When presented with your school or classroom data, it can be tempting to jump immediately into analysis, or even right to problem solving, without stepping back to ensure you are clear on the data’s technical meaning. This can be dangerous because it may lead us down a path that is not actually founded in the evidence.
To avoid solving the wrong problem, be disciplined about how you approach a data visual or data set. Try following these steps to form good habits—
|1) Read aloud the title and subtitles of the visual or data set.||“This is Student Growth by Subgroup for our school’s 5th grade math results as of the second diagnostic window in 2020-21.”||Is your audience still with you, or do they already look confused? Do you need to explain any of the terms in the title before moving on?|
|2) Ask a volunteer to read aloud the labels on the page (e.g., x- and y- axes, units of measurement), as you point to them.||“The unit of measurement is Norm Curve Equivalents on a scale of -10 to 10. Bars pushing left mean on average students are losing ground relative to peers. Bars pushing right mean on average students are gaining ground relative to their peers or learning more than a year’s material in a year’s time.”||Is the audience still with you, or do they now look confused? Do you need to explain any of the terms in the labels before moving on?|
|3) If there is a key, point to each symbol and define it aloud (or ask someone else to, depending on time).||“An exclamation point means the same metric for the prior year. In other words, the exclamation point represents subgroup growth for 5th grade math for students enrolled in 2019-20.”||Is the audience still with you, or do they now look confused? Do you need to explain any of the terms in the key before moving on?|
|4) Model how to read a couple of data points technically and precisely.||“Overall, our 5th grade students are gaining ground relative to their national peers at a rate of 2.0 NCEs. Students with disabilities are gaining ground at twice that rate at 4.0 NCEs.”||Ask if there are questions about the mechanics of reading the data. If so, ask if another participant can help to clarify. If no one can, you may need to stay in the orientation a bit longer.|
|5) Establish purpose by asking a volunteer to share why we care about these outcomes.||“Because our school has a shared value that ALL students demonstrate a year’s growth in a year’s time. This visual helps us know for which student groups we’re meeting this goal, and for which groups we’re missing the mark.”||There’s usually not a single correct answer to this question. Affirm multiple points of view that speak to personal and corporate accountability to students, affirmation of current work, or identification of calls to action.|
Now that you have a common language and understanding among your team, it is safe to move into analysis. One way to approach this phase is to ask your audience to look for and share out patterns in the data that they want to celebrate and patterns they hope to change. Continue encouraging them to speak technically and precisely, using the numbers and labels on the page to describe the patterns.
When you have general consensus on the important patterns to be drawn from the data, then delve into your problem-solving phase. Why do we believe the patterns exist? What is going well that we need to protect? What do we need to change to see improvements? What resources do we need in order to fulfill these commitments?
Affirming successes and identifying challenges are the primary reasons for looking at data and arguably the more interesting part! But if we forego the data orientation, purpose setting, and analysis phases, we may be limiting our solutions to those based on biases, assumptions, and fears.
Allowing the evidence to speak first can free us to think outside our traditional patterns and challenge preconceived notions.
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