06</td><td>50.11</td><td>52.48</td><td>43.85</td></tr><tr><td> non-Hispanic White</td><td>6.54</td><td>9.51</td><td>10.50</td><td>6.92</td></tr><tr><td> non-Hispanic Black</td><td>41.71</td><td>34.67</td><td>31.20</td><td>43.85</td><td>*</td></tr><tr><td> non-Hispanic Multiracial/Other</td><td>7.69</td><td>5.71</td><td>5.83</td><td>5.38</td></tr><tr><td>Female</td><td>49.88</td><td>49.05</td><td>46.65</td><td>55.38</td></tr><tr><td>Age at district Kindergarten cutoff</td></tr><tr><td> Two</td><td>14.71</td><td>5.92</td><td>5.54</td><td>6.92</td></tr><tr><td> Three</td><td>46.02</td><td>56.87</td><td>55.98</td><td>59.23</td></tr><tr><td> Four</td><td>39.27</td><td>37.21</td><td>38.48</td><td>33.85</td></tr><tr><td>Dual language learner</td><td>32.32</td><td>41.23</td><td>43.44</td><td>35.38</td></tr><tr><td>Enrollment year</td></tr><tr><td> Enrolled PY 2020 and 2021</td><td>20.47</td><td>28.33</td><td>29.74</td><td>24.62</td></tr><tr><td> Enrolled only PY 2021</td><td>79.53</td><td>71.67</td><td>70.26</td><td>75.38</td></tr><tr><td>Family Head Start eligibility</td></tr><tr><td> Foster care</td><td>2.04</td><td>2.11</td><td>2.04</td><td>2.31</td></tr><tr><td> Homeless</td><td>9.15</td><td>6.34</td><td>6.41</td><td>6.15</td></tr><tr><td> Receives public assistance</td><td>13.92</td><td>7.19</td><td>5.83</td><td>10.77</td></tr><tr><td> Income ≤100% FPL</td><td>55.99</td><td>59.62</td><td>63.56</td><td>49.23</td><td>**</td></tr><tr><td> Income 101%−130% FPL</td><td>10.59</td><td>14.80</td><td>13.99</td><td>16.92</td></tr><tr><td> Income >130% FPL</td><td>8.29</td><td>9.73</td><td>7.87</td><td>14.62</td></tr><tr><td>Family highest educational attainment</td></tr><tr><td> Less than high school degree</td><td>11.91</td><td>14.16</td><td>13.70</td><td>15.38</td></tr><tr><td> High school diploma or GED</td><td>48.35</td><td>46.93</td><td>45.48</td><td>50.77</td></tr><tr><td> Associate's degree or some college</td><td>29.59</td><td>29.39</td><td>29.74</td><td>28.46</td></tr><tr><td> Bachelor's degree or more</td><td>10.16</td><td>9.51</td><td>11.08</td><td>5.38</td><td>*</td></tr><tr><td>Parent/guardian employed or enrolled in education/job training</td><td>69.97</td><td>71.88</td><td>72.01</td><td>71.54</td></tr><tr><td>Single parent household</td><td>68.04</td><td>57.51</td><td>55.39</td><td>63.08</td></tr><tr><td>Delegate</td></tr><tr><td> Clark County, NV</td><td>38.40</td><td>22.83</td><td>19.83</td><td>31.01</td><td>*</td></tr><tr><td> Camden/Philadelphia, NJ</td><td>26.23</td><td>21.99</td><td>16.33</td><td>37.21</td><td>***</td></tr><tr><td> Monmouth/Middlesex County, NJ</td><td>20.80</td><td>34.04</td><td>40.23</td><td>17.05</td><td>***</td></tr><tr><td> Wisconsin</td><td>14.57</td><td>21.14</td><td>23.62</td><td>14.73</td><td>*</td></tr><tr><td>Sample size</td><td>4219</td><td>473</td><td>343</td><td>130</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 Source: Acelero demographic intake data PY 2021–2022</item> <item>5 <emph>Note:</emph> All students represent all students enrolled in the Acelero system throughout the 2021–2022 academic year. The initial sample is made up of students assessed in the winter and the analytic sample only includes students with at least one valid assessment in both the winter and spring. ***<emph>p</emph> <.001, **<emph>p</emph> <.01, *<emph>p</emph> <.05</item> </ulist> <hd id="AN0183685179-37">Appendix B. Associations between Age, Race/Ethnicity, Dll Status, and Sex and Gains in Langua...</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Language</td><td>Math</td><td>Print Knowledge</td><td>Phonological Awareness</td><td>Executive Functioning (std.)</td></tr><tr><td>Predictor</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td></tr></thead><tbody><tr><td>Intercept</td><td>63.42</td><td>***</td><td>9.58</td><td>10.08</td><td>***</td><td>1.72</td><td>14.50</td><td>***</td><td>3.45</td><td>17.46</td><td>***</td><td>3.03</td><td>98.88</td><td>***</td><td>5.37</td></tr><tr><td>Winter assessment score</td><td>0.75</td><td>***</td><td>0.04</td><td>0.75</td><td>***</td><td>0.04</td><td>0.90</td><td>***</td><td>0.03</td><td>0.50</td><td>***</td><td>0.05</td><td>0.31</td><td>***</td><td>0.06</td></tr><tr><td>Days between assessments</td><td>0.23</td><td>*</td><td>0.09</td><td>0.00</td><td>0.02</td><td>0.00</td><td>0.03</td><td>0.00</td><td>0.03</td><td>0.04</td><td>0.05</td></tr><tr><td>Age 4 eligibility</td><td>4.90</td><td>*</td><td>2.40</td><td>0.88</td><td>*</td><td>0.41</td><td>0.45</td><td>0.82</td><td>2.06</td><td>**</td><td>0.66</td><td>0.41</td><td>1.16</td></tr><tr><td>Race/ethnicity</td></tr><tr><td> Non-Hispanic Black</td><td>4.10</td><td>3.25</td><td>−0.43</td><td>0.57</td><td>−0.66</td><td>1.12</td><td>−0.58</td><td>0.93</td><td>−1.66</td><td>1.69</td></tr><tr><td> Hispanic</td><td>1.89</td><td>3.35</td><td>−1.10</td><td>0.59</td><td>−1.23</td><td>1.16</td><td>−1.26</td><td>0.95</td><td>−1.28</td><td>1.74</td></tr><tr><td> Other race/Multiracial</td><td>2.58</td><td>4.58</td><td>−0.98</td><td>0.81</td><td>−3.38</td><td>*</td><td>1.58</td><td>−0.68</td><td>1.29</td><td>−1.26</td><td>2.35</td></tr><tr><td>DLL</td><td>4.52</td><td>2.68</td><td>0.54</td><td>0.47</td><td>0.71</td><td>0.91</td><td>0.44</td><td>0.75</td><td>3.09</td><td>*</td><td>1.41</td></tr><tr><td>Female</td><td>−6.61</td><td>***</td><td>1.89</td><td>−0.49</td><td>0.33</td><td>1.09</td><td>0.64</td><td>0.28</td><td>0.52</td><td>0.87</td><td>0.96</td></tr><tr><td>Total days present</td><td>−0.03</td><td>0.04</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.00</td><td>0.01</td><td>0.02</td><td>0.02</td></tr><tr><td>Acelero enrollment in 2020</td><td>4.53</td><td>2.42</td><td>0.56</td><td>0.43</td><td>−0.45</td><td>0.85</td><td>−0.06</td><td>0.70</td><td>−1.07</td><td>1.30</td></tr><tr><td>Delegate</td></tr><tr><td> Clark County</td><td>3.95</td><td>3.01</td><td>−0.04</td><td>0.54</td><td>−1.47</td><td>1.08</td><td>0.58</td><td>0.94</td><td>5.64</td><td>**</td><td>1.70</td></tr><tr><td> Camden/Philadelphia</td><td>4.32</td><td>3.02</td><td>−0.74</td><td>0.54</td><td>−1.10</td><td>1.09</td><td>−1.51</td><td>0.95</td><td>1.93</td><td>1.72</td></tr><tr><td> Wisconsin</td><td>−2.09</td><td>3.05</td><td>−0.65</td><td>0.55</td><td>−2.04</td><td>1.10</td><td>0.50</td><td>0.95</td><td>1.19</td><td>1.71</td></tr><tr><td>Head Start eligibility</td></tr><tr><td> Categorical</td><td>5.12</td><td>4.31</td><td>0.73</td><td>0.77</td><td>0.66</td><td>1.50</td><td>−1.11</td><td>1.23</td><td>0.55</td><td>2.23</td></tr><tr><td> Income ≤100% FPL</td><td>2.02</td><td>3.53</td><td>0.61</td><td>0.63</td><td>0.37</td><td>1.24</td><td>−1.06</td><td>1.02</td><td>−0.55</td><td>1.84</td></tr><tr><td> Income 101-130% FPL</td><td>0.20</td><td>4.19</td><td>0.69</td><td>0.75</td><td>0.53</td><td>1.47</td><td>0.86</td><td>1.21</td><td>−1.34</td><td>2.20</td></tr><tr><td>Highest parental educational attainment</td></tr><tr><td> Less than high school</td><td>−5.43</td><td>4.23</td><td>−1.76</td><td>*</td><td>0.73</td><td>0.48</td><td>1.44</td><td>0.58</td><td>1.18</td><td>−2.09</td><td>2.15</td></tr><tr><td> High