When I first start working with a school, I usually come to find that the bulk of data analysis happens in Professional Learning Communities through data dives once a quarter. Teachers will bring their data to the meeting and a protocol will be used to analyze said data, and hopefully, if there is time, next steps will be made.
And when I ask why so much time is spent analyzing in these meetings, leaders give varying responses, such as,
This is how we’ve always done it.
When else are they supposed to analyze data?
They need time to look at the data so that they can do something with it.
Because before they develop next steps, they have to analyze.
I agree with some of it. Yes, teachers have to analyze data and yes, they need time to analyze it. But, why can’t the analysis happen in the classroom during lessons?
The Power of Data Analysis in the Classroom
If teachers are collecting and tracking the Right Data, then analysis should happen quickly because time does not have to be spent sorting through notes and observations and anecdotes that are documented in different places and different notebooks.
Instead, data analysis should be concise and to occur in the classroom. In order for that to happen, three things must be established and identified within a single lesson:
Standard or objective or skill being measured. Even if children are in the library selecting a book, the goal would be for students to select a library book of interest. There is always a standard/objective/skill for students to accomplish at the end of every lesson.
Names of students who are proficient, almost proficient, not yet proficient in number 1. This information comes from a dedicated Check for Understanding.
The teacher's response and remediation for proficiency levels identified in number 2. Small-groups for students who are not yet proficient, is one form of response.
A look inside a data-driven classroom
Below is an example of how a 9th grade math teacher collected and analyzed data in her lesson to gauge student understanding of inequalities.
Objective: Inequalities, solve for X,
Clear indication of who got it and who didn’t is provided.
Students who were identified as not proficient were pulled into a small group for a reteach before moving into the next problem.
From this very simple tracking of a Check for Understanding, the teacher has a clear understanding of individual student proficiency in inequalities.
So, let’s dive deeper.
Specifically, out of 22 students, the teacher now knows that
7 students are proficient in inequalities
3 students didn’t provide work
2 students didn’t show a graph
2 students didn’t provide the negative
Over the course of the month, the teacher continued to check for understanding of inequalities because it is a prerequisite skill necessary for understanding new skills taught in the class. As a result, she was able to identify individual student growth from 01/30 to 02/10 and determine any student trends. As a result of this analysis, these numbers were presented during her PLC meeting and the majority of the PLC time was spent determining next steps for remediation.
By embracing what I term micro data and analysis within the classroom, a revolutionized approach to PLC analysis can be exercised. The right data collection systems in the classroom allow teachers to seamlessly integrate data analysis into their daily teaching routines. I speak more about micro data collection in this article,
Ready to enhance PLC data conversations in your school? I created a PLC insight survey and video to assess your PLC’s understanding of data. Survey results will allow you to provide targeted support and align your PLC's for data-driven conversations. Get it here!