A couple days back, the Washington Post published a thoughtful op-ed by a third grade teacher railing against data walls. Though there are many variations on the data wall, the version the author describes is a popular one. Essentially, a teacher has a classroom chart showing his or her students' data. The goals, ostensibly, are to motivate students through friendly competition and keep progress front and center.
Posting student information for motivational purposes is not a new practice; it’s not even the Post’s first op-ed on the practice. Both my wife and I saw it firsthand during our years in the classroom when we called them things like progress walls and behavior charts. Today, we categorize the strategy under the banner of data-driven instruction and data-rich decision making, but it is no different and no less pernicious.
What is new is the naming, which capitalizes on data-mania. In a sense, it's not a bad description. Data is involved. But it is a bad use of data not only because of the shaming the author describes (which must not be understated) but because it misses the point of data, threatening to poison the well of what could be a transformative tool.
There Are No Answers In Your Data
There are no answers in data, only better questions. The answers come from the meaning making we bring when we analyze and interrogate the data. Rich data sets make this quest easier and more effective - reducing mental friction, keeping us on the right path, honing our questions until we get finally, painfully to the right ones. To borrow from Wiggins and McTighe, data can help us get to the essential questions.
Put another way, data is mute until we give it voice, and that voice should speak first and perhaps always in the interrogative - why. The Five Why process can be particularly helpful.
What is the "why" in the data wall? This sort of student data can drive us towards many essential questions - why did one student perform better, what can we do as a school to help, what supports would work best and when, how should I differentiated instruction and for whom? But does the publishing of academic results on a poster help drive us to these questions and, more importantly, to the answers beyond? Or does it merely turn academic data into yet another competition dividing kids into winners and losers?
Reframing the Conversation
By misusing and misunderstanding data-driven instruction, we risk ultimately turning the educational community off to its power. Without question, data can be a force of immense good, helping us achieve the kind of learning-centered teaching many of us have long dreamed of, if we learn how to harness it in the right ways.
So what can be done? A starting point is to ensure that whenever we use data, we start with two "why" questions:
- Why am I seeing these patterns and trends?
- Why do I care?
The first asks you to interrogate at a deeper level, to engage in meaning making, not to obsess with a single data point. The second asks you to root yourself on things that matter, to ensure that the data relates to your ultimate goals and mission, not to use data as a bludgeon but as an opening door.
These practices won't necessarily end all bad data use, but they will help us begin to think about data in a new and healthier way. We are data's voice; let's give it one worth hearing.