GUEST BLOGGER: Mary Anne Mather, Using Data Facilitator & Social Media Liaison on Twitter & FaceBook

“Make data observations. Then generate possible explanations that inform next-steps to finding the best teaching and learning solutions.”
(from: Love, Nancy et al. The Data Coach’s Guide to Improving Learning for All Students, 2008.)

drawing of a figure with a question mark and thought bubbleData analysis is more effective, and more on-target for getting student achievement results, if a team of stakeholders first observe and list as many details as possible about what the data reveal, followed by making inferences about these observations, and then asking “why is this happening?” “what else do we need to know to be sure?”.

Infer/Question is the fourth stage in a team-based, 4-phase dialogue process* that guides deep discussion toward deriving accurate meaning from performance data. (See more information about Step 1: Predict, Step 2: Go Visual, and Step 3: Make Observations.)

These action steps will help you and your data team share inferences about the story the data reveal—inferences that will inform important next-steps toward identifying a valid student learning problem and its true causes.

Action Steps

• Infer/Question: After capturing a complete set of observations drawn from analysis of aggregate, disaggregate, strand, or item data, begin to generate possible explanations for what you observe. It’s important to understand what is happening, and why, before moving to solutions. Think about these questions:

–What inferences and explanations can we draw about our observations?
–What questions do we need to consider?
–What tentative conclusions might we draw?
–What additional data might we explore to verify our explanations?

Begin your inferences with phrases like, “I wonder if…, Could this situation exists because…, I would like to know if…, We really should explore…, Agroup of teachers looking at charts, pointing at something, discussing question I have is…”

Inference statements link back to your data-informed observations and might look like this:

We really should explore whether district scores improved more than our school scores because some schools are on a year-round schedule.”

I wonder if mathematical reasoning is not emphasized enough in our curriculum.”

I’m surprised that our regular education and special education students had the same difficulty with the vocabulary used in this open response science question.“

“Our observations of disaggregate data indicate a high mobility rate. A question I have is…do we have programs for kids who come to our school in the middle of the year to help them catch up?”

• Now, set out to find the answers to your questions or confirm your inferences by identifying additional data and indicators you can collect. If you began by looking at aggregate data, start to drill down and look at disaggregate, strand, and item data. Or consider a look at common grade-level assessments, student work, or even survey data. Are you observing new things? Does the new data inform your inferences? Does it change your thinking?

After your data team successfully completes the making inferences phase of the data-driven dialog process, there is one important last step before adjourning the meeting. Ask yourselves,

What are the implications of what we just learned?

What action do we need to take next?

Who needs to know?

Once you’ve collected additional data to clarify and answer your inferences, subsequent meetings will focus on using it to pinpoint very specific student learning problems and their causes—bringing you one step closer to finding solutions that can effectively impact student achievement.

Making inferences and generating questions that will be verified and answered before finding solutions is a classic example of the “go slow to go fast” strategy. It gets you on track for making sure the problem you are solving is one you actually have!

*The four-phase dialog process is adapted from Wellman, B., & Lipton, L., 2004. Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: MiraVia LLC. Used with permission.