Grant Chandler, former Director of Professional Development for MSU MI Excel, explains Phase 1 of the Collaborative Learning Cycle.


Bruce Wellman, Co-Director MiraVia, LLC, reviews Phase One of the collaborative learning cycle and its importance in establishing well-prepared teams to support deep data conversations.


The data dialogues process begins with the formation of a well-prepared team. Before any data is considered, school and district leaders agree upon team norms, make predictions about what the data will show, and uncover their own underlying assumptions.

Corresponds to the MI School Improvement Model Phase:
GATHER

Sketch of Team Activities:

  • Identify and select district and school support team members.
  • Develop tools and strategies to promote thoughtful conversation.
  • Before looking at data, begin talking about what is expected and set norms.
  • Identify predictions and assumptions.
  • Reframe/rethink habits of mind.
  • Debrief the process and prepare to dig deeper.

Purpose:

  • Develop team readiness.
  • Honor team members’ expertise.

Key Questions:

  • What do we predict the data will show? Why?
  • What questions do we have?
  • What are the possibilities for learning?
  • What might be missing from the data?

Why Phase One Matters:

  • When this phase is cut short, teams often find themselves overwhelmed by data.
  • By taking all the necessary steps to engage the data dialogue team (without digging too deep), a foundation is built to ensure open-minded, effective problem solving.

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