More Important Than Strategies

“More strategies!,” educators often request when we talk about teaching and leading for multilingual learner (ML) excellence. This impulse is understandable: strategies and scaffolds are shiny and inviting. They are easy-to-visualize solutions, often written into checklists and mandates to implement at scale. And, yes, strategies are important tools. However, tools alone do not build a house. Strategies without high expectations lead to over-scaffolding at scale.Strategies without data-driven reflection lead to,…

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New Edition of EL Excellence Every Day

Educators are asking, “Where can I find EL Excellence Every Day? Did it go out of print?” It’s not out of print. On the contrary, this popular flip-to guide has an updated and enhanced second edition released this year! NEW EDITION, NEW TITLE! EL Excellence Every Day now has a new edition with a new title: Asset-Based Language and Literacy: The Flip-To Guide to Multilingual Learner Excellence in the Core.…

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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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Unexpected Joy

With the mad string of traumatic and unexpected events in 2020, why not a bit of magic? Why not a totally irrational moment of joy?

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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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Expectations Define What We See

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?

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