By Ben Johnston
Whenever a theory appears to you as the only possible one, take this as a sign that you have neither understood the theory nor the problem which it was intended to solve. – Karl Popper
Imagine returning from a doctor’s visit. The prognosis, acid reflux. The doctor gave you a prescription to Nexium.
You’re feeling good that you finally found a fix; the maker even advertises it as the “Healing Purple Pill,” and besides, it’s a popular drug with sales over $6 billion per year.
You consult Google to confirm the good news. What you learn is shocking.
Besides startling side-effects, you find out that Nexium only works for 1 in 25 people (Schork, 2015).
The drug was likely approved on the basis that it worked for some people better than sugar pills (placebo). This was based on analyzing a group average—it doesn’t tell you whether the specific treatment will work for you or any particular individual, but that’s the one thing you need to know!
You can ask a similar question for educational programs, like intervention. The research shows that, on average across a group, it works. Around two in three students receiving intervention get above the lower 30th percentile (Torgesen, 2000)–a much better percentage than most pharmaceutical drugs.
But until the intervention is underway, it’s difficult to know with accuracy which students will respond.
Remember back to the 1 in 25 chance that Nexium would fix your acid reflux? Well, there’s a push in medicine for better results.
It’s called precision (or personalized) medicine.
The idea behind precision medicine is logical—identify the traits of individuals that will determine a drug’s effectiveness. Then doctors could better understand what should be prescribed.
Intervention could learn from precision medicine—better understanding the individual traits that lead to better educational outcomes. It goes beyond merely knowing a student’s reading scores (that’s similar to knowing that you have heartburn). You want to know about the destination—where exactly a student can get to with the intervention.
Let’s examine the 30% of students in reading intervention who are still stuck in that lowest group—the ones who fail to respond to intervention. Despite receiving research-based reading interventions, one of every three students remain in the lowest 30th percentile.
Through intervention, if these students are not getting back to grade level reading, they wouldn’t be able participate and comprehend the general education curriculum. What then? You’d want some precision diagnostics to know what they need.
That’s what the diagnostic screener called uPAR (Universal Protocol for Accommodations in Reading) gives you.
For the students who likely won’t get to grade level with intervention, uPAR shows the text level they can comprehend with a reading accommodation.
This is not the type of prediction based on an average of student outcomes. It’s a precision diagnostic where you and your students find out exactly the text levels they can comprehend with an accommodation.
It’s often transformative for students. Many students learn that they can comprehend grade-level content while some students find out that they can read well above grade level by listening to text read aloud.
Kayle is one of the students who was transformed when her teacher brought in uPAR in 3rd grade. Despite Kayle reading at a beginning level (Lexile BR), she learned that through listening to passages read aloud, she could comprehend text above grade level.
And it was exactly what she needed.
Kayle would still receive specialized reading instruction, but now she had a way to read independently and handle grade-level passages while her reading skills improved.
Now extrapolate Kayle’s transformation across an entire school, district, or state. How many students could say that what they learned in one class period would change their lives?
The future isn’t in averages, it’s in precision.
Marketing Director, Don Johnston
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