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.

Districts, states, and researchers are using value-added models with increasing frequency to evaluate educational policies and programs, as well as teachers and other educators individually. Despite their prevalence, little research assesses whether value-added measures (VAM) are consistent across student subgroups. Are teachers who are effective with one group of students also effective with others? If they are not, then it may be worthwhile to develop separate measures of teacher effectiveness for different student groups; if they are, a single average measure will likely suffice. Our article uses data from a large urban district with a considerable English learner (EL) population to compare teachers' VAM with ELs to the same teachers' VAM with non-ELs. We find that teachers who are effective with ELs also tend to be effective with their non-ELs and vice versa. We also, however, find evidence that some teachers are relatively more effective with ELs than with non-ELs, and that this increased efficacy is predicted by a teacher's fluency in students' home language and whether he or she possesses a bilingual teaching certification.

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.