school or GED</td><td>−3.91</td><td>3.19</td><td>−0.77</td><td>0.56</td><td>1.29</td><td>1.13</td><td>−0.03</td><td>0.90</td><td>−0.36</td><td>1.66</td></tr><tr><td> Associate's degree or some college</td><td>1.97</td><td>3.33</td><td>−0.45</td><td>0.58</td><td>0.41</td><td>1.16</td><td>0.76</td><td>0.93</td><td>1.21</td><td>1.74</td></tr><tr><td>Employed or enrolled in education/job training</td><td>5.87</td><td>**</td><td>2.20</td><td>0.73</td><td>0.39</td><td>−0.25</td><td>0.75</td><td>0.00</td><td>0.61</td><td>2.52</td><td>*</td><td>1.12</td></tr><tr><td>Single-parent family</td><td>7.43</td><td>***</td><td>2.16</td><td>0.24</td><td>0.38</td><td>−0.81</td><td>0.74</td><td>0.36</td><td>0.61</td><td>−0.14</td><td>1.13</td></tr><tr><td>CLASS dimension average</td><td>−1.58</td><td>1.69</td><td>−0.34</td><td>0.31</td><td>0.10</td><td>0.62</td><td>−1.08</td><td>0.55</td><td>−0.88</td><td>0.99</td></tr><tr><td>Random effects</td></tr><tr><td> Between-classroom variance (SD)</td><td>0.00</td><td>0.34</td><td>0.96</td><td>1.18</td><td>1.96</td></tr><tr><td> Residual variance (SD)</td><td>14.60</td><td>2.54</td><td>4.92</td><td>3.96</td><td>7.18</td></tr><tr><td>Sample size</td><td>261</td><td>261</td><td>261</td><td>261</td><td>253</td></tr></tbody></table> </ephtml> </p> <p>6 <emph>Note:</emph> Model contains the same covariates as our original model but removes students who have failed their English preLAS assessment. Reference groups for characteristics with multiple subgroups are as follows: for race/ethnicity, Non-Hispanic White students; for delegate, students from Monmouth/Middlesex, NJ; for Head Start eligibility, family income > 130% of the FPL; for highest level of educational attainment, Bachelor's degree or higher. ***<emph>p</emph> <.001, **<emph>p</emph> <.01, *<emph>p</emph> <.05.</p> <hd id="AN0183685179-38">Appendix C. Demographic Characteristics of Analytic and Comparison Samples</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Sample</td></tr><tr><td>Characteristic (%)</td><td>Analytic Sample, 2021–2022</td><td>Barnett & Jung, 2011–2012</td><td>Aikens et al, 2014–2015</td><td>Groom-Thomas et al, 2020–2021</td></tr></thead><tbody><tr><td>4-year-olds</td><td>38.48</td><td>55.5</td><td>56.2</td><td>69</td></tr><tr><td>Participation Years</td></tr><tr><td> First</td><td>70.26</td><td>45.9</td><td>64.9</td><td>33</td></tr><tr><td> Second or more</td><td>29.74</td><td>54.2</td><td>35.1</td><td>67</td></tr><tr><td>Head Start Eligibility</td></tr><tr><td> Under FPL</td><td>63.56</td><td>70.8</td><td>67.3</td></tr><tr><td> 101–30% FPL</td><td>13.99</td><td>3.8</td><td>13.1</td></tr><tr><td> Over 130% FPL</td><td>7.87</td><td>3.8</td><td>19.5</td></tr><tr><td> Foster child</td><td>2.04</td><td>1.2</td><td>2</td></tr><tr><td> Homeless</td><td>6.41</td><td>6.7</td><td>11</td></tr><tr><td> Public assistance</td><td>5.83</td><td>13.7</td></tr><tr><td>Race/ethnicity</td></tr><tr><td> Hispanic</td><td>52.48</td><td>54.2</td><td>41.6</td><td>47</td></tr><tr><td> Non-Hispanic White</td><td>10.50</td><td>27.2</td><td>9</td></tr><tr><td> Non-Hispanic Black</td><td>31.20</td><td>33.8</td><td>22.6</td><td>38</td></tr><tr><td> Multiracial/other</td><td>5.83</td><td>8.6</td><td>7</td></tr><tr><td>Female</td><td>46.65</td><td>49.6</td></tr><tr><td>DLL/Primary home language other than English</td><td>43.44</td><td>44.3</td><td>24.9</td><td>32</td></tr><tr><td>Sample size</td><td>343</td><td>1718</td><td>1921</td><td>196</td></tr></tbody></table> </ephtml> </p> <p>7 <emph>Note:</emph> Sample percentages are given at two decimal places where possible and are otherwise provided with the most detail given in source.</p> <hd id="AN0183685179-39">Appendix D. Gains in Academic and Cognitive Skills Across Demographic Groups (Standardized Sc...</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Language</td><td>Math</td><td>Print knowledge</td><td>Phonological awareness</td><td>Executive functioning</td></tr><tr><td>Predictor</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td></tr></thead><tbody><tr><td>Intercept</td><td>87.80</td><td>***</td><td>5.29</td><td>89.98</td><td>***</td><td>7.93</td><td>94.31</td><td>***</td><td>5.23</td><td>102.21</td><td>***</td><td>8.40</td><td>100.04</td><td>***</td><td>5.89</td></tr><tr><td>Winter assessment score</td><td>0.69</td><td>***</td><td>0.04</td><td>0.69</td><td>***</td><td>0.04</td><td>0.83</td><td>***</td><td>0.03</td><td>0.48</td><td>***</td><td>0.05</td><td>0.29</td><td>***</td><td>0.06</td></tr><tr><td>Days between assessments</td><td>0.05</td><td>0.05</td><td>−0.04</td><td>0.07</td><td>0.00</td><td>0.05</td><td>−0.01</td><td>0.07</td><td>0.03</td><td>0.05</td></tr><tr><td>Age 4 eligibility</td><td>0.06</td><td>1.18</td><td>1.44</td><td>1.66</td><td>0.81</td><td>1.08</td><td>1.59</td><td>1.60</td><td>1.39</td><td>1.20</td></tr><tr><td>Race/ethnicity</td></tr><tr><td> Non-Hispanic Black</td><td>1.72</td><td>1.85</td><td>−2.01</td><td>2.62</td><td>−0.03</td><td>1.70</td><td>−0.48</td><td>2.50</td><td>−2.80</td><td>1.85</td></tr><tr><td> Hispanic</td><td>0.23</td><td>1.86</td><td>−4.71</td><td>2.62</td><td>−1.31</td><td>1.71</td><td>−3.40</td><td>2.48</td><td>−2.41</td><td>1.85</td></tr><tr><td> Other race/Multiracial</td><td>1.44</td><td>2.63</td><td>−5.15</td><td>3.65</td><td>−5.15</td><td>*</td><td>2.36</td><td>−1.79</td><td>3.42</td><td>−2.02</td><td>2.55</td></tr><tr><td>DLL</td><td>1.23</td><td>1.44</td><td>−0.29</td><td>1.94</td><td>0.03</td><td>1.26</td><td>−2.67</td><td>1.83</td><td>0.78</td><td>1.38</td></tr><tr><td>Female</td><td>−3.41</td><td>***</td><td>1.01</td><td>−0.11</td><td>1.39</td><td>1.18</td><td>0.91</td><td>1.34</td><td>1.31</td><td>1.88</td><td>0.99</td></tr><tr><td>Total days present</td><td>−0.03</td><td>0.02</td><td>−0.01</td><td>0.03</td><td>0.00</td><td>0.02</td><td>0.02</td><td>0.03</td><td>0.01</td><td>0.02</td></tr><tr><td>Acelero enrollment in 2020</td><td>3.17</td><td>*</td><td>1.34</td><td>1.67</td><td>1.91</td><td>−0.85</td><td>1.25</td><td>1.00</td><td>1.83</td><td>0.02</td><td>1.37</td></tr><tr><td>Delegate</td></tr><tr><td> Clark County</td><td>2.12</td><td>1.62</td><td>−0.55</td><td>2.44</td><td>−1.11</td><td>1.60</td><td>2.82</td><td>2.61</td><td>5.02</td><td>**</td><td>1.81</td></tr><tr><td> Camden/Philadelphia</td><td>1.91</td><td>1.63</td><td>−4.36</td><td>2.48</td><td>−0.15</td><td>1.63</td><td>−3.57</td><td>2.65</td><td>2.53</td><td>1.87</td></tr><tr><td> Wisconsin</td><td>−2.65</td><td>1.68</td><td>−3.11</td><td>2.54</td><td>−1.25</td><td>1.66</td><td>1.22</td><td>2.66</td><td>1.39</td><td>1.87</td></tr><tr><td>Head Start eligibility</td></tr><tr><td> Categorical</td><td>3.69</td><td>2.42</td><td>5.56</td><td>3.39</td><td>2.61</td><td>2.20</td><td>−1.58</td><td>3.20</td><td>−0.10</td><td>2.37</td></tr><tr><td> Income ≤100% FPL</td><td>2.09</td><td>1.96</td><td>4.34</td><td>2.75</td><td>2.47</td><td>1.79</td><td>−1.07</td><td>2.63</td><td>−1.24</td><td>1.94</td></tr><tr><td> Income 