Building a stronger education evidence system

A cross-sector group of researchers, evaluators, and evidence leaders.

We work to make education evidence more credible, interpretable, and useful.

Who we are

Network expertise

The Network spans the main sources of failure in evidence use — from study design to the decision contexts where findings are acted on.

1

Methods and design

Causal inference; design and power in field settings; effect heterogeneity and generalizability — the issues that sit underneath many claims about "what works," "for whom," and "where."

2

Measurement and modern data

Psychometrics, measurement, and data science/AI — including the opportunities and risks of platform data and rapidly evolving measures.

3

Evaluation and evidence standards

Large-scale evaluations; evidence synthesis; and roles that shape field standards for credible evidence.

4

Evidence use in systems

Procurement and contracting; cost and cost-effectiveness; and how implementation constraints shape what evidence can reasonably be asked to do.

Context

Why This Work Now

Education evidence is now generated and used through more channels, on shorter timelines, and with less shared infrastructure. At the same time, leaders are asked to make setting-specific decisions from evidence that was often produced for other purposes.

As federal support for education R&D becomes less certain and schools continue to change through new technologies and evolving practices, these challenges become more consequential.

Featured Work

Necessary but Not Sufficient

Report 1

Necessary but Not Sufficient: Building a Stronger Education Evidence System

This report argues that rigorous studies, shared causal standards, and research-practice partnerships were important advances in education research, but not enough on their own to create a public evidence system that leaders can use. It identifies recurring vulnerabilities in the current system and outlines recommendations for strengthening the next phase of evidence generation, interpretation, and use.

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Methods Principles Report

Report 2

Methods Principles Report

This report helps education leaders interpret claims about program effectiveness with greater confidence. It presents ten principles and an Evidence Receipt for judging what a study shows, what kind of claim it can support, and what still needs to be learned before adopting, piloting, or scaling a program.

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Commentary

Related Commentary

Op-Ed

Why More Rigorous Studies Alone Are Not Enough

An op-ed on the main findings of the Network's two reports: one on the education evidence system, and one on interpreting evidence in decision settings.

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Op-Ed

From Compliance to Learning: Rethinking the Federal Role in Education Evidence

How federal policy can better support state learning, evidence use, and decision-making.

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Our focus

From studies to decisions

The Network's work addresses three recurring challenges in education evidence: how the system is built, how evidence should be interpreted, and how findings are translated into claims and then into action.

1

Building a stronger evidence system

How the education evidence system is organized, where it is under strain, and what stronger public infrastructure would require.

2

Interpreting evidence for decisions

How leaders can judge what evidence supports, what it does not, and what still needs to be learned.

3

Tools for using evidence well

Practical resources, including the Evidence Receipt and worked examples of how evidence can be used more responsibly in decision settings.

What we do

Our impact priorities

1

Comparability

Define clearer standards so claims can be interpreted across settings, student groups, and implementation conditions.

2

Accountability

Improve how claims are documented, bounded, and reviewed over time using practical tools like Evidence Receipts.

3

Updating

Make evidence guidance easier to refresh as products, measures, and contexts evolve quickly.

4

Decision Use

Support funders and field leaders making real decisions under budget, timing, and implementation constraints.