by Riana Fisher & Elise Heil | 4 February 2022
Data is everywhere.
We generate and consume data at an unprecedented rate. Everywhere we look, data is telling us stories about our world that impact our lives. The ability to read, understand, and use data has become an essential skill—in schools, in an ever-growing field of careers, and in our everyday lives.
Looking at a graph and being able to understand what it is telling you is important. Data literacy is absolutely valuable. You also need to be able to dig further and uncover the less obvious messages. The critical analysis of everyday data is essential to exploring its complexities and making stories of power, representation, and in/justice visible whenever we consume and create data.
As stated in the JusticexDesign (JxD) framework, Design is not neutral. Data is designed. So the ability to read data is not enough. Like all things designed by humans, data is collected and presented in the context of a set of beliefs and ideas about the world, and we have seen over and over again that our beliefs and ideas about the world are subject to bias. These biases can cloud our understanding and perpetuate injustices in our world. If data is not neutral, then our reading of data is not—and must not—be neutral either.
An important first step is to consider the ways in which injustices e.g. institutionalized racism, opposition to LGBTQ rights, and rampant ageism, might be embedded in the design of the data we consume. JxD has created a set of questions that support us in developing a critical sensitivity to the design of data, and to reflect on ideas of power, representation, and in/justice in the data we explore.
Below is one way we’ve used JxD’s Critical Sensitivity to Design Questions to reflect on the data we explored, and considered the design of the data, the perspectives represented (or not), and our own participation in the data.
In 7th grade science class, we used these graphs to explore injustices in our healthcare system. First, we did a slow reveal of the graph, while asking students to respond to the question ‘What do you notice?’
“I notice different races listed at the bottom.”
“I see that for most chunks of the graph, the light blue bar is lower than the dark blue bar.”
“I notice the light blue bar is showing us 2018 and the dark blue is 2013.”
“I notice that the bars are showing ‘No Health Care Visit’ in a year.”
“I notice this is showing us non-elderly adults not getting a health or dental visit.”
“I see the category ‘hispanic’ has high amounts of ‘No Health Care Visits.”
After sharing our observations, we then began thinking about the question ‘What do you wonder?’
“I’m wondering why is there no data for the NHOPI (Native Hawaiians and Other Pacific Islanders) category?”
“I wonder why they put the data in a negative way—‘no health care visit’ instead of ‘health care visit'?”
“I wonder why the ‘white’ category has the lowest percentages and the ‘hispanic’ and ‘AIAN’ categories have the highest?”
“I wonder why it is just non-elderly adults. What about the elderly adults?”
“I am wondering why the numbers are usually lower in 2018?”
After documenting and sharing our observations and our questions, I wanted students to cultivate an awareness of the design of the data and graphs. Using JxD’s Critical Sensitivity to Design Questions, I selected these 3 questions to help us think critically about how the data was designed and presented to us:
Who designed/made this graph? Why might that be important?
Whose perspectives are represented in this data and whose are not?
How do I participate in this data?
Here are some insights from our students:
Who designed/made this graph? Why might that be important?
“It looks like the Kaiser Family Foundation made this graph and collected the numbers for the data. This might be important because I wonder what their goal was? Like why did they want to collect this information and then why did they want to present it to people like this? If they are a pharmaceutical company I wonder if they want to make money off this somehow? Or prove a point to someone important to make changes in the healthcare system?”
Whose perspectives are represented in this data and whose are not?
“I see that white, black, hispanic, Asian, AIAN, and NHOPI populations are represented in this graph, but also not really. NHOPI is a category but they have no information, so they aren’t actually really represented on here. I also see that the graph is only talking about ‘non-elderly’ adults. So old people and kids aren’t represented on here at all. I wonder why not.”
How do I participate in this data?
“I participate in this data because I am a hispanic person and I see that represented as a category of people here on this graph. I also participate because I did see a doctor in the past year and probably in 2018 so I feel like I would maybe lower the percentage for the number of people that didn't get care. However I also don’t really feel like I participate in this data because it’s not like they asked my family for our answers to a survey, and because I am more than just a category on a graph.”
We can see from our students' responses that the data we have explored is anything but neutral. It was designed, collected and presented in the context of the world we live in—a world we know struggles with issues of power, representation, and in/justice. Both our students and ourselves need to be able to ask questions that help us probe these ideas, and consider what’s beneath the surface when we consume data. School is—or can be—a microcosm of what young people will do in their lives beyond the school walls—as members of families, communities, and as participants in greater society. If as teachers, we are intentional about the design of lessons that reveal intersections of data and injustice, our students might develop the habits of mind to support them in making power, representation, and injustice visible—and relevant—every time they consume and create data.
Some further resources to explore the ideas presented in this piece:
Website: Slow Reveal Graphs
Article: Math Curriculum to Promote Growth Mindset by Jo Boaler
Book: Data Feminism by Catherine D'Ignazio and Lauren F. Klein