101–130% FPL</td><td>1.82</td><td>2.31</td><td>4.64</td><td>3.23</td><td>2.53</td><td>2.11</td><td>3.73</td><td>3.08</td><td>−2.89</td><td>2.28</td></tr><tr><td>Highest level of parental education</td></tr><tr><td> Less than high school</td><td>−3.85</td><td>2.09</td><td>−3.14</td><td>2.88</td><td>1.10</td><td>1.90</td><td>1.20</td><td>2.77</td><td>−3.36</td><td>2.05</td></tr><tr><td> High school or GED</td><td>−2.11</td><td>1.67</td><td>−1.22</td><td>2.30</td><td>1.72</td><td>1.55</td><td>−0.18</td><td>2.19</td><td>−1.07</td><td>1.63</td></tr><tr><td> Associate's degree or some college</td><td>0.86</td><td>1.75</td><td>0.10</td><td>2.42</td><td>0.81</td><td>1.59</td><td>3.15</td><td>2.27</td><td>−0.19</td><td>1.71</td></tr><tr><td>Employed or enrolled in education/job training</td><td>2.62</td><td>*</td><td>1.24</td><td>2.82</td><td>1.72</td><td>−0.81</td><td>1.11</td><td>−0.89</td><td>1.60</td><td>2.23</td><td>1.20</td></tr><tr><td>Single-parent family</td><td>4.42</td><td>***</td><td>1.16</td><td>0.47</td><td>1.61</td><td>−1.02</td><td>1.05</td><td>0.92</td><td>1.53</td><td>−0.04</td><td>1.15</td></tr><tr><td>CLASS dimension average</td><td>−1.19</td><td>0.94</td><td>−1.24</td><td>1.45</td><td>−0.16</td><td>0.96</td><td>−3.03</td><td>1.56</td><td>−0.84</td><td>1.09</td></tr><tr><td>Random effects</td></tr><tr><td> Between-classroom variance (SD)</td><td>0.00</td><td>2.72</td><td>1.89</td><td>4.17</td><td>2.49</td></tr><tr><td> Residual variance (SD)</td><td>8.74</td><td>12.01</td><td>7.80</td><td>11.06</td><td>8.27</td></tr><tr><td>Sample size</td><td>320</td><td>325</td><td>325</td><td>325</td><td>316</td></tr></tbody></table> </ephtml> </p> <p>8 <emph>Note:</emph> Reference groups for characteristics with multiple subgroups are as follows: for race/ethnicity, Non-Hispanic White students; for delegate, students from Monmouth/Middlesex, NJ; for Head Start eligibility, family income > 130% of the FPL; for highest level of educational attainment, Bachelor's degree or higher. ***<emph>p</emph> <.001, **<emph>p</emph> <.01, *<emph>p</emph> <.05.</p> <hd id="AN0183685179-40">Appendix E. Associations between Age, Race/Ethnicity, DLL Status, and Sex and Gains in Langua...</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Language</td><td>Math</td><td>Print Knowledge</td><td>Phonological Awareness</td><td>Executive Functioning (std.)</td></tr><tr><td>Predictor</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td><td>γ</td><td>SE</td></tr></thead><tbody><tr><td>Intercept</td><td>65.75</td><td>***</td><td>8.62</td><td>9.85</td><td>***</td><td>1.64</td><td>13.37</td><td>***</td><td>3.18</td><td>16.61</td><td>***</td><td>2.89</td><td>101.52</td><td>***</td><td>5.61</td></tr><tr><td>Winter assessment score</td><td>0.75</td><td>***</td><td>0.04</td><td>0.73</td><td>***</td><td>0.04</td><td>0.90</td><td>***</td><td>0.03</td><td>0.48</td><td>***</td><td>0.04</td><td>0.22</td><td>***</td><td>0.06</td></tr><tr><td>Days between assessments</td><td>0.15</td><td>0.08</td><td>0.00</td><td>0.01</td><td>0.02</td><td>0.03</td><td>0.00</td><td>0.03</td><td>0.01</td><td>0.05</td></tr><tr><td>Failed preLAS</td><td>−6.97</td><td>*</td><td>2.71</td><td>−0.97</td><td>*</td><td>0.48</td><td>−1.91</td><td>*</td><td>0.89</td><td>−3.79</td><td>***</td><td>0.72</td><td>−6.51</td><td>***</td><td>1.57</td></tr><tr><td>Age 4 eligibility</td><td>5.04</td><td>*</td><td>2.16</td><td>1.09</td><td>**</td><td>0.37</td><td>1.03</td><td>0.72</td><td>2.10</td><td>***</td><td>0.58</td><td>0.89</td><td>1.17</td></tr><tr><td>Race/ethnicity</td></tr><tr><td>Non-Hispanic Black</td><td>3.86</td><td>3.01</td><td>−0.51</td><td>0.54</td><td>−0.39</td><td>1.05</td><td>−0.11</td><td>0.86</td><td>−2.87</td><td>1.80</td></tr><tr><td>Hispanic</td><td>2.32</td><td>3.03</td><td>−1.07</td><td>*</td><td>0.54</td><td>−0.86</td><td>1.05</td><td>−0.91</td><td>0.85</td><td>−2.39</td><td>1.81</td></tr><tr><td>Other race/Multiracial</td><td>3.75</td><td>4.29</td><td>−1.22</td><td>0.75</td><td>−3.54</td><td>*</td><td>1.46</td><td>−0.47</td><td>1.17</td><td>−1.97</td><td>2.48</td></tr><tr><td>DLL</td><td>3.67</td><td>2.49</td><td>0.37</td><td>0.44</td><td>0.85</td><td>0.86</td><td>0.43</td><td>0.69</td><td>3.49</td><td>*</td><td>1.50</td></tr><tr><td>Female</td><td>−6.10</td><td>***</td><td>1.65</td><td>−0.23</td><td>0.29</td><td>0.92</td><td>0.57</td><td>0.26</td><td>0.45</td><td>1.51</td><td>0.97</td></tr><tr><td>Total days present</td><td>−0.03</td><td>0.04</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.02</td></tr><tr><td>Acelero enrollment in 2020</td><td>4.55</td><td>*</td><td>2.20</td><td>0.41</td><td>0.40</td><td>−0.86</td><td>0.77</td><td>0.09</td><td>0.63</td><td>−0.82</td><td>1.34</td></tr><tr><td>Delegate</td></tr><tr><td>Clark County</td><td>4.43</td><td>2.65</td><td>0.18</td><td>0.51</td><td>−1.12</td><td>0.97</td><td>0.79</td><td>0.89</td><td>4.67</td><td>**</td><td>1.72</td></tr><tr><td>Camden/Philadelphia</td><td>3.22</td><td>2.68</td><td>−0.99</td><td>0.51</td><td>−0.71</td><td>0.99</td><td>−1.83</td><td>*</td><td>0.91</td><td>2.13</td><td>1.77</td></tr><tr><td>Wisconsin</td><td>−3.08</td><td>2.76</td><td>−0.42</td><td>0.53</td><td>−0.98</td><td>1.01</td><td>0.46</td><td>0.91</td><td>0.74</td><td>1.78</td></tr><tr><td>Head Start eligibility</td></tr><tr><td>Categorical</td><td>5.98</td><td>3.95</td><td>0.98</td><td>0.70</td><td>1.15</td><td>1.36</td><td>−0.50</td><td>1.09</td><td>0.56</td><td>2.31</td></tr><tr><td>Income ≤100% FPL</td><td>3.12</td><td>3.20</td><td>0.73</td><td>0.57</td><td>0.98</td><td>1.11</td><td>−0.53</td><td>0.90</td><td>−0.93</td><td>1.88</td></tr><tr><td>Income 101–130% FPL</td><td>1.21</td><td>3.76</td><td>0.80</td><td>0.67</td><td>0.61</td><td>1.30</td><td>1.08</td><td>1.06</td><td>−2.95</td><td>2.22</td></tr><tr><td>Highest parental educational attainment</td></tr><tr><td>Less than high school</td><td>−6.62</td><td>3.43</td><td>−1.07</td><td>0.60</td><td>1.01</td><td>1.18</td><td>0.25</td><td>0.95</td><td>−3.74</td><td>2.00</td></tr><tr><td>High school or GED</td><td>−4.41</td><td>2.75</td><td>−0.81</td><td>0.48</td><td>1.07</td><td>0.97</td><td>−0.46</td><td>0.75</td><td>−1.53</td><td>1.59</td></tr><tr><td>Associate's degree or some college</td><td>−0.35</td><td>2.86</td><td>−0.57</td><td>0.50</td><td>0.29</td><td>0.99</td><td>0.66</td><td>0.78</td><td>−0.63</td><td>1.67</td></tr><tr><td>Employed or enrolled in education/job training</td><td>6.28</td><td>**</td><td>2.03</td><td>0.54</td><td>0.36</td><td>−0.15</td><td>0.69</td><td>0.10</td><td>0.55</td><td>2.59</td><td>*</td><td>1.18</td></tr><tr><td>Single-parent family</td><td>6.81</td><td>***</td><td>1.89</td><td>0.10</td><td>0.34</td><td>−0.72</td><td>0.65</td><td>0.39</td><td>0.53</td><td>0.24</td><td>1.12</td></tr><tr><td>CLASS dimension average</td><td>−1.99</td><td>1.54</td><td>−0.31</td><td>0.30</td><td>0.06</td><td>0.58</td><td>−1.03</td><td>0.54</td><td>−1.01</td><td>1.03</td></tr><tr><td>Random effects</td></tr><tr><td>Between-classroom variance (SD)</td><td>0.00</td><td>0.55</td><td>1.00</td><td>1.42</td><td>2.12</td></tr><tr><td>Residual variance (SD)</td><td>14.26</td><td>2.49</td><td>4.85</td><td>3.79</td><td>8.11</td></tr><tr><td>Sample size</td><td>320</td><td>325</td><td>325</td><td>325</td><td>316</td></tr></tbody></table> </ephtml> </p> <p>9 <emph>Note</emph>: Reference groups for characteristics with multiple subgroups are as follows: for race/ethnicity, Non-Hispanic White students; for delegate, students from Monmouth/Middlesex, NJ; for Head Start eligibility, family income > 130% of the FPL; for highest level of educational attainment, Bachelor's degree or higher. ***<emph>p</emph> <.001, **<emph>p</emph> <.01, *<emph>p</emph> <.05.