Emily Talmage teaches in Maine. She recounts in this post her first encounters with data-driven instruction, both in public schools and in a no-excuses charter school. She has come to understand that the data are not meaningful and that they are inherently at odds with the individual nature of the child, who cannot be defined by numbers. She suspects that someone will use whatever data her classes produces to make money. Better to look at the children in front of you, not the data that reduces them to digits on a spreadsheet.

A few weeks into my first year as a teacher, my colleagues and I met for our first “data team” meeting of the year.

Our principal had printed results from the previous year’s standardized tests and given a copy to each of us.

“Take a few minutes to look at the data, and then we’ll decide what inferences we can make from it,” he instructed.

He had a book with him – something with “data coaches” in the title – and was following a protocol laid out within.

I looked at the graphs, then – smiling – at my principal.

Surely he was joking.

At that point in the year, I had only five students – four third graders and one fifth grader – in a self-contained special ed classroom for kids with severe emotional disturbances. They were children who had experienced extreme trauma and abuse, and who struggled to get through a day at school without an attack of panic, rage, or violence.

All five had gotten one’s – the lowest possible score – on the previous year’s math and reading tests.

“Ms. Kennedy,” our principal said flatly, “what inferences can you make from this data? This is how we will be planning our instruction for the year.”

It was my first time experiencing the absurdity of data-driven education, but far from my last.

Then she worked at a no-excuses charter school in Brooklyn, and remembering it still gives her nightmares.

Now teaching in Maine, she is swimming–or drowning–in data, and she learns nothing from it that she didn’t know already.

Yet somewhere, she assumes, someone is figuring out how to monetize the data, even though it is utterly meaningless.