Improvement Science Meets Neuroscience

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.

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“There is no greater agony than bearing an untold story inside you.” 

Maya Angelou

“There is no greater agony than bearing an untold story inside you.” Maya Angelou

What’s your commitment in 2021? Mine is courageous creativity. Here’s why.

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Use Observation Data to Reflect in Humble Inquiry

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?

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Observe with an Asset Orientation

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.

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Observation Begins with Active Engagement

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.

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Four Verbs to Hone Your Observation Superpowers

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.

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