</p> <ref id="AN0183685179-41"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref11" type="bt">1</bibl> <bibtext> Resilience in this study is defined as children scoring and growing at similar – or even higher levels and faster rates – than were observed in demographically similar populations of children attending Head Start in the years before the crisis (Weiland & Morris, [77]).</bibtext> </blist> <blist> <bibl id="bib2" idref="ref77" type="bt">2</bibl> <bibtext> Note that because the team was not able to access identifiable data, we were not able to calculate exact age of the child. However, we did have access to age on the day of the assessment in months rounded to the tenths place and used that to calculate standardized scores.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref27" type="bt">3</bibl> <bibtext> Head Start programs are permitted to allocate up to 10% of slots to families that do not technically meet the Head Start eligibility criteria, explaining why a small proportion of students in the current sample have slightly higher family income than would otherwise be expected.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref58" type="bt">4</bibl> <bibtext> We also compared the 2011–2012 sample to updated data from internal reports (not made public) done on the 2014–2015 sample which would be more recent. Findings were consistent across the cohorts. Non-published data is available upon request.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref91" type="bt">5</bibl> <bibtext> While students in the attrited sample were significantly more likely to be non-Hispanic Black, have family incomes above the FPL, and have parents who did not complete college than students in our analytic sample, we did not observe differential gains for Black students, for students with family incomes below the FPL (each relative to their respective reference groups), or for students with a parent with a bachelor's degree or higher compared to those with any other parental educational levels. Given these subgroup results, the impact of differential attrition on observed scores and gains was likely limited.</bibtext> </blist> </ref> <ref id="AN0183685179-42"> <title> References </title> <blist> <bibtext> Adeyemi, T. (2011). The effective use of standard scores for research in educational management. Research Journal of Mathematics and Statistics, 3 (3), 91 – 96.</bibtext> </blist> <blist> <bibtext> Aikens, N., Manley, M., Kopack Klein, A., Malone, L., Knas, E., Tarullo, L., Hartog, J., & Lukashanets, S. (2017). Child and family outcomes during the head start year: Faces 2014-2015 data tables and study design (OPRE Report 2017-100). R. Office of Planning, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.</bibtext> </blist> <blist> <bibtext> Almeida, M., Challa, M., Ribeiro, M., Harrison, A. M., & Castro, M. C. (2022). Editorial perspective: The mental health impact of school closures during the COVID-19 pandemic. Journal of Child Psychology and Psychiatry, 63 (5), 608 – 612. https://doi.org/10.1111/jcpp.13535</bibtext> </blist> <blist> <bibtext> Andrade, C. (2021). Z scores, standard scores, and composite test scores explained. Indian Journal of Psychological Medicine, 43 (6), 555 – 557. https://doi.org/10.1177/02537176211046525</bibtext> </blist> <blist> <bibtext> Ansari, A., Pianta, R. C., Whittaker, J. V., Vitiello, V. E., & Ruzek, E. A. (2019). Starting early: The benefits of attending early childhood education programs at age 3. American Educational Research Journal, 56 (4), 1495 – 1523. https://doi.org/10.3102/0002831218817737</bibtext> </blist> <blist> <bibl id="bib6" idref="ref38" type="bt">6</bibl> <bibtext> Bailey, D., Duncan, G. J., Odgers, C. L., & Yu, W. (2017). Persistence and fadeout in the impacts of child and adolescent interventions. Journal for Research on Educational Effectiveness, 10 (1), 7 – 39. https://doi.org/10.1080/19345747.2016.1232459</bibtext> </blist> <blist> <bibl id="bib7" idref="ref82" type="bt">7</bibl> <bibtext> Barnett, S., & Jung, K. (2013). Acelero learning 2011-12 program evaluation: Summary report.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref52" type="bt">8</bibl> <bibtext> Barrueco, S., Lopez, M., Ong, C., & Lozano, P. (2012). Assessing Spanish-English bilingual preschoolers: A guide to best approaches and measures. Paul H Brookes Publishing.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref79" type="bt">9</bibl> <bibtext> Belsky, J., Vandell, D. L., Burchinal, M., Clarke‐Stewart, K. A., McCartney, K., Owen, M. T., & The NICHD Early Child Care Research Network. (2007). Are there long‐term effects of early child care? Child Development, 78 (2), 681 – 701. https://doi.org/10.1111/j.1467-8624.2007.01021.x</bibtext> </blist> <blist> <bibtext> Benton, T., Njoroge, W. F., & Ng, W. Y. (2022). Sounding the alarm for children's mental health during the COVID-19 pandemic. JAMA Pediatrics, 176 (4), e216295. https://doi.org/10.1001/jamapediatrics.2021.6295</bibtext> </blist> <blist> <bibtext> Berkowitz, T., Schaeffer, M. W., Maloney, E. A., Peterson, L., Gregor, C., Levine, S. C., & Beilock, S. L. (2015). Math at home adds up to achievement in school. Science, 350 (6257), 196 – 198. https://doi.org/10.1126/science.aac7427</bibtext> </blist> <blist> <bibtext> Betthäuser, B. A., Bach-Mortensen, A. M., & Engzell, P. (2023). A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nature Human Behaviour, 7 (3), 375 – 385. https://doi.org/10.1038/s41562-022-01506-4</bibtext> </blist> <blist> <bibtext> Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary–developmental theory of the origins and functions of stress reactivity. Development & Psychopathology, 17 (2), 271 – 301. https://doi.org/10.1017/S0954579405050145</bibtext> </blist> <blist> <bibtext> Burchinal, M., Vernon-Feagans, L., Vitiello, V., Greenberg, M., & Investigators, F. L. P. K. (2014). Thresholds in the association between child care quality and child outcomes in rural preschool children. Early Childhood Research Quarterly, 29 (1), 41 – 51. https://doi.org/10.1016/j.ecresq.2013.09.004</bibtext> </blist> <blist> <bibtext> Carlson, S. M. (2021). Minnesota Executive Function Scale: Technical report. Reflection Sciences, Inc.