Resources

We used data from interim assessments to examine how the rate of student learning from fall 2019 through winter 2020–21 differed from that of student learning before COVID-19 for approximately 100,000 students in grades 4-8 across 19 local education agencies in California. Results showed that students experienced approximately 2.5 months of learning lag on average in math and ELA, with more substantial learning lag for students who are economically disadvantaged, English Learners, and Latinx.

We drew on data from the CORE Districts' Student Well-Being and Learning Conditions Survey, given to 32,000 students in Grades 4–12 from three districts at the beginning of the 2020–21 school year and to 15,000 students again a few months later. We examined patterns in responses by student characteristics, the connection between well-being and academic performance, and changes in students' responses from fall to winter.

In this document, we provide technical details on our learning change models, which estimate how much faster or slower students have grown during COVID-19. We also summarizes results from both fall-to-fall and fall-to-winter learning change models in South Carolina and California.

EA developed this guide and accompanying webinar to support our partners in investigating the impacts of missing data due to COVID-19-related school closures and making adjustments in order to account for data limitations.

In partnership with the CORE Data Collaborative and the National Campaign for Highest-Need Students, we developed a School Needs Index to help us look across schools to understand the variation of student need in a common, digestible, and actionable way. This working paper describes the School Needs Index, which uses academic performance, demographic, and economic indicators to yield a nuanced profile of the level of student need at a school.

This brief is one in a series aimed at providing K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students during and following the novel coronavirus pandemic.

We used mixture IRT models to evaluate confusion due to the negative wording of certain items on a social-emotional learning (SEL) survey. We also evaluated the consequences of the potential confusion. We found evidence of rating scale confusion due to negatively worded items. We also found that confusion was most prevalent at lower grade levels and was positively related to both reading proficiency and ELL status.

We used social-emotional learning survey data to simulate how four constructs—growth mindset, self-efficacy, self-management, and social awareness—develop from grades 4 to 12 and how these trends vary by gender, socioeconomic status, and race/ethnicity among students for two consecutive years. We found that, with the exception of growth mindset, self-reports of these constructs do not increase monotonically as students move through school; self-efficacy, social awareness, and, to a lesser degree, self-management decrease after Grade 6.

This guide outlines the steps that organizations might consider for measuring students’ social and emotional learning (SEL). We highlight the lessons we have learned from the research that Education Analytics has conducted on SEL survey measures. We also discuss future directions of SEL measurement that policymakers and practitioners at the state and district level should consider.