Activity 5B: Data-Driven Dialogue - A Structured Process to Examine and Discuss Data TemplateWorkshop: Transforming Teaching and Learning Through Data-Driven Decision Making

Phase 1: Predict Phase 2: Go Visual! Phase 3: Observe Phase 4: Infer/Question

Surfacing experiences, possibilities, expectations

○ With what assumptions are we entering?

○ What are some predictions we are making?

○ What are some questions we are asking?

○ What are some possibilities for learning that this experience presents us with?

Analyzing Data

○ What important points seem to “pop out”?

○ What are some patterns or trends that are emerging?

○ What seems to be surprising or unexpected?

○ What are some things we have not explored?

Generating possible explanations

○ What inferences and explanations can we draw?

○ What questions are we asking?○ What additional data might we

explore to verify our explanations?

○ What tentative conclusions might we draw?

This phase occurs before the Data Team looks at data. It focuses on generating predictions about what the data might show. This phase serves a variety of purposes:

accessing prior knowledge of working with data;

making predictions about what to expect when looking at the data; and

getting concerns and assumptions on the table.

The team should now chart/graph the data so that the whole team can see it. The importance of creating big charts and graphs:

thinking of the group is shared publicly;

it keeps the data over “there” and not between team members;

everyone on the team focuses together on the same data set at the same time; and

graphs can be used later to share with other audiences.

Document your observations on chart paper and add it to the Data Wall. Make sure the team understands that an observation is something that can be made using the five senses and contains no explanations (inference). For example:

When reading a thermometer, “It's 52 degrees” is an observation; “52 degrees is cold” is an inference.

Or, “75 percent of fifth-grade students are below proficiency in geometry” is an observation; “teachers aren’t teaching geometry very well” is an inference.

In this phase the Data Coach will want to pay attention to “blaming statement” and engage that Data Team in a dialogue to explore those statement more carefully for example Why? Why? Why?

Inference: Students do not know how to graph data.

Why? Students have limited opportunities to learn how to graph.

Why? The instructional materials do not include graphing.

Why? The instructional materials are outdated and do not align with the learning standards.

Phase 1: Predict Phase 2: Go Visual! Phase 3: Observe Phase 4: Infer/Question

Explain the type of data set the team will be analyzing:

What kind of data is it? What does it measure? What does it quantify? What does the grade level cover?

Data Team Task:

1. Predict what the data may show2. Discuss the predictions 3. Record predictions on chart

paper4. Tape them to the data wall

Have data team members draw the graph for providing a deeper personal and group learning experience.

Display data for all to observe on a daily basis to:

identifying a student-learning problem,

verifying causes, generating solutions implementing, monitoring, and

achieving results

Display “No-Because Sign”

This sign will be used as a signal to the Data Team when they are moving into making inferences (i.e., using “because statements) during observation phase.

The dialogue monitor will take a primary responsibility for altering the team to inferences that are made during the observation phase by placing the sign on the table.

All data team members should feel free to support the role of the dialogue monitor and place this sign on the table when inferences are being made during the observation phase.

Debrief the process:

What was an “aha” members had about using the process?

What questions do you still have?

Predictions Visuals Observations Questions

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