</bibtext> </blist> <blist> <bibtext> Catani, C., Jacob, N., Schauer, E., Kohila, M., & Neuner, F. (2008). Family violence, war, and natural disasters: A study of the effect of extreme stress on children's mental health in Sri Lanka. BMC Psychiatry, 8, 1 – 10. https://doi.org/10.1186/1471-244X-8-33</bibtext> </blist> <blist> <bibtext> Chapman, B. (2022). Math scores dropped in every state during pandemic, report card shows. The Wall Street Journal. https://<ulink href="http://www.wsj.com/articles/math-scores-dropped-in-every-state-during-pandemic-report-card-shows-11666584062">www.wsj.com/articles/math-scores-dropped-in-every-state-during-pandemic-report-card-shows-11666584062</ulink></bibtext> </blist> <blist> <bibtext> Cicchetti, D., & Curtis, W. J. (2015). The developing brain and neural plasticity: Implications for normality, psychopathology, and resilience. Developmental Psychopathology, 1 – 64. https://doi.org/10.1002/9780470939390.ch1</bibtext> </blist> <blist> <bibtext> Contini, D., Tommaso, M. L. D., Muratori, C., Piazzalunga, D., & Schiavon, L. (2022). Who lost the most? Mathematics achievement during the COVID-19 pandemic. The BE Journal of Economic Analysis & Policy, 22 (2), 399 – 408. https://doi.org/10.1515/bejeap-2021-0447</bibtext> </blist> <blist> <bibtext> Crosnoe, R., Morrison, F., Burchinal, M., Pianta, R., Keating, D., Friedman, S. L., & Clarke-Stewart, K. A. (2010). Instruction, teacher–student relations, and math achievement trajectories in elementary school. Journal of Educational Psychology, 102 (2), 407. https://doi.org/10.1037/a0017762</bibtext> </blist> <blist> <bibtext> De Avila, E. A., & Duncan, S. E. (1998). Pre-las 2000. CTB/McGraw-Hill.</bibtext> </blist> <blist> <bibtext> Diamond, A. (2012). Activities and programs that improve children's executive functions. Current Directions in Psychological Science, 21 (5), 335 – 341. https://doi.org/10.1177/0963721412453722</bibtext> </blist> <blist> <bibtext> Dorn, E., Hancock, B., Sarakatsannis, J., & Viruleg, E. (2020). COVID-19 and student learning in the United States: The hurt could last a lifetime. McKinsey & Company.</bibtext> </blist> <blist> <bibtext> Dunn, D. M. (2018). Peabody picture vocabulary test (5th ed.). NCS Pearson.</bibtext> </blist> <blist> <bibtext> Egan, S. M., Pope, J., Moloney, M., Hoyne, C., & Beatty, C. (2021). Missing early education and care during the pandemic: The socio-emotional impact of the COVID-19 crisis on young children. Early Childhood Education Journal, 49 (5), 925 – 934. https://doi.org/10.1007/s10643-021-01193-2</bibtext> </blist> <blist> <bibtext> Ford, T. G., Kwon, K.-A., & Tsotsoros, J. D. (2021). Early childhood distance learning in the U.S. during the COVID pandemic: Challenges and 2. Children & Youth Services Review, 131, 106297. https://doi.org/10.1016/j.childyouth.2021.106297</bibtext> </blist> <blist> <bibtext> Friedman-Krauss, A. H., Barnett, W. S., Garver, K. A., Hodges, K. S., Weisenfeld, G., Gardiner, B. A., & Jost, T. M. (2022). The state of preschool : 2021 : State preschool yearbook. https://nieer.org/wp-content/uploads/2022/04/YB2021_Full_Report.pdf</bibtext> </blist> <blist> <bibtext> Goldhaber, D., Kane, T. J., McEachin, A., Morton, E., Patterson, T., & Staiger, D. O. (2022). The consequences of remote and hybrid instruction during the pandemic.</bibtext> </blist> <blist> <bibtext> Griffiths, A. (2023). School enrollment rates of 3- and 4-year-olds returned to pre-pandemic levels in 2022. https://<ulink href="http://www.census.gov/library/stories/2023/11/preschool-enrollment-rebounds.html">www.census.gov/library/stories/2023/11/preschool-enrollment-rebounds.html</ulink></bibtext> </blist> <blist> <bibtext> Groom-Thomas, L., Kalogrides, D., Lee, M., Loeb, S., & Lynch, K. (2021). Acelero learning: Annual report. Annenberg Institute at Brown University.</bibtext> </blist> <blist> <bibtext> Halloran, C., Jack, R., Okun, J. C., & Oster, E. (2021). Pandemic schooling mode and student test scores: Evidence from US states.</bibtext> </blist> <blist> <bibtext> Hamilton, L., & Gross, B. (2021). How has the pandemic affected students' social-emotional well-being? A review of the evidence to date. Center on Reinventing Public Education.</bibtext> </blist> <blist> <bibtext> Hamre, B., & Pianta, R. (2005). Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development, 76 (5), 949 – 967. https://doi.org/10.1111/j.1467-8624.2005.00889.x</bibtext> </blist> <blist> <bibtext> Hanno, E. C., Cuartas, J., Miratrix, L. W., Jones, S. M., & Lesaux, N. K. (2022). Changes in children's behavioral health and family well-being during the COVID-19 pandemic. Journal of Developmental & Behavioral Pediatrics, 43 (3), 168 – 175. https://doi.org/10.1097/DBP.0000000000001010</bibtext> </blist> <blist> <bibtext> Hanno, E. C., Gonzalez, K. E., Gardner, M., Jones, S. M., Lesaux, N. K., Hofer, K. G., & Goodson, B. (2020). Pandemic meets preschool: Impacts of the COVID-19 outbreak on early education and care in Massachesetts. H. G. S. o. E. Saul Zaentz Early Education Initiative.</bibtext> </blist> <blist> <bibtext> Hayward, D., Stewart, E., Phillips, L., Norris, S., & Lovell, M. (2013). Language, phonological awareness, and reading test directory. Canadian Centre for Research on Literacy.</bibtext> </blist> <blist> <bibtext> Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2 (3), 172 – 177. https://doi.org/10.1111/j.1750-8606.2008.00061.x</bibtext> </blist> <blist> <bibtext> Holtzman, D. J., Manship, K., Quick, H., Hauser, A., & Jones, K. T. (2022). Experiences of families of dual language learners during COVID-19.</bibtext> </blist> <blist> <bibtext> Hunter, J. E., & Hamilton, M. A. (2002). The advantages of using standardized scores in causal analysis. Human Communication Research, 28 (4), 552 – 561. https://doi.org/10.1111/j.1468-2958.2002.tb00823.x</bibtext> </blist> <blist> <bibtext> Jung, K., & Barnett, W. S. (2021). Impacts of the pandemic on young children and their parents: Initial findings from NIEER's May-June 2021 preschool learning activities survey. National Institute for Early Education Research.</bibtext> </blist> <blist> <bibtext> Justice, L., Cain, K., Jiang, H., Logan, J., Jia, R., Jiang, H., Logan, J. A., & Jia, R. (2018). Modeling the nature of grammar and vocabulary trajectories from prekindergarten to third grade. Journal of Speech, Language, & Hearing Research, 61 (4), 910 – 923. https://doi.org/10.1044/2018_JSLHR-L-17-0090</bibtext> </blist> <blist> <bibtext> Kauhanen, L., Wan Mohd Yunus, W. M. A., Lempinen, L., Peltonen, K., Gyllenberg, D., Mishina, K., Gilbert, S., Bastola, K., Brown, J. S., & Sourander, A. (2022). A systematic review of the mental health changes of children and young people before and during the COVID-19 pandemic. European Child & Adolescent Psychiatry, 32 (6), 1 – 19. https://doi.org/10.1007/s00787-022-02060-0</bibtext> </blist> <blist> <bibtext> Kim, Y., Montoya, E., Doocy, S., Austin, L. J. E., & Whitebook, M. (2022). Impacts of COVID-19 on the early care and education sector in California: Variations across program types. Early Childhood Research Quarterly, 60, 348 – 362. https://doi.org/10.1016/j.ecresq.2022.03.004</bibtext> </blist> <blist> <bibtext> Kuhfeld, M., Soland, J., Lewis, K., Ruzek, E., & Johnson, A. (2022). The COVID-19 school year: Learning and recovery across 2020-2021. American Educational Research Association, 8. https://doi.org/10.1177/23328584221099306</bibtext> </blist> <blist> <bibtext> Lewis, K., & Kuhfeld, M. (2022). Progress toward pandemic recovery: Continued signs of rebounding achievement at the start of the 2022–23 school year. Center for School and Student Progress.