The Data Truck is Running Over Education

The data truck continues to run over education. I used to think that schools got hit by the data truck, but that didn't speak to the true impact that data is having on learning. I'm now convinced that the impact is so great that it feels much more like this video. 

 

First the volume of data is too great to consume. It comes in floods through individual, classrooms, school, district data, and it also comes from the volume of qualitative data that we are consuming each day to make the multitude of precise decisions about what is best for kids and adults. This quickly leads to data fatigue and as a result good data that can shape better results quickly misses its target audience with its intended message. When we are overstimulated with data, we move into survival mode, and there is rarely transformative data uncovered when we are in this state of survival. 

 

If we are lucky enough to regulate the flow of data, what truly allows us to find the right mix? Thinking silos and echo chambers emerge when we internalize a consistent mix of data. It is natural to find a comfortable mix of data that speaks into our beliefs. It is easy to find articles, data points about students' learning, and reinforcing words about how we believe that education should be. This easy data almost attaches itself to us without intention or thought. How do we avoid the trap of sterilizing our mix of data as we look to regulate the flow of data simultaneously?

 

Finally, is there any reason to continue to produce data visualizations that limit understanding for the audiences that we serve. By this, I mean that the power of data transparency (in that it serves to tell the story of a district, reinforces its mission, and generates greater connections and collaboration) comes only when a broad, diverse audience can use the data for conversations that can lead to change. Data is often seen in formats that limit understand, suppress emerging opportunities for change, and lead different parties to different conclusion based on a failed common language around the data. 

 

What can we do to stop getting run over by the data truck? How can we construct systems that optimize the volume of data that is collected, distributed, and discussed? In what ways can we introduce fresh data flows into our work without upsetting the balance in volume? Are there ways to display our most important data to promote transparency and conversations?