How We Funded Assumptions for Decades (Discussion) | The Paradigm Shift in Federal Education Grants

What happens when you fund programs for decades without ever asking if they work?

In this conversation, we explore the massive paradigm shift in federal education grants—from serving "at-risk" students based on demographics to requiring measurable outcomes based on data. For 25 years, program evaluators watched this transition unfold from inside the system, and what they discovered was startling.

Programs designed to help struggling students were filled with students who were already succeeding. Staff selected students based on the bus they rode, their clothing, or whether their names sounded like they belonged to minority groups—and assumed this was the same as using academic data. When evaluators reported that students already at grade level didn't need remedial services, program staff would ask: "Are you saying these students aren't poor?"

The federal government's own assessment found that almost half of Department of Education grant programs couldn't demonstrate whether they were effective. Some were shut down entirely.
We discuss the "lotta chicken" problem—how people make decisions without knowing what can actually be computed—and what happened to high-achieving students who were placed in programs that damaged rather than helped them.

This isn't about blaming well-meaning educators. It's about understanding how assumptions became systems, and why the shift to data-driven decisions has been so difficult to complete.

Based on research by Janet Johnson