Sarah Schuhl, a consultant specializing in mathematics, has been a secondary mathematics teacher, high school instructional coach, and K–12 mathematics specialist for nearly 20 years.

Doing It or Doing It Well? Using Data for Learning

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About five years ago, I decided that it was time to get in shape. An infomercial caught my eye and I found myself ordering a video program, weights, bands, nutritional guide, and pull-up bar while waiting impatiently for my new life to begin. 

After the second day of inserting a DVD into my computer and following along, I realized this commitment was not going to be easy.  With persistence, in about a month, I felt I was actually making progress and able to do the exercises.  However, just as I began to swell with pride, I caught a glimpse of myself in the fireplace glass and gasped.  While I thought I looked like the trainers on the DVD, I suddenly realized I wasn’t even close! 

Was I “doing” the exercise? Yes! Was I doing it well? Not so much.

I was reminded of this moment recently when working with a collaborative team analyzing data. They had already been working for about a year to address the four critical questions of a PLC.  With commitment and persistence to improving student learning, the collaborative team embodies a “can do” spirit, even when the work seemed difficult.

Each teacher on the fourth grade elementary team brought data from a recent assessment from his or her own class.  They stared at their own clipboards and looked at the number of red, yellow, and green cells they had shaded for students not proficient, close to proficient, and proficient.  They then determined some students didn’t learn and some did, guessed at a few reasons for why, and wanted to talk about planning the next unit. 

Were they looking at data to determine if students had learned? Yes!  Were they looking at data to analyze student strengths and weaknesses and determine a collective, specific response to student learning – the purpose of looking at data? Not so much.


5 Steps to Doing Data Well

Much like exercise, with practice, collaborative teams become more effective and efficient analyzing and responding to data.  I have found teams are most effective looking at data when they:

  1. Start with a common assessment: Collaboratively plan the common assessment that will be used to analyze and respond to student learning.  During the planning (ideally, before the unit ever begins), teachers chunk the assessment items by essential learning standards, determine rigor of the items, make common scoring agreements, and determine the level of student work needed for varying proficiency levels.
  2. First look at an overview of the data: Teachers determine the percentage of students proficient by target and gather their data onto one document for all members of the collaborative team to view when discussing the data (Google Docs is one possibility). Looking at this initial picture of the data allows teachers to address areas of strength and areas to grow related to student learning across the team and within each classroom. They also can discuss any surprises in the data and make sense of the student learning in each classroom compared to the whole grade level or course.


    Target 1

    Target 2

    Target 3

    Target 4

    Teacher A





    Teacher B





    Teacher C





    Team Total





  3. Identify student by standard proficiency: Once there is a common understanding of student learning, it is then critical to acknowledge and discuss which students are proficient and not proficient, or proficient, close to proficient, and far from proficient by target.  Elementary teachers often do this by listing the names of students in each category by target.  Secondary teachers often highlight class rosters green, yellow, and red to see the name and number of students in each classification.
  4. Identify trends and patterns in student work from the highest to the lowest performers: Finally, teams identify first, the trends in student thinking and work that caused a student to be proficient.  What did these students do in their evidence of learning to set their work apart from the others?  Next, they address the evidence of the work shown by students close to proficiency and compare and contrast that student work to the work of proficient students.  Is there something to target that might be a catalyst to move students close to proficient into the proficient category?  Last, the team looks at the work of those students far from demonstrating proficiency and continue the process, looking at what might be targeted in future learning. 
  5. Make re-engagement/enrichment plans: From these discussions, teachers can make a collaborative plan to re-engage students in learning.  Does the team need to stop, shuffle students, and plan a full lesson for each group of students?  Does the team need to address learning by spending 10 – 15 minutes three days a week during core with specific activities all students will have opportunities across the team to learn from?  Does the team need to plan for focused and targeted Tier 2 or Tier 3 intervention?  Also, how are students part of the plan by identifying what they learned and what they still need to learn in this process? 

There is a sample data analysis protocol under Tools and Resources developed by Rick DuFour on the All Things PLC website your team may want to use when analyzing data.  You can find it using the following link:

By analyzing data as a collaborative team and collectively responding to the learning of students, collaborative teams learn and students learn – a win for both. More than a checklist for having “done it” is the need to do it well to impact student learning.

While I may never have perfected the exercise routines, I know with practice my skills and results improved.  I encourage you to determine a protocol for analyzing student data so your team answers “Are we analyzing and responding to student data to improve student learning? “ with a resounding “Yes!” 

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