Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., & Liu, J. (2020). Projecting the potential impacts of COVID-19 school closures on academic achievement. Educational Researcher, 49(8), 549–565.
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.
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.
The Stanford Educational Data Archive (SEDA) is the first data set to allow comparisons of district academic achievement and growth from Grades 3 to 8 across the United States, shining a light on the distribution of educational opportunities. This study describes a convergent validity analysis of the SEDA growth estimates in mathematics and English Language Arts (ELA) by comparing the SEDA estimates against estimates derived from NWEA’s MAP Growth assessments. We find strong precision-adjusted correlations between growth estimates from SEDA and MAP Growth in math (.90) and ELA (.82). We also find that the discrepancy between the growth estimates in ELA is slightly more pronounced in high socioeconomic districts. Our analyses indicate a high degree of congruence between the SEDA estimates and estimates derived from the vertically scaled MAP Growth assessment. However, small systematic discrepancies imply that the SEDA growth estimates are less likely to generalize to estimates obtained through MAP Growth in some states.
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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