</bibtext> </blist> <blist> <bibtext> LiBetti, A. (2019). Leading by exemplar: Case studies of head start programs. Bellweather Education Partners.</bibtext> </blist> <blist> <bibtext> Lipsey, M. W., Puzio, K., Yun, C., Hebert, M. A., Steinka-Fry, K., Cole, M. W., Roberts, M., Anthony, K. S., & Busick, M. D. (2012). Translating the statistical representation of the effects of education interventions into more readily interpretable forms. National Center for Special Education Research.</bibtext> </blist> <blist> <bibtext> Lonigan, C., Wagner, R., Torgesen, J., & Rashotte, C. (2007). Test of early preschool literacy. Pro-Ed, Inc.</bibtext> </blist> <blist> <bibtext> Masten, A. S. (2018). Resilience theory and research on children and families: Past, present, and promise. Journal of Family Theory & Review, 10 (1), 12 – 31. https://doi.org/10.1111/jftr.12255</bibtext> </blist> <blist> <bibtext> Masten, A. S., & Cicchetti, D. (2010). Developmental cascades. Development & Psychopathology, 22 (3), 491 – 495. https://doi.org/10.1017/S0954579410000222</bibtext> </blist> <blist> <bibtext> Mattera, S., Jacob, R., & Morris, P. (2018). Strengthening children's math skills with enhanced instruction: The impacts of making pre-K count and high 5s on kindergarten outcomes. MDRC.</bibtext> </blist> <blist> <bibtext> Mattera, S., Jacob, R. T., MacDowell, C., & Morris, P. (2021). Long-term effects of enhanced early childhood math instruction: The impacts of making pre-K count and high 5s on third-grade outcomes. Frontiers in Psychology, 12 (MDRC, Issue). https://doi.org/10.3389/fpsyg.2021.640702</bibtext> </blist> <blist> <bibtext> McCormick, M. P., & O'Connor, E. E. (2015). Teacher–child relationship quality and academic achievement in elementary school: Does gender matter? Journal of Educational Psychology, 107 (2), 502 – 516. https://doi.org/10.1037/a0037457</bibtext> </blist> <blist> <bibtext> McCormick, M. P., Weissman, A. K., Weiland, C., Hsueh, J., Sachs, J., & Snow, C. (2020). Time well spent: Home learning activities and gains in children's academic skills in the prekindergarten year. Developmental Psychology, 56 (4), 710 – 726. https://doi.org/10.1037/dev0000891</bibtext> </blist> <blist> <bibtext> McCormick, M., Weiland, C., Hsueh, J., Pralica, M., Moffett, C., Weissman, A. K., Snow, L., & Sachs, J. (2021). Is skill type the key to the PreK fadeout puzzle? Differential associations between enrollment in PreK and constrained and unconstrained skills across kindergarten. Child Development, 92 (4), 1 – 22. https://doi.org/10.1111/cdev.13520</bibtext> </blist> <blist> <bibtext> Meeter, M. (2021). Primary school mathematics during the COVID-19 pandemic: No evidence of learning gaps in adaptive practicing results. Trends in Neuroscience and Education, 25, 100163. https://doi.org/10.1016/j.tine.2021.100163</bibtext> </blist> <blist> <bibtext> Meherali, S., Punjani, N., Louie-Poon, S., Abdul Rahim, K., Das, J. K., Salam, R. A., & Lassi, Z. S. (2021). Mental health of children and adolescents amidst COVID-19 and past pandemics: A rapid systematic review. International Journal of Environmental Research and Public Health, 18 (7), 3432. https://doi.org/10.3390/ijerph18073432</bibtext> </blist> <blist> <bibtext> Mitchell, F. (2020). COVID-19's disproportionate effects on children of color will challenge the next generation.</bibtext> </blist> <blist> <bibtext> Moffett, L., Weissman, A., McCormick, M., Weiland, C., Hsueh, J., Snow, C., & Sachs, J. (2022). Enrollment in pre-K and children's social-emotional and executive functioning skills: To what extent are associations sustained across time? Journal of Educational Psychology. https://doi.org/10.1037/edu0000782</bibtext> </blist> <blist> <bibtext> Na, L., Yang, L., Mezo, P. G., & Liu, R. (2022). Age disparities in mental health during the COVID-19 pandemic: The roles of resilience and coping. Social Science & Medicine, 305, 115031. https://doi.org/10.1016/j.socscimed.2022.115031</bibtext> </blist> <blist> <bibtext> NCES. (2022). 2022 long-term trend reading and mathematics age 9 highlights report (Report No. NCES 2022121). https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2022121</bibtext> </blist> <blist> <bibtext> Orengo-Aguayo, R., Stewart, R. W., de Arellano, M. A., Suárez-Kindy, J. L., & Young, J. (2019). Disaster exposure and mental health among Puerto Rican youths after Hurricane Maria. JAMA Network Open, 2 (4), e192619 – e192619. https://doi.org/10.1001/jamanetworkopen.2019.2619</bibtext> </blist> <blist> <bibtext> Osofsky, J. D. (2004). Young children and trauma: Intervention and treatment. Guilford Press.</bibtext> </blist> <blist> <bibtext> Panchal, N., Kamal, R., Cox, C., Garfield, R., & Chidambaram, P. (2021). Mental health and substance use considerations among children during the COVID-19 pandemic. Kaiser Family Foundation. https://<ulink href="http://www.kff.org/mental-health/issue-brief/mental-health-and-substance-use-considerations-among-children-during-the-covid-19-pandemic/">www.kff.org/mental-health/issue-brief/mental-health-and-substance-use-considerations-among-children-during-the-covid-19-pandemic/</ulink></bibtext> </blist> <blist> <bibtext> Paris, S. G. (2005). Reinterpreting the development of reading skills. Reading Research Quarterly, 40 (2), 184 – 202. https://doi.org/10.1598/RRQ.40.2.3</bibtext> </blist> <blist> <bibtext> Perone, S., Palanisamy, J., & Carlson, S. M. (2018). Age‐related change in brain rhythms from early to middle childhood: Links to executive function. Developmental Science, 21 (6), e12691. https://doi.org/10.1111/desc.12691</bibtext> </blist> <blist> <bibtext> Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom assessment scoring system™: Manual K-3. Paul H Brookes Publishing.</bibtext> </blist> <blist> <bibtext> Puma, M., Bell, S., Cook, R., Heid, C., Shapiro, G., Broene, P., Jenkins, F., Fletcher, P., Quinn, L., & Friedman, J. (2010). Head start impact study: Final report.</bibtext> </blist> <blist> <bibtext> Santibanez, L., & Guarino, C. M. (2021). The effects of absenteeism on academic and social-emotional outcomes: Lessons for COVID-19. Educational Researcher, 50 (6), 392 – 400. https://doi.org/10.3102/0013189X21994488</bibtext> </blist> <blist> <bibtext> Schrank, F. A., McGrew, K. S., & Mather, N. (2014). Woodcock-Johnson IV. Riverside Publishing.</bibtext> </blist> <blist> <bibtext> Snijders, T. A., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). SAGE Publications Ltd.