26 September 2017

Introducing Data Academy

In my previous post, I shared some of my impetus to build professional learning opportunities around data use. Now I want to preview the full story arc---9 hours built around starting with a question and ending with a data story.


There are five chunks. Not pictured is how the stories will be shared this spring. The role of an audience is both unique and critical when storytelling with data...so we'll definitely need a space and opportunity to engage with that. But I'm still pondering what that piece will look like. Each session will have some time to learn together, and some "lab" time for independent practice with the concepts. Here's what we have planned:

Session one: What is effective data use?
We will explore current frameworks and research around data use in schools. Topics will include data literacy, student-involved data use, and classroom “look fors.”

Session two: Frame questions and find data
We will focus on asking high-leverage questions, then using data mining, canned reports and extracts, and other options to pull relevant data from Skyward and Homeroom to answer these questions.

Session three: Clean, organize, and explore
It’s time to sharpen your skills with Excel, including how to join data from multiple sources, build pivot tables, as well as formula basics.

Session four: Visual(ization) literacy
Many of us were taught to read and write text, but few know the basic rules of creating and interpreting powerful visuals. We will focus on elements of visual communication, including color, chart choice, and other attributes.

Session five: Storytelling with data
A data story combines text, data, and visual elements. During this session, we will consider the ethics of design choices, the responsibilities of communicating data in equitable ways, and how we can use basic statistics to know when we have a story.


I am sure that these descriptions and the path we take through them will morph along the way. Although I built these sessions from the lens of my own background and work, I was pleased to see it reflected in an article by Ellen Mandinach and Edith Gummer entitled What does it mean for teachers to be data literate.

http://www.sciencedirect.com/science/article/pii/S0742051X16301391

The bottom row of their framework envisions something similar to the arc I identified. They have
  • Identify problems/frame questions
  • Use data
  • Transform information into decisions
  • Transform data into information
  • Evaluate outcomes
I'll post some additional information about each session as we go along this year. If you are interested in the materials that support the work, please visit the repo on GitHub.

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