Note: I write this blog as a white woman especially for white readers who have ever thought, “I’m not a racist,” as I have. All readers welcome.
Enjoy and share this video with a comforting message to families in sixteen languages.
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.
For observation data to matter, we need to be clear on our learning intentions. For it to matter for equity, we need to be aware of our biases and intentional about disrupting defaults of low expectations for students from historically-marginalized groups. What do you see as students engage? How do you interpret the data? How do your lived experiences shape what you see?
Observing students is one of the most important teaching skills. It is also one of the most under-prioritized in professional learning initiatives and district-wide change. Using observation data for equity requires more than watching students — we need to learn to see beyond our own biases and use new data to challenge our own assumptions.