</bibtext> </blist> <blist> <bibtext> Snow, C. E., & Matthews, T. J. (2016). Reading and language in the early grades. The Future of Children, 26 (2), 57 – 74. https://doi.org/10.1353/foc.2016.0012</bibtext> </blist> <blist> <bibtext> Son, S. H., & Morrison, F. J. (2010). The nature and impact of changes in home learning environment on development of language and academic skills in preschool children. Developmental Psychology, 46 (5), 1103. https://doi.org/10.1037/a0020065</bibtext> </blist> <blist> <bibtext> Tudge, J. R., Merçon-Vargas, E. A., Liang, Y., & Payir, A. (2017). The importance of Urie Bronfenbrenner's bioecological theory for early childhood education. In L. Cohen & S. Stupiansky (Eds.), Theories of early childhood education: Developmental, behaviorist, and critical (pp. 45 – 57). Routledge. https://doi.org/10.4324/9781315641560</bibtext> </blist> <blist> <bibtext> Weiland, C., Greenberg, E., Bassok, D., Markowitz, A., Rosada, P. G., Luetmer, G., Abenavoli, R., Gomez, C., Johnson, A., Jones-Harden, B., Maier, M., McCormick, M., Morris, P., Nores, M., Phillips, D., & Snow, C. (2021). Historic crisis, historic opportunity: Using evidence to mitigate the effects of the COVID-19 crisis on young children and early care and education programs. U. Institute.</bibtext> </blist> <blist> <bibtext> Weiland, C., Moffett, L., Rosada, P. G., Weissman, A., Zhang, K., Maier, M., Snow, C., McCormick, M., Hsueh, J., & Sachs, J. (2023). Learning experiences vary across young children in the same classroom: Evidence from the individualizing student instruction measure in the Boston Public Schools. Early Childhood Research Quarterly, 63, 313 – 326. https://doi.org/10.1016/j.ecresq.2022.11.008</bibtext> </blist> <blist> <bibtext> Weiland, C., & Morris, P. (2022). The risks and opportunities of the COVID-19 crisis for building longitudinal evidence on today's early childhood education programs. Child Development Perspectives, 16 (2), 76 – 81. https://doi.org/10.1111/cdep.12445</bibtext> </blist> <blist> <bibtext> Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children's mathematics, language, literacy, executive function, and emotional skills. Child Development, 84 (6), 2112 – 2130. https://doi.org/10.1111/cdev.12099</bibtext> </blist> <blist> <bibtext> Werner, K., & Woessmann, L. (2021). The legacy of COVID-19 in education. CESifo Working Paper No. 9358. https://doi.org/10.2139/ssrn.3945280</bibtext> </blist> <blist> <bibtext> Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson III. NU Complete. Riverside Publishing.</bibtext> </blist> <blist> <bibtext> Zelazo, P. D. (2006). The dimensional change card sort (DCCS): A method of assessing executive function in children. Nature Protocols, 1 (1), 297 – 301. https://doi.org/10.1038/nprot.2006.46</bibtext> </blist> </ref> <aug> <p>By Meghan McCormick; Maya Goldberg; Emily Swinth; Cate Smith Todd; Lydia Carlis; Victoria Chavez and Samantha Xia</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib79" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib28" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib61" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib12" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib17" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib58" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib77" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib40" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib23" firstref="ref9"></nolink> <nolink nlid="nl10" bibid="bib56" firstref="ref10"></nolink> <nolink nlid="nl11" bibid="bib57" firstref="ref12"></nolink> <nolink nlid="nl12" bibid="bib19" firstref="ref13"></nolink> <nolink nlid="nl13" bibid="bib20" firstref="ref14"></nolink> <nolink nlid="nl14" bibid="bib64" firstref="ref15"></nolink> <nolink nlid="nl15" bibid="bib31" firstref="ref17"></nolink> <nolink nlid="nl16" bibid="bib27" firstref="ref18"></nolink> <nolink nlid="nl17" bibid="bib29" firstref="ref19"></nolink> <nolink nlid="nl18" bibid="bib45" firstref="ref20"></nolink> <nolink nlid="nl19" bibid="bib42" firstref="ref22"></nolink> <nolink nlid="nl20" bibid="bib75" firstref="ref25"></nolink> <nolink nlid="nl21" bibid="bib60" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib49" firstref="ref30"></nolink> <nolink nlid="nl23" bibid="bib63" firstref="ref31"></nolink> <nolink nlid="nl24" bibid="bib13" firstref="ref32"></nolink> <nolink nlid="nl25" bibid="bib16" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib62" firstref="ref34"></nolink> <nolink nlid="nl27" bibid="bib74" firstref="ref35"></nolink> <nolink nlid="nl28" bibid="bib18" firstref="ref36"></nolink> <nolink nlid="nl29" bibid="bib50" firstref="ref37"></nolink> <nolink nlid="nl30" bibid="bib55" firstref="ref39"></nolink> <nolink nlid="nl31" bibid="bib65" firstref="ref40"></nolink> <nolink nlid="nl32" bibid="bib72" firstref="ref41"></nolink> <nolink nlid="nl33" bibid="bib68" firstref="ref43"></nolink> <nolink nlid="nl34" bibid="bib78" firstref="ref44"></nolink> <nolink nlid="nl35" bibid="bib38" firstref="ref45"></nolink> <nolink nlid="nl36" bibid="bib46" firstref="ref48"></nolink> <nolink nlid="nl37" bibid="bib30" firstref="ref49"></nolink> <nolink nlid="nl38" bibid="bib21" firstref="ref51"></nolink> <nolink nlid="nl39" bibid="bib51" firstref="ref53"></nolink> <nolink nlid="nl40" bibid="bib76" firstref="ref55"></nolink> <nolink nlid="nl41" bibid="bib67" firstref="ref56"></nolink> <nolink nlid="nl42" bibid="bib39" firstref="ref59"></nolink> <nolink nlid="nl43" bibid="bib24" firstref="ref60"></nolink> <nolink nlid="nl44" bibid="bib70" firstref="ref62"></nolink> <nolink nlid="nl45" bibid="bib80" firstref="ref63"></nolink> <nolink nlid="nl46" bibid="bib36" firstref="ref64"></nolink> <nolink nlid="nl47" bibid="bib48" firstref="ref65"></nolink> <nolink nlid="nl48" bibid="bib15" firstref="ref66"></nolink> <nolink nlid="nl49" bibid="bib81" firstref="ref67"></nolink> <nolink nlid="nl50" bibid="bib66" firstref="ref68"></nolink> <nolink nlid="nl51" bibid="bib22" firstref="ref69"></nolink> <nolink nlid="nl52" bibid="bib14" firstref="ref74"></nolink> <nolink nlid="nl53" bibid="bib47" firstref="ref80"></nolink> <nolink nlid="nl54" bibid="bib37" firstref="ref81"></nolink> <nolink nlid="nl55" bibid="bib71" firstref="ref87"></nolink> <nolink nlid="nl56" bibid="bib10" firstref="ref98"></nolink> <nolink nlid="nl57" bibid="bib34" firstref="ref99"></nolink> <nolink nlid="nl58" bibid="bib26" firstref="ref100"></nolink> <nolink nlid="nl59" bibid="bib43" firstref="ref101"></nolink> <nolink nlid="nl60" bibid="bib35" firstref="ref102"></nolink> <nolink nlid="nl61" bibid="bib54" firstref="ref103"></nolink> <nolink nlid="nl62" bibid="bib73" firstref="ref104"></nolink> <nolink nlid="nl63" bibid="bib59" firstref="ref105"></nolink> <nolink nlid="nl64" bibid="bib69" firstref="ref107"></nolink> <nolink nlid="nl65" bibid="bib44" firstref="ref109"></nolink> <nolink nlid="nl66" bibid="bib11" firstref="ref112"></nolink> <nolink nlid="nl67" bibid="bib32" firstref="ref116"></nolink> <nolink nlid="nl68" bibid="bib41" firstref="ref117"></nolink> <nolink nlid="nl69" bibid="bib53" firstref="ref118"></nolink> <nolink nlid="nl70" bibid="bib33" firstref="ref119"></nolink> <nolink nlid="nl71" bibid="bib25" firstref="ref120"></nolink> <nolink nlid="nl72" bibid="bib52" firstref="ref121"></nolink>
