We specialize in a variety of ways to analyze data and create metrics that help our partners better understand their students, teachers, and leaders. Our team uses a co-build process to produce results that help illuminate patterns as they relate to student growth, human capital, social-emotional learning, and more.
We explore these types of challenges:
How similar are school performance measures based on public data to measures based on student-level data?
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.
What are appropriate and valid uses for student self-report responses on a Social-Emotional learning (SEL) survey?
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.
How do we combine data from multiple stages of students’ educational journeys to estimate predicted student trajectories?
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.
Research-Based Analytics topics:
Student Growth Metrics
We help our partners measure student learning over time by showing how much progress can be attributed to teachers and schools, separate from other factors that affect students’ learning. We also help our partners set realistic yet rigorous goals for student growth that allow teachers and school leaders to customize instructional practices to the needs of their students.
Human Capital Analytics
We analyze data about teachers and school leaders so we can provide our partners with human capital metrics that support recruitment, hiring, professional development, and teacher retention practices. We not only describe the current state of your human capital pipeline, but also help you forecast future states, validate measures of human capital quality, and evaluate the results of human capital interventions.
Public Data Metrics
Our team collects and analyzes publicly available data to create metrics used for improvement and investment decisions. We currently collect public data from nineteen states in several programmatic areas.
We use analytics to predict the likelihood of students' meeting educational benchmarks, such as proficiency on a state assessment or entering post-secondary education. We also develop metrics to support progress monitoring toward those milestones, and evaluate the extent to which predictions are correct when actual data are known.
School Improvement Metrics
Our team produces timely and user-centered analytics that support continuous improvement work. We develop annual and periodic reporting to better understand the indicators and determinants of student success and provide user-friendly reports on student-, cohort-, and school-level outcomes. Reports can be used to implement a set of research-based practices around 8th-to-9th grade transition supports, collaboration, and tiered interventions.
EA brought a level of rigor to our results. They give us a level of credibility. They’re at the forefront of grappling with how we measure SEL skills in kids through surveys when we know there are inherent biases and issues with the survey instrument.
Interested in Learning More About EA?
We want to empower you to be informed and discerning data consumers. We get excited about the work we do and are enthusiastic about the changes it can bring to education.