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Using Data Solutions


Data Tip #5: "Make Inferences and Question Your Data's Story"

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

Before generating solutions, be certain that you fully understand the problem. As a data team, take the time to verify what learning problems are revealed in your data—and why—before suggesting solutions. After making observations about the data and listing details about what you see in it, draw inferences about why these observations are revealed. Ask yourselves, “Why are we seeing this result?” and/or “What else do we need to know to be sure of this observation?” Making inferences and asking questions 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!

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

The following action steps will help you and your data team share inferences about the story your data are telling. These inferences will inform important next steps toward pinpointing a valid student learning problem and its true cause.

Action Steps

  • After capturing observations about your data, make inferences and question your data’s story. Begin to generate possible explanations for what you observe by considering 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 such as: “I wonder if…, Might this situation exist because…, I would like to know if…, We really should explore…, A question I have is…

Inference statements link back to the observations you made about your data, and might look like the following:

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. Do we have programs for kids who come to our school in the middle of the year to help them catch up?

  • Next, work to find the answers to your questions or to confirm your inferences by identifying additional data and indicators you can collect. For example, drill down and look at disaggregate, strand, or item data. Or consider analyzing common grade-level assessments, student work, or even survey data. Does the new data confirm your inferences? Does it change your thinking?
  • Lastly, as your team completes the four-phase dialogue process for analyzing data, consider these three questions to help you define next steps:
    --What are the implications of what we just learned?
    --What actions do we need to take next?
    --Who needs to know?

Now that your data team has clarified inferences about your data, you can focus on using this information to pinpoint very specific student learning problems and generating solutions that can truly impact your students’ achievement.

Written by: Diana Nunnaley, Director
Mary Anne Mather, Facilitator
TERC's Using Data