The schooling disruptions due to the COVID-19 pandemic continue to reverberate across the K-12 educational system more than a year after schools closed for in-person instruction. In this study, we examined the aftermath of these disruptions by modeling student achievement trends prior to and during the pandemic, with particular focus on growth in 2020-2021. The data included test scores from 4.9 million U.S. students in Grades 3 through 8. Although the average student demonstrated positive gains in math and reading during the 2020-2021 school year, students were still behind typical (pre-pandemic) averages by spring 2021 (0.16 to 0.26 standard deviations behind in math and 0.06 to 0.11 standard deviations behind in reading). Furthermore, growth in math was more variable than in prior years, and much of the gains occurred among initially high-performing students pulling further ahead. Findings support the theory that the pandemic left students behind academically across the board while also worsening existing educational inequities.
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.
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.
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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https://scholar.google.com/citations?user=fkArtoQAAAAJ&hl=en
Megan Kuhfeld 