- James Soland
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Many dimensions of teacher working conditions influence both teacher and student outcomes; yet, analyses of schools’ overall working conditions are challenged by high correlations among the dimensions. Our study overcame this challenge by applying latent profile analysis of Virginia teachers’ perceptions of school leadership, instructional agency, professional growth opportunities, rigorous instruction, managing student behavior, family engagement, physical environment, and safety. We identified four classes of schools: Supportive (61%), Unsupportive (7%), Unstructured (22%), and Structured (11%). The patterns of these classes suggest schools may face tradeoffs between factors such as more teacher autonomy for less instructional rigor or discipline. Teacher satisfaction and their stated retention intentions were correlated with their school’s working conditions classes, and school contextual factors predicted class membership. By identifying formerly unseen profiles of teacher working conditions and considering the implications of being a teacher in each, decisionmakers can provide schools with targeted supports and investments.
Reclassification can be an important juncture in the academic experience of English Learners (ELs). Literature has explored the potential for reclassification to influence academic outcomes like achievement, yet its impact on social-emotional learning (SEL) skills, which are as malleable and important to long-term success, remains unclear. Using a regression discontinuity design, we examine the causal effect of reclassification on SEL skills (self-efficacy, growth mindset, self-management, and social awareness) among 4th to 8th graders. In the districts studied, reclassification improved academic self-efficacy by 0.2 standard deviations for students near the threshold. Results are robust to alternative specifications and analyses. Given this evidence, we discuss ways districts might establish practices that instill more positive academic beliefs among ELs.
An important subgroup of English learner-classified (EL) students immigrate to the U.S., entering U.S. schools upon their arrival. Using growth models and statewide data, this study asks first, how newcomers’ English proficiency status and growth compare to those of non-newcomer EL students; and second, what characteristics are associated with differences in English language growth patterns among newcomers. We find that newcomers enter school at earlier stages of English proficiency compared to their non-newcomer peers, but grow faster, especially in their first two years. We also find variation in growth patterns suggestive that schools play an important role in fostering growth.
The COVID-19 pandemic has been a seismic and on-going disruption to K-12 schooling. Using test scores from 5.4 million U.S. students in grades 3-8, we tracked changes in math and reading achievement across the first two years of the pandemic. Average fall 2021 math test scores in grades 3-8 were .20-27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores decreased by .09-.18 SDs. Achievement gaps between students in low-poverty and high-poverty elementary schools grew by .10-.20 SDs, primarily during the 2020-21 school year. Observed declines are more substantial than during other recent school disruptions, such as those due to natural disasters.
Nearly one in five U.S. students attends a rural school, yet we know very little about achievement gaps and academic growth in rural schools. This study leverages a unique dataset that includes longitudinal test scores for more than five million 3rd to 8th grade students in approximately 17,000 public schools across the 50 states, including 900,000 students attending 4,727 rural schools. We find rural achievement and growth to be slightly above public schools. But there is considerable heterogeneity by student race/ethnicity. For all grades and subjects, White-Black and White-Hispanic gaps are smaller in rural schools than gaps nationwide, and White-Native American gaps are larger in rural schools than gaps nationwide. Separate analyses by racial/ethnic subgroup show that rural Black, Hispanic, and Native American students are often growing slower than their respective subgroup national average. In contrast, White students are often growing faster than the national average for White students.
With 55 million students in the United States out of school due to the COVID-19 pandemic, education systems are scrambling to meet the needs of schools and families, including planning how best to approach instruction in the fall given students may be farther behind than in a typical year. Yet, education leaders have little data on how much learning has been impacted by school closures. While the COVID-19 learning interruptions are unprecedented in modern times, existing research on the impacts of missing school (due to absenteeism, regular summer breaks, and school closures) on learning can nonetheless inform projections of potential learning loss due to the pandemic. In this study, we produce a series of projections of COVID-19-related learning loss and its potential effect on test scores in the 2020-21 school year based on (a) estimates from prior literature and (b) analyses of typical summer learning patterns of five million students. Under these projections, students are likely to return in fall 2020 with approximately 63-68% of the learning gains in reading relative to a typical school year and with 37-50% of the learning gains in math. However, we estimate that losing ground during the COVID-19 school closures would not be universal, with the top third of students potentially making gains in reading. Thus, in preparing for fall 2020, educators will likely need to consider ways to support students who are academically behind and further differentiate instruction.
Important educational policy decisions, like whether to shorten or extend the school year, often require accurate estimates of how much students learn during the year. Yet, related research relies on a mostly untested assumption: that growth in achievement is linear throughout the entire school year. We examine this assumption using a data set containing math and reading test scores for over seven million students in kindergarten through 8th grade across the fall, winter, and spring of the 2016-17 school year. Our results indicate that assuming linear within-year growth is often not justified, particularly in reading. Implications for investments in extending the school year, summer learning loss, and racial/ethnic achievement gaps are discussed.
A huge portion of what we know about how humans develop, learn, behave, and interact is based on survey data. Researchers use longitudinal growth modeling to understand the development of students on psychological and social-emotional learning constructs across elementary and middle school. In these designs, students are typically administered a consistent set of self-report survey items across multiple school years, and growth is measured either based on sum scores or scale scores produced based on item response theory (IRT) methods. While there is great deal of guidance on scaling and linking IRT-based large-scale educational assessment to facilitate the estimation of examinee growth, little of this expertise is brought to bear in the scaling of psychological and social-emotional constructs. Through a series of simulation and empirical studies, we produce scores in a single-cohort repeated measure design using sum scores as well as multiple IRT approaches and compare the recovery of growth estimates from longitudinal growth models using each set of scores. Results indicate that using scores from multidimensional IRT approaches that account for latent variable covariances over time in growth models leads to better recovery of growth parameters relative to models using sum scores and other IRT approaches.
Survey respondents use different response styles when they use the categories of the Likert scale differently despite having the same true score on the construct of interest. For example, respondents may be more likely to use the extremes of the response scale independent of their true score. Research already shows that differing response styles can create a construct-irrelevant source of bias that distorts fundamental inferences made based on survey data. While some initial studies examine the effect of response styles on survey scores in longitudinal analyses, the issue of how response styles affect estimates of growth is underexamined. In this study, we conducted empirical and simulation analyses in which we scored surveys using item response theory (IRT) models that do and do not account for response styles, and then used those different scores in growth models and compared results. Generally, we found that response styles can affect estimates of growth parameters including the slope, but that the effects vary by psychological construct, response style, and model used.
Students’ level of academic skills at school entry are a strong predictor of later academic success, and focusing on improving these skills during the preschool years has been a priority during the past ten years. Evidence from two prior nationally representative studies indicated that incoming kindergarteners’ math and literacy skills were higher in 2010 than 1998, but no national studies have examined trends since 2010. This study examines academic skills at kindergarten entry from 2010 and 2017 using data from over 2 million kindergarten students. Results indicated kindergarteners in 2017 have slightly lower math and reading skills than in 2010, but that inequalities at school entry by race/ethnicity and school poverty level have decreased during this period.