Creating Data Readers & Communicators in a Pandemic

The 21st century is awash in data. Regardless of what job our learners hold in the future, they will ALL need to know how to read and make sense of data.

Here is an example of my electricity bill from May 2019. While the electrical company was more than willing to charge me for 3x as much electricity usage in May 2019 as I used in May 2018, by looking at the data I wasn’t so certain about the numbers. By noticing that the May 2019 didn’t follow the pattern from the previous May, nor the annual trend, nor my anecdotal memory of our electricity use that month we were able to contest the bill and save a solid chunk of change. :)

The importance of high quality data, of understanding how to read and make sense of data, and how to represent data are especially on display as we continue to live through the Covid-19 pandemic. As a data nerd what I think is most interesting is how much the pandemic is helping people more readily see that data are just numbers/words/ink on a page unless we make meaning from them. And we need to learn to be critical consumers of the meaning others attribute to the data and how they present the data. Here are a few examples from the past couple of months similar to my electricity bill.

Posted on May 9, 2020 on the Georgia Department of Public Health website, this graph indicates a rosy outlook on the case counts. However, the organization of the data was inaccurate and misleading. For more information about concerns with this approach were written up here: https://www.businessinsider.com/graph-shows-georgia-bungling-coronavirus-data-2020-5.

Published on March 29, 2020 by the Global Times this graph carries a lot of information but conflates the two variables of “per 1,000 people” and “mortality rate” by tying them to the same y-axis scale. While better than a double y-axis graph, this is misleading as 0.81% mortality is not equivalent or relevant to 0.81 per 1,000 people of hospital beds, nurses, or doctors. Original graph at: https://www.globaltimes.cn/content/1184112.shtml.

These maps use a similar color scheme, but adjusted values to represent each color between July 2nd and July 22nd for cases per 100,000 people in Georgia. A discussion of the concerns with this practice was written up here: https://www.globaltimes.cn/content/1184112.shtml.

Looking through such examples of Covid-19 data visualizations recently I was reminded of a paper by Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, and Gerd Kortuem (2016*). In the article they identify 4 different types of Data Literate citizens (listed below).

Types of Data Literate Citizen

The team identifies four types of citizen according to the situations in which they would need to use data for solving real world problems (like making sense of Covid-19). These are:

1. Readers – who need skills to interpret data that is increasingly presented as part of their every day life.

2. Communicators – who make sense of and tell stories about data for others to digest.

3. Makers – who need the skills to integrate data into broader overall strategies for identifying and solving real-world problems and to be actively conscious of their own data contributions that drive smart city applications.

4. Scientists – who need to combine strong technical data skills with communication skills and in-depth knowledge of the domain of the data.

The authors clearly state that these four types are not all kinds of ways that citizens would need to use data, but instead present them as a “good basis” to think about what kind of data literacy skills citizens would need into each use case.

As we live through this pandemic together and as we work tirelessly to educate our learners through the extra bumps and hurdles this year, let’s take this as a good reminder that we are working to set all of our learners in K-12 up for success at least as Readers and Communicators of data.

 *Wolff, A., Gooch, D., Cavero Montaner, J.J, Rashid, U., Kortuem, G., (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), 9-26.