Our Work
Our Work
The Future of Evidence in Education Network examines how education evidence is generated, interpreted, and used. Our first two reports offer guidance for evaluating causal evidence and recommendations for strengthening the education R&D system. Our current work asks how evidence frameworks should apply to EdTech and AI-enabled tools that change over time.
Reports
Our First Two Reports Focus on Evaluating Evidence and Strengthening the Education R&D System
Evaluating Causal Evidence in Education: A Practical Guide
This report presents ten principles for evaluating causal evidence in education. The principles focus on the information decision-makers need to understand what a study shows, what it does not show, and how useful the evidence is for a particular decision.
Report Credits
Developed through: The Future of Evidence in Education Convening
Report team: Beth Boulay, Laura Peck, Jessaca Spybrook, Elizabeth Tipton, and Betsy Wolf
Report editors: Cara Jackson and Luke Miratrix
Series editor: Vivian C. Wong
The Report Focuses On
Study design, implementation and costs, comparison conditions, effect sizes, outcomes, context, bodies of evidence, subgroup claims, and personalization claims.
Necessary but Not Sufficient: Six Design Principles for a Stronger Education R&D System
This report identifies six design principles for strengthening the broader education R&D system. The principles address how evidence is generated, reported, accumulated, and connected to the decisions education systems face.
Report Credits
Developed through: The Future of Evidence in Education Convening
Report team: Beth Boulay, Rekha Balu, Ben Domingue*, Brooks Bowden, Dan Goldhaber, Robert B. Olsen, and Brian Wright
Report editors: Kelly Hallberg and Erin Higgins
Series editor: Vivian C. Wong
* Ben Domingue participated in the Systems report team and asked to be acknowledged rather than listed as an author.
The Report Focuses On
Rigorous evidence, transparent reporting, synthesis, capacity and partnerships, public investment, and assessment of the R&D system itself.
Advancing Evidence for EdTech and AI
With support from the National Science Foundation, this project examines how existing education R&D frameworks apply to EdTech and AI-enabled tools. These technologies are already in use, continue to change over time, and may need to be studied through cycles of implementation, diagnosis, redesign, and testing.
One goal is to identify where existing frameworks work well and where they need to be extended. We are also developing a Field Guide that connects study purposes, designs, outcomes, and the kinds of claims different forms of evidence can support.
This work will include a convening of researchers, developers, evaluators, and education system leaders.
This work is led by Jessaca Spybrook, with Network members Erin Higgins and Vivian Wong, and Jeremy Roschelle of Digital Promise.
View the NSF Award