Boosting Students’ Data Literacy

I truly believe that data literacy is literacy in the Information Age. Increasingly, we are inundated with conflicting claims about products, medical treatments, the weather—you name it—and it’s tough to make the right decision. To make a rational, optimal choice requires us to be data literate—to be able to understand the nature of how data is collected and visualized in a variety of different formats. 

Unfortunately, most standards don’t put an adequate emphasis on data literacy, given its importance in modern society. The good news is, there’s a way to take a lesson on pretty much any topic and add an authentic data element, and there are ways to use these graphs to build student engagement.

Adding Data Elements

Step 1: Define the topic. For this example, let’s say we have a lesson about climate change. From experience, students often want to know how they can make a positive impact. Ideally, we could find some data about how choices students make over their lifetime will affect greenhouse gas emissions.

Step 2: Find data. One of my go-to resources is OurWorldInData.org. I really like this site because it’s legit (founded and run by scholars in the UK), and their data browser has tabs that show the graph, a table of the data, and the sources all in one place. 

Here is a graph I found pretty easily that shows greenhouse emissions for different food products. Instead of telling students, “You can reduce your carbon footprint by eating less meat,” we can guide them to an even deeper understanding by analyzing this graph for themselves.

Our World In Data makes things easy by giving us a nice graph. If you need to make your own, Google Sheets and Microsoft Excel have options for making charts from a CSV or XLSX file. You might also check out RAWGraphs for a free graph-building wizard.

We might just have students think-pair-share about which dietary changes would have the biggest impact on climate change. Alternatively, we could go another step to build some mystery and investment around the “reveal” of what the graph is telling us, while also deepening student understanding of how raw data gets represented through different visuals.

Step 3: Make the graph mysterious (enigmatize it)!

Strategy A: Blank out the labels. This strategy requires having a graph and its data as a table. Here I have simply blanked out the labels for a few of the bar plot categories. (I dragged little white boxes over the labels and added blanks using a photo editor). I would now ask students to fill in the blanks by looking at the data table (which I got from the same source under the table tab). 

Students will need to carefully compare the graph and table to figure out what’s missing, concreting the connection between the data and its visualization. As an added bonus, because of the mystery element, students will be more engaged in digesting the graph further! 

To facilitate, I would ask some probing questions and reward student observations. Do we notice any general patterns in the data (is there something in common about the categories that have high emissions)? Are there any plant products with super-high carbon footprints? Why might that be?

I used this engagement strategy in two free Galactic Polymath lessons for grades six through 12, which I hope you’ll check out: Part 5 of “Genetic Rescue to the Rescue: Preventing Extinction Through Gene Flow” and Part 2 of “I Like That! How Perception, Emotion, and Cognition Shape Our Preferences.”

Strategy B: Turn the graph into a puzzle (cipher). Another way to add a level of mystery to a graph is to encipher the labels. (Encipher here means to change all the letters using a rule that the students need to crack.) In this example (with a New York Times graph), I used the 2,000-plus-year-old “Caesar cipher” and shifted each letter forward one place (e.g., CAT -> DBU). Enciphering labels is a good data literacy hook because many students are really into ciphers and puzzles, but it also works well to deepen student connection to a graph when the table of data is too big for Strategy A to work. Cryptii is a great resource for creating ciphers. 

At the top of the graph it reads, “Percent of nutritionists saying a food is healthy.” But after using the Caesar cipher, the word “nutritionists” becomes “ovusjujpojtut.” At the bottom of the graph it reads, “Percent of all Americans saying a food is healthy.” But after using the Caesar cipher to change a few key words, it becomes, “Percent of bmm Bnfsjdbot saying a food is healthy.”

There are multiple ways for students to decipher the text. Many will use brute force and randomly try shifting letters forward or back a few spaces. Alternatively, some students might reason that the last word before “saying” in the x-axis label is a plural word (ending in s). Thus, if t should be an s, we need to go back -1. Shifting all letters back one changes “bmm Anfsjdbot” to “all Americans.” 

Once we’ve deciphered it, now we’re curious about the next level of the enigma. What does it mean that granola is off the 1:1 line in this graph?

I used Caesar ciphers as an engagement hook throughout the lesson: “Females singing to be heard: Challenging long-held assumptions about birdsong through data visualization.”

I hope these tips inspire you to weave data literacy into all sorts of lessons. I’m sure you’ll come up with lots of clever ways of enigmatizing graphs that I haven’t thought of!

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