This page features work I have created outside of my district role, including design challenges and independent projects in professional learning, instructional design, and artificial intelligence (AI). Each sample includes a short note about the context in which it was created.
Sample Professional Learning Session: Flexible Grouping with Playlists, Driven by Data
About This Project
I developed this session as a design challenge. All student data in the presentation is fictional and was created for instructional purposes, and all images were generated with Google Gemini.
Overview
This 20-minute live session was designed for classroom teachers who already use progress monitoring and want to connect that data to daily instruction. Participants learn how flexible grouping and playlists work together to support differentiated instruction, and they practice turning student data into grouping decisions.
Learning Goals
By the end of the session, teachers will be able to:
Describe how flexible grouping and playlists work together to support differentiated instruction.
Connect data to real-time grouping decisions that drive targeted instruction.
Leave with a concrete next step they can implement the following day.
Design Approach
I built the session around a music theme to reinforce the playlist concept and make the experience engaging for teachers. The bright, colorful design keeps the tone fun while the content stays practical.
The session moves from concept to application. It opens by defining playlists, with "Must Dos" tied to the current skill and "May Dos" that give students choice, and then explains flexible grouping as daily, data-informed small group instruction. From there, it connects both strategies to Science of Reading, differentiated instruction, and formative assessment, and it shows how the pieces form one system: diagnostic data, small group instruction, and independent practice.
To ground the ideas in real classroom practice, the session includes a sample weekly playlist and a step-by-step look at the daily cycle of pulling small groups while the rest of the class works independently.
Teachers then apply what they learned in a "Data to Decisions" activity, reviewing a fictional class data set and discussing which students to pull first, what the rest of the class should work on, and who might need a targeted "Must Do."
The session closes with differentiated next steps for teachers at three levels: Just Getting Started, Ready to Try It, and Taking It Further. This allows every participant to leave with an action that fits where they are. A final Q&A gives teachers time to ask questions about putting the strategy into practice in their own classrooms.