Our expertise is rooted in decades of experience with student-level data, and how to use such data to answer questions about how schools, districts, and states are progressing in terms of student learning and the closing of educational opportunity gaps. Many of our partners are interested in these same questions, but do not have access to student-level data that are the gold standard for answering those questions. At EA, we have explored how to apply our best-in-class expertise in student growth metrics to publicly available data about student proficiency and performance over time. In doing so, we aim to expand access to these kinds of metrics for a lower cost to a wider range of partners, all in the spirit of open-source metrics for the betterment of K-12 public education.

We have years of experience in co-developing, analyzing, improving, and reporting survey data on student SEL and related topics. There have long been concerns about how to align appropriate reliability and validity interpretations of survey measures to their intended uses, as well as how to avoid potential reference bias and social desirability bias. At EA, we carefully research and explore the range of analytics and metrics that can be produced using student SEL data, as well as the appropriateness of those analytics and metrics for different audiences, applications, and contexts. We not only advise our partners on aligning the design and selection of SEL survey measures to their intended use, but also conduct rigorous analyses of the measures themselves, such as their psychometric properties at the student and aggregate levels and associations with academic outcomes. By promoting appropriate interpretations of survey responses for different uses, we help bridge research and practice using a whole-child lens.

At EA, predictive analytics is more than just a buzzword. We understand the importance of early intervention for ensuring students get on track and stay on track towards their post-secondary goals. Currently, most state or district data systems do not seamlessly integrate data from early education, K-12 education, career and technical pathways, two- and four-year post-secondary institutions, and the labor market. At EA, we are working to build datasets that span students' educational journeys so we can link together statistical models that predict students’ on-track status towards meeting key educational milestones, like high school readiness, college readiness, and early college success. Our models can also update those predictions as newer information about students becomes available. Though difficult from a data collection and management perspective, these ongoing efforts strengthen our ability to identify students who may be off-track to meeting their long-term goals early enough for educators, parents, and students themselves to be able to make informed choices with the potential for real impact.