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  Data: <searchLink fieldCode="AR" term="%22Meghan+McCormick%22">Meghan McCormick</searchLink><br /><searchLink fieldCode="AR" term="%22Maya+Goldberg%22">Maya Goldberg</searchLink><br /><searchLink fieldCode="AR" term="%22Emily+Swinth%22">Emily Swinth</searchLink><br /><searchLink fieldCode="AR" term="%22Cate+Smith+Todd%22">Cate Smith Todd</searchLink><br /><searchLink fieldCode="AR" term="%22Lydia+Carlis%22">Lydia Carlis</searchLink><br /><searchLink fieldCode="AR" term="%22Victoria+Chavez%22">Victoria Chavez</searchLink><br /><searchLink fieldCode="AR" term="%22Samantha+Xia%22">Samantha Xia</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Early+Education+and+Development%22"><i>Early Education and Development</i></searchLink>. 2025 36(3):515-541.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 27
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Pandemics%22">Pandemics</searchLink><br /><searchLink fieldCode="DE" term="%22Emergent+Literacy%22">Emergent Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Skills%22">Language Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Skills%22">Mathematics Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Executive+Function%22">Executive Function</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Development%22">Child Development</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Structure%22">Family Structure</searchLink><br /><searchLink fieldCode="DE" term="%22Low+Income+Students%22">Low Income Students</searchLink><br /><searchLink fieldCode="DE" term="%22Federal+Programs%22">Federal Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Services%22">Social Services</searchLink><br /><searchLink fieldCode="DE" term="%22Early+Intervention%22">Early Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+Status%22">Socioeconomic Status</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Differences%22">Racial Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Usage%22">Language Usage</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Characteristics%22">Individual Characteristics</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Nevada%22">Nevada</searchLink><br /><searchLink fieldCode="DE" term="%22New+Jersey%22">New Jersey</searchLink><br /><searchLink fieldCode="DE" term="%22Pennsylvania+%28Philadelphia%29%22">Pennsylvania (Philadelphia)</searchLink><br /><searchLink fieldCode="DE" term="%22Wisconsin+%28Milwaukee%29%22">Wisconsin (Milwaukee)</searchLink>
– Name: SubjectThesaurus
  Label: Laws, Policies and Program Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Head+Start%22">Head Start</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Peabody+Picture+Vocabulary+Test%22">Peabody Picture Vocabulary Test</searchLink><br /><searchLink fieldCode="SU" term="%22Classroom+Assessment+Scoring+System%22">Classroom Assessment Scoring System</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/10409289.2024.2423384
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1040-9289<br />1556-6935
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Research Findings: The COVID-19 pandemic had significant negative effects on the learning and development of school-aged children in the United States, with disproportionate impacts on children from marginalized groups. There is less evidence on the extent to which the pandemic affected younger children -- ages 3 to 5 -- from these groups. The current study examined the extent to which children in Acelero Head Start centers (N = 343) made gains in literacy, language, math, and executive functioning 2 years after the start of the pandemic and compared those learning gains to pre-pandemic norms in national Head Start and Acelero comparison samples. Children grew rapidly in all domains, performing and gaining in line with (or faster than) pre-pandemic Acelero Head Start children in language, literacy, and executive functioning. Overall scores were lower and growth was slower in math than pre-pandemic levels. Four-year-old children in the current study generally made larger gains than their younger peers. Boys and children from single parent households made larger gains in language skills compared to girls and children from two-parent households, respectively. Practice or Policy: Results provide evidence on Head Start children's academic and cognitive skills during the pandemic recovery and highlight the need for continued research to support children's resilience.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1499534
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1499534
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        Value: 10.1080/10409289.2024.2423384
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        PageCount: 27
        StartPage: 515
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      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: Emergent Literacy
        Type: general
      – SubjectFull: Language Skills
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      – SubjectFull: Mathematics Skills
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      – SubjectFull: Executive Function
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      – SubjectFull: Child Development
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      – SubjectFull: Age Differences
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      – SubjectFull: Gender Differences
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      – SubjectFull: Family Structure
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      – SubjectFull: Low Income Students
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      – SubjectFull: Federal Programs
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      – SubjectFull: Social Services
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      – SubjectFull: Early Intervention
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      – SubjectFull: Racial Differences
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      – SubjectFull: Ethnicity
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      – SubjectFull: Language Usage
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      – SubjectFull: Individual Characteristics
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      – SubjectFull: Nevada
        Type: general
      – SubjectFull: New Jersey
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      – SubjectFull: Pennsylvania (Philadelphia)
        Type: general
      – SubjectFull: Wisconsin (Milwaukee)
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      – SubjectFull: Classroom Assessment Scoring System
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    Titles:
      – TitleFull: Understanding Young Children's Learning and Development in the Wake of the Pandemic: Evidence from Acelero Head Start Programs
        Type: main
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