As the COVID-19 pandemic upended the 2019–2020 school year, education systems scrambled to meet the needs of students and families with little available data on how school closures may impact learning. In this study, we produced a series of projections of COVID-19-related learning loss based on (a) estimates from absenteeism literature and (b) analyses of summer learning patterns of 5 million students. Under our projections, returning students are expected to start fall 2020 with approximately 63 to 68% of the learning gains in reading and 37 to 50% of the learning gains in mathematics relative to a typical school year. However, we project that losing ground during the school closures was not universal, with the top third of students potentially making gains in reading.
Results identified specific SRS items that are more vulnerable to non-ASD-related traits. The resultant 16-item SRS short form may possess superior psychometric properties compared to the original scale and emerge as a more precise measure of ASD core symptom severity, facilitating research and practice. Future research using IRT is needed to further refine existing measures of autism symptomatology.
The COVID-19 pandemic has been a seismic and ongoing 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 2 years of the pandemic. Average math test scores in the fall of 2021 in Grades 3–8 were .20–.27 standard deviation (SD) lower relative to same-grade peers in the fall of 2019, while reading test scores decreased by .09–.18 SD. Achievement gaps between students in low-poverty and high-poverty elementary schools grew by .10–.20 SD, primarily during the 2020–2021 school year. Achievement disparities by student race/ethnicity also widened substantively. Observed declines are more substantial than during other recent school disruptions, such as those due to natural disasters.
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. Although 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.
It has been common knowledge for decades that poor and working-class students tend to experience “summer learning loss,” a drop in performance between spring and fall that serves to widen the gap between students. However, new research shows that the reality of summer learning loss is more complex. Megan Kuhfeld draws on data from the 3.4 million students who took the NWEA MAP Growth assessments to find that summer slide is common, but not inevitable. According to the data, the students who experienced the greatest loss were those who made the greatest gains during the previous school year. The research also calls into question about the usual explanations for learning loss, such as access to summer programs and length of the school year.
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