Doug Lillydahl

Doug Lillydahl is director of communication arts at Adlai E. Stevenson High School in Illinois. He guides literacy interventions and staff development, oversees ELA assessment, and supports curricular and instructional evolution.

Helping Data Analysis Take Root

Data analysis.

It is a concept that spurs rich reflection from many, but eyerolls or quizzical looks from too many. Some teachers feel that the data analysis they do is just to satisfy someone else’s administrative need or to show that, yet again, the same set of kids in their class struggled. This feels like barren ground.

But the reality is that there is no better way to learn and grow as you look at student results —the harvest of your teaching labor—than to use student performances to approach two key PLC questions:

Without such analysis of student thinking and moves, we can’t design the best next step for each learner.

So, let’s prepare a few seeds of thought for spring planting with our teams:

Seed 1: Data = Student Work

While some teachers I speak with believe that student data is simply meaningless “numbers,” they need to understand that we are actually looking at patterns of student thinking. Each “number” comes from a student performance—and by looking at those performances closely we can see what our students have learned from us about how to tackle a task. Please note that while numbers give the overview, until a team progresses to looking at actual samples of the student work representing the numbers, they will only guess at what students need to learn next.

Seed 2: Everyone Has a Stake

When a team sits down to look at data, every person at the table should have a stake in the work being examined. No one should sit at the edge of the conversation and say, “Well, this doesn’t really concern me.” If one teacher has students draw a model of photosynthesis and another has students answer four multiple choice questions regarding the same topic, the teachers are assessing different skills and content. Therefore, the assessment should be a common assessment, and each teacher should contribute to the data gathered before the group. Like a team of horses plowing the fields, they must pull together.

Seed 3: Patterns of Student Thinking and “Moves”

Examining representative students’ work allows your team to understand what instruction has led students to think and do. For instance, if students have constructed a short paragraph arguing for or against America’s involvement in the Bay of Pigs invasion from history class, a savvy team would look at an example of a “mastery,” “approaching mastery,” and an “early development” answer from each teacher’s class.

  • What moves have these students made in common? (For instance, your “approaching mastery” students may show a pattern of including an attempted claim statement, using an article to draw evidence, and often featuring three pieces of evidence to support their claims.)
  • What misconceptions or areas for growth do they reveal? (For instance, is a pattern that the claim is often not debatable? Is the evidence sometimes irrelevant? Why? What have they overlooked?)

Seed 4: The Point is Teacher Learning

In a professional learning community the first learners are the teachers. Now that specific student misunderstandings or areas for growth have been revealed, it is time for teachers to collaboratively determine an instructional response. Although it is often not faster to create new, focused responses to a few common issues raised, the shared creation process results in each teacher engaging in the creation, explanation, review/defense, and implementation of new methods.

To tend any new lesson idea, individual teachers must understand how and why it was created—and therefore understand how instruction like it could be transplanted into future lessons they design. This is rich soil for professional learning.

Helping Ideas Take Root

Planting these seeds of ideas is an essential first step toward boosting teacher professional learning in your school. But that can’t be all, right? Yes, you must pay attention to their growth and support the young seedlings until the culture of your school has made them firmly rooted concepts. As you might guess, your garden is not really ever “done,” and there are tools for improved practice that you might never have imagined.

In my own school, Adlai Stevenson High School of Lincolnshire, IL, we have found that these seeds described above especially thrive when there is a focus on student performances calibrated around key standards. This model of proficiency-based grading leads to additional focus and learning with teacher teams and students and is described in detail in our new work Proficiency-Based Grading in the Content Areas.

Happy gardening!

Comments

Carey Girardi

As a teacher with many many years of interpreting data, I agree data drives instruction. I agree we should use the data we receive from our students to make sure we understand they whys and hows of their learning. It reflects our classroom and should help us self reflect as teachers and make positive changes to ensure student success. I have a couple of questions however after reading your post.
1. Can you clarify types of assessments that we should use more often to drive instruction? I ask because I believe formative assessment data should be a resource more often than standardized testing. I find that formative assessments often capture a child’s knowledge more often than summative assessments or even state testing.

2. How do we get some teachers to change their mindset about data analysis? How do
we teach teachers to have a growth mindset during the data analysis process? I do think so many of us get bogged down and it becomes yet another thing we have to do for accountability or to have evidence that we are effective teachers. There are many times that data analysis isn’t used for collaboration or to meet the needs of students. How do we change the reputation of data analysis?

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Kelsey Rippe

Doug, thank you for a refreshing view on data analysis. You're right, it's extremely critical but the value of it has been lost. As a newer teacher, I have seen experienced teachers that need a new mindset regarding the importance of data analysis and how to use it in a way that is valuable to them and their classroom. I myself felt like I did not have much training in analyzing data which lead me to doing more research on my own and asking many clarifying questions to my administration and building coach.

I found seed 3 valuable. I can collect data and create common assessments in my sleep, but actually interpreting it and knowing what to do with it was a struggle for me during my first year of teaching. I've been learning a lot lately about the value of studying students' patterns of thinking and addressing student misconceptions. Thanks for insight, and I can't wait to share this article with my team.

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