True to our non-profit nature, we believe in sharing our knowledge.
We’re motivated by something greater than the bottom-line. We believe in supporting both the health of the education system in the United States and the well-being of each and every student. So we make a point to share what we learn with others who are positioned to make a difference, too.
This resource provides guidance for education agencies that are beginning to consider whether and how they might implement Ed-Fi to better manage their data. We define some key terms and concepts, walk through the five steps to get started with Ed-Fi, and include a list of resources where you can learn more.
Recent attempts to measure schools’ influence on students’ social-emotional learning (SEL) show differences across schools, but whether these estimated differences measure the true effects of schools remain unclear. To better understand these measures, we examine the stability of estimated school-by-grade effects across two years using large-scale survey data.
This resource provides guidance for education agencies that are considering how and when to evaluate programs designed to address the impacts of COVID-19 on student outcomes. We present some key questions to consider, share common data requirements, and outline different options for designing the analytic approach.
This report examines how New York City teachers from the Relay Graduate School of Education influenced students’ attendance. We found that Relay teachers had positive, statistically significant impacts on students’ attendance, especially for students from historically marginalized backgrounds.
This resource is meant to support education agencies as they consider additional data they can use to identify where students are academically, including EA’s lessons learned in working with these data. We include guiding questions and data requirements that agencies can use to start conversations with their internal stakeholders.
This infographic highlights EA's research into changes in learning patterns experienced by students in grades 3–8 in California and South Carolina. Using results from winter 2020–21 interim assessments, EA provides an up-to-date picture of the learning lag students have experienced during the pandemic. EA also highlights findings from a well-being student survey collected during the 2020–21 school year.
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.