This is my final installment of my Digging into Data series, so I’m going to give an example of how this data has helped me identify struggling students, and how it helps me differentiate for all of students.
If you haven’t already read the other posts in the series, I encourage you to do so, even if it’s after reading this one. The whole process is invaluable, and it’s worth the time to dig around for student data.
Identifying struggling students
One of my students, who I’ll call Adam, was just like all of the others in the beginning of the year in that I had no idea who he was or his abilities in reading and writing. As I’ve mentioned in this series, it’s always my job to learn as much as I can about my students so that I can meet their educational needs. During conversations in class, he usually participated (although he was a bit social), and was overall a pleasant student. The only reason why he was initially on my radar was the fact that he has an Individualized Education Plan (IEP), but I’m careful to not jump to conclusions with that fact.
I ultimately became concerned when I was grading Adam’s pre-assessment essay. I noticed that 1) he barely wrote three paragraphs (they were basically three really long run-on sentences), 2) his writing is illegible, and 3) I wasn’t sure he really understood the text or the prompt. Here’s his submission:
I’m accustomed to students with low writing skills, and I could tell that Adam was a struggling writer, but I wanted to know more about what was going on. Luckily I’m a data collector and had it handy! I had all of my students sorted with their Lexile levels, pre-assessment scores, program enrollment, and English Proficiency. Here’s where he fell:
Not only did he earn a score of 1 for all four standards I assessed, his Lexile score is 355, which puts him at first grade level. This definitely set me on a mission to uncover more about him so that I could figure out if he was properly placed.
I looked closely at his IEP, and discovered that he
“meets eligibility for Other Health Impairment (ADHD) which is a chronic or acute health condition that adversely impacts his educational performance. Adam demostrated a severe discrepancy between intellectual ability and processing speed that impacts achievement in the following academic area: math calculations. The discrepancy is due to a disorder in the area of attention.”
Really? Only in the area of math? This student was obviously struggling in multiple areas that need to be addressed! I met with my coteacher, Kristie Green-Bannister, to find out if there was something I’d missed. She informed me that he was already on her radar because she felt that he was improperly placed. His case carrier had already recommended that he be switched into our program that offers more structure and even smaller class sizes for our students that struggle the most.
Who else is struggling?
While I wasn’t the one to have the EUREKA! moment, I wanted to see if there were any other potential red flags. Looking at the spreadsheet above, it’s easy to see that a large number of my IEP students are in the same boat as Adam. That doesn’t necessarily mean that all of them will be moved, in fact, most of them will stay, and Mrs. Green-Bannister and I will use our expert skills to help bring those students up to as close to grade level as possible. But had I not had this data, I might have made some incorrect assumptions about these students regarding their low performance.
Can you imagine a physician prescribing medication or giving medical advice in the absence of a patient’s medical history? While this does happen sometimes, they can give more comprehensive and appropriate care when they have data from bloodwork, x-rays, MRIs, previous medical conditions and procedures, etc. A skilled physician wouldn’t just chalk up obesity to laziness – they look to see if there are underlying conditions or family history that might be contributing factors.
This brings up an important point about the importance of collecting student data – it’s easy to peg a kid as a slacker, troublemaker, or lump on a log. We know from countless stories and data that many of these students act out as a coping mechanism, particularly when they struggle in a subject. Armed with data, you can do even more as a teacher to help those students. Without it, you may misdiagnose them.
What to do with your data
If you’ve been following along with me to this point, here’s how you can best use your data:
- Put all of your data in a spreadsheet. I used Conditional Formatting to create a heat map, which gives me more of a visual of where my students are
- Pinpoint students that will need more help and attention. Ask yourself:
- Are they already identified in a program?
- How are they performing orally and with written work? Is their poor performance a result of lack of motivation, or are there underlying factors at play?
- If they are truly struggling with grade level tasks, what is the process for having them tested? Who do you speak to?
- Plan to differentiate based on your students’ data. This may mean homogenous groups for small-group and targeted instruction, and hetergenous groups for the rest of the time. I create different graphic organizers and scaffolds for my students that struggle the most, while I challenge my strongest students to complete assignments without any scaffolds.
- Continue to track progress on specific skills throughout the grading period. It’s almost impossible to know how much your students are progressing if you don’t have a pre and post-assessment!
I hope you’ve found some value in this series and can incorporate data collection into your educational practice. Please leave a comment below and let me know how it has or hasn’t worked for you!
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