Confirmation bias and implicit bias pack a double punch in shaping how we use data in our professional learning communities and classrooms to serve our kids. If we are looking at the wrong data or interpreting it via implicit blinders, the science of our data-driven process isn’t going to save us from false assumptions.
This story is a metaphor for a common scenario in our classrooms: those moments where a scaffold or a task stumps student—not because the students need reteaching, but because the scaffold or task fails to connect to how students think and what they already know and can do.
This is an essential question to ask when a student struggles. It’s an essential question to engage educators in asking when they analyze data together to improve teaching for equity and EL excellence.
Our data shows some students are struggling in our lessons. Now what?
This is the pivotal moment. Do we focus on providing supports to the students who demonstrate struggle? Or do we also focus on how to evolve our teaching? Are we using data only to sort students for services, or also to challenge our assumptions and change our approach?
We have high expectations. We actively engage students. We observe to take notes on what they say and do. We are feeling on top of our formative data-gathering game!
Then, brain science enters the equation with humbling news: We don’t always see what’s right in front of us. This is especially true when we have implicit biases—which, as humans, we always do. We have all been conditioned by false narratives about racial difference, language hierarchies, and gender differences—whether we believe them or not.
To get good observation data, we have to shift from traditional methods (like lectures and silent testing) to challenging, open-ended, collaborative tasks that actively engage students in processing and applying the new learning. If our learning is sit-and-get, there is nothing to observe but student behaviors of either compliance or disruption.
