This is part 4 of the “True PLC” blog series designed to articulate the specifics of and to reflect upon an 8-month virtual Professional Learning Community (PLC).
The first PLC meeting was largely about getting connected to one another and to the purpose for the group’s collaboration. The second meeting was about getting specific about the problems we want to solve about student learning. The third meeting, which I will highlight in this piece, focused on planning first improvement cycles in service of our specific improvement aims. This is really where the rubber hits the road!
The objectives for this 3rd meeting were to:
- Connect with one another
- Glean insights from an example of an improvement process to inform our own
- Support one another to ensure change ideas are aligned to key “whys” behind outcomes you want to change
- Draft the plan for your first improvement cycle, using the Plan-Do-Study-Act cycle as a guide
Here’s how we went about it…
- 10min: Get Connected – Round Robin Share-Out (All Together)
- 15min: Text Analysis – A Case Study of an Improvement Process (On Your Own & Share Key Insights)
- 5min: Capturing Your Current State – the key “whys” producing the outcomes you want to change and the change ideas you think will result in desired improvements (On Your Own)
- 20min: Collaborative Planning – Ensure each group member leaves having zeroed in on their first specific change idea and the data they’ll use to measure whether their change leads to an improvement (Small Group Break-Outs)
- 5min: Next Steps – In next month, carry out change idea and collect relevant data. Be ready to share at next meeting. (All Together)
- 5min: Reflection – (On Your Own)
Check out the modified (for public consumption) and more granular version of this agenda here, and you should feel free to make a copy of the document and make it your own. You might find it interesting to review the improvement aims this PLC has identified in the Improvement Process Planning Table (Table prompts heavily informed by Bryk’s Learning to Improve).
We’re not necessarily standards-driven in our approach. Over the weekend, my supervisor shared Solution Tree’s Global PD effort with me. It’s the DuFour team’s effort to offer a virtual support system for teams enacting PLCs. The online tool looks to be heavily standards-driven, with an emphasis on learning targets and common assessments. I think there is great potential in this and at the same time it bumps up against some of my beliefs about assessment and data.
I decided to create an environment where my participants selected the thing about their students’ learning that they’d like to change most (i.e. deepen contextual responses to peer feedback on writing). They are determining the most relevant data to create, collect, and analyze (i.e. evidence of peer feedback) in order to understand the impact their efforts have on that desired change. If an aim ends up being tied to a content standard, and if a common assessment is the data generation approach that feels relevant, great! If not, and the approach still leads to measurable improvements in student outcomes, I think this effort is just as valid. I would be VERY curious to hear thoughts on this, and I look forward to reflecting upon this with my PLC at the end of our effort too – Does an improvement effort have to be standards-driven?
So far we’ve been planning and preparing. Now it’s time to DO. I’ve asked my participants to enact their first prioritized change idea and to gather any relevant data that emerges between now and our next monthly meeting, even if minimal. At the next meeting, I’ll create the conditions for participants to share that data and to make sense of it in a way that helps them to make informed decisions about what to do next to continually strive towards their improvement aim.
In participants’ reflections, they continue to appreciate small group breakout time, the support they receive in this setting, and the space it provides to share plans and discuss ideas. To honor this, I think for the next meeting I will quickly model a protocol for data analysis and then send folks off into small groups to replicate it on their own, using the data they’ve brought with them.