Steven Singer writes here about the mechanistic, anti-child implicationsand consequences of data-driven Instruction. He identifies six issues. I offer only the first of these problems. To learn about the other five, open the link.

He writes:

No teacher should ever be data-driven. Every teacher should be student-driven.

You should base your instruction around what’s best for your students – what motivates them, inspires them, gets them ready and interested in learning.

To be sure, you should be data-informed – you should know what their test scores are and that should factor into your lessons in one way or another – but test scores should not be the driving force behind your instruction, especially since standardized test scores are incredibly poor indicators of student knowledge.

No one really believes that the Be All and End All of student knowledge is children’s ability to choose the “correct” answer on a multiple-choice test. No one sits back in awe at Albert Einstein’s test scores – it’s what he was able to do with the knowledge he had. Indeed, his understanding of the universe could not be adequately captured in a simple choice between four possible answers.

As I see it, there are at least six major problems with this dependence on student data at the heart of the data-driven movement.

So without further ado, here is a sextet of major flaws in the theory of data-driven instruction:

The Data is Unscientific

When we talk about student data, we’re talking about statistics. We’re talking about a quantity computed from a sample or a random variable.

As such, it needs to be a measure of something specific, something clearly defined and agreed upon.

For instance, you could measure the brightness of a star or its position in space.

However, when dealing with student knowledge, we leave the hard sciences and enter the realm of psychology. The focus of study is not and cannot be as clearly defined. What, after all, are we measuring when we give a standardized test? What are the units we’re using to measure it?

We find ourselves in the same sticky situation as those trying to measure intelligence. What is this thing we’re trying to quantify and how exactly do we go about quantifying it?

The result is intensely subjective. Sure we throw numbers up there to represent our assumptions, but – make no mistake – these are not the same numbers that measure distances on the globe or the density of an atomic nucleus.

These are approximations made up by human beings to justify deeply subjective assumptions about human nature.

It looks like statistics. It looks like math. But it is neither of these things.

We just get tricked by the numbers. We see them and mistake what we’re seeing for the hard sciences. We fall victim to the cult of numerology. That’s what data-driven instruction really is – the deepest type of mysticism passed off as science.

The idea that high stakes test scores are the best way to assess learning and that instruction should center around them is essentially a faith based initiative.

Before we can go any further, we must understand that.