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Updated: Oct 16, 2022

Alright, teachers, let's chat about data! We all know that we have to collect data, but the most important thing is that we are using data to guide our instruction.

So what does using the data mean? It's more than just looking at it. I know this seems obvious, but when I go into classrooms with teachers, I see students working on the same skill in the same format with minimal progress. Sometimes, because we know how important repetition is, we think that if we keep repeating the same thing, eventually, it will stick! But that's not the reality for students, and honestly, it makes them less motivated to learn because they get bored. Repetition is important, but our instructional toolbox needs to include more than that. But that's a post for another day.

When providing instruction, we need to use the data we collect to make instructional changes. Here are some decisions you may make when analyzing data.

Analyzing data in special education


1. Move on to the next objective.

Data will show us when mastery is met, which means it is time to move on to the next lesson, objective, or benchmark. Make sure to explicitly teach your paraprofessionals to watch for this so they don't continue teaching the same objective long after it's been mastered.

2. Go back to a previous lesson.

If the data shows you that a student has regressed in previously mastered skills, you may need to go back to a previous lesson, objective, or benchmark and re-teach. One thing you can do to prevent this is to think about adding the mastered skills into your daily routine to ensure maintenance. If a student is really struggling and progress is minimal, they may be missing a prerequisite skill. You may need to re-teach the pre-requisite skill until the concept is solidified and they are ready to move on.

3. Add scaffolding and support.

If data shows minimal progress, you may have jumped too far into the next skill or faded supports too quickly. You may need to add visuals, an additional prompt, or an alternate response (such as a receptive response vs. an expressive response, some natural cues, or more prompts) to help the student succeed. Then, make sure you fade those supports out.

For example, I observed a student working on sequencing numbers 1-10. They had mastered matching the numbers on a number line, so the teacher faded the matching numbers out, and the student was now expected to just sequence on the number line. But they were making minimal progress and still continually missing the same numbers. So, we added a few of the matching numbers back in and then faded just one at a time as they were improving. Make sure not to jump too far when fading prompts!

4. Remove scaffolding and support.

In the example I shared above, it would have been super helpful for the student if we had started by just fading a few of the numbers and continued to remove them as the data showed progress. Ideally, we want to remove supports instead of adding them in, so be extra cautious about the steps you take in your scaffolding.

5. Add behavior interventions.

Yes, we know that behavior interferes with learning. If your data shows that a student is struggling, it may be helpful to add in some behavior support. For example, I recently talked to a teacher who said that they could not get through an academic session. Next to the data, she continually was writing, "noncompliance." This is a red flag that we need to support the student's behavior needs before we can expect academic progress. Try some basic behavior interventions, such as a visual schedule, token board, breaks, etc., to help the student be engaged during learning time.

6. Change your instructional strategy.

Data should guide your instruction. If the student is not making progress, it's time to change our instructional strategy. Try a new approach, like direct instruction, different engagement strategies, increase modeling and guided practice, graphic organizers, etc. Don't do the same thing for weeks or months and expect the student to "get it".

free analyzing data checklist

I hope these tips help guide your decision-making when you are analyzing student data. Grab a free checklist in the resource library to put in your data binders to help you when analyzing data!

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