How do we empower diverse learners with the academic language essential for school success? Let’s discuss this important topic on Monday, August 18th at 6pm EST during #thetileonechat on Twitter. Thank you, Tiawana Giles (@TiawanaG) for this opportunity to be a guest moderator this week for your great Twitter chat! For readers who like to have time to think about questions early, I’m posting each question now in this blog. As a chat participant, I…
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.
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.
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.