Over the past few weeks we have engaged in data talks at my school and several colleagues from other sites have asked me to share effective protocols for data talks. Ever since I began developing my facilitation and coaching skills, protocols have intrigued me. Not only do protocols provide structure and a predictable, transparent routine, but they also can be intentionally designed to support asset-based and equity-rich conversations. In this post I want to share some of what I have learned and, as always, I hope to learn from reader suggestions and feedback.

“Learning occurs when we shift from professional certainty to conscious curiosity, from isolated individual to collaborative community member, and from passive technician to active researcher.” – Data-Driven Dialogue

 

When preparing for effective and transformational data talks, begin by building a strong learning community. Define and assign roles, including a Process Observer, a Facilitator, and Thought Partners. Focus on asset-based, person first language – for students, self and colleagues (for more on Process Observers, see my posts in Resources.). When engaging in a data conversation, encourage:

• exploratory language,
• a focus on equity of talk with structured talk routines (whip around, popcorn, organic sharing of ideas),
• putting ideas on the table to discuss (and not personally identifying with the ideas),
• a focus on continuous improvement as part of our work,
• a willingness to be vulnerable,
• plural, open-ended questions, and
• listening fully to understand, pausing, and paraphrasing.

For more information, I highly recommend Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry and Adaptive Schools’ Seven Norms of Collaborative Work.

“When teachers operate in professional communities and take collective responsibility for student learning they produce school-wide gains in academic achievement. – Data-Driven Dialogue

 

Before engaging in a data talk, decide which data is going to provide the richest conversation. I highly recommend reading Street Data: A Next-Generation Model for Equity, Pedagogy, and School Transformation to understand using data to transform schools. The book discusses Satellite Data, Map Data, and Street Data. Consider a focus on Street Data which helps to understand the student experience. Street Data includes:

• Artifacts – these give us insight into the strengths, and culture of the creator of the artifact
• Stories/Narratives – Empathy Interviews, Focal Student Inquiry, Identity maps, etc.
• Observations – these include a study of interactions and communication moves

There are a variety of protocols available for data talks, and I highly recommend modifying whatever you use, to make it fit the needs of your students and teachers. Then, ask for feedback and reflect on what worked and what can be improved with the protocol, to revise it for the next time you use it. I began with The Collaborative Learning Cycle (as described in Data-Driven Dialogue); refer to the link for effective questions for each stage. Street Data has an excellent protocol (p. 180-81). Dividing a data talk into sections provided clarity for participants about the focus of each section (times below are based on a 45 minute team meeting):

• Activating and Engaging (5 minutes) – This section sets the tone and shapes how we will explore the data, and occurs before looking at any data. We surface assumptions and mental models, in order to go in with an open mind.
• Exploring and Discovering (5-10 minutes) – As we collaboratively inquire, we make space for multiple perspectives. Data is presented in a third space and is referred to as “the data” rather than “our data” to share what pops out and avoid rushing to premature conclusions.
  • During this stage, consider patterns in data from a variety of groups. This is where an equity lens is important: Are there groups of students whose needs we are not meeting?
• Organizing and Integrating (15-20 minutes) – During this stage, we interpret and consider the stories behind the data, we explore a variety of ideas to try, while focusing on what is within our sphere of influence. It is recommended to develop at least three ideas to try.
  • At this time, consider barriers that may be interfering with success. What might we be doing that is getting in the way of learning? How can we design with intention to eliminate barriers and offer scaffolds and on-ramps to the learning?
  • Some important questions that come from Street Data include:
    • “What is the student experience being revealed to you? What does the date reveal about the experiences of our most vulnerable learners?
    • What are the student’s strengths, assets, and sources of cultural wealth?
    • What is getting in the way of the learner’s well-being, cognitive growth, and agency?
    • How might racism and white supremacy culture be at play here?”

Street Data also offers Reflection questions:

• “What matters about this data?
• How does it (or doesn’t it) stand up to our vision?
• Where is our greatest opportunity?
• What will help us learn more?
• What will help us move toward the pedagogy of voice?”

After the discussion, offer time to receive feedback, debrief, and decide on action steps with a timeline. How will you measure the impact of the changes? During follow ups on the action steps, focus on what worked and what can be improved/tried next. Keep the focus on continual growth and reflection. And keep students, especially our formerly marginalized students, at the center of all decisions and reflections.

The writing above is just the tip of the iceberg of my learning (in large part alongside colleagues) and the powerful resources included. I hope to continue to learn about how we can use protocols to reflect on practices and beliefs, how we can engage in continuous improvement to explore new ways of teaching and learning, and how we can continue to listen to our students. Every day we have the privilege of learning from, and alongside, student genius.

“Equity is an approach to ensuring equally high outcomes for all by removing the predictability of success or failure that currently correlates with any racial, social, economic, or cultural factor. – Street Data

 

Resources:
Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry, Wellman and Lipton
The Collaborative Learning Cycle, Wellman and Lipton
Focus Students and Process Observers, Collier
The Power of a Process Observer, Collier
Seven Norms of Collaborative Work, Adaptive Schools
Street Data: A Next-Generation Model for Equity, Pedagogy, and School Transformation, Safir and Dugan