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Data Literacy Guide

Data Literacy Guide

By Tamara L. Shreiner

Data literacy is the ability to comprehend, analyze, interpret, evaluate, create, and argue with data and data visualizations. It involves knowing what information data visualizations such as timelines, maps, and graphs provide; being able to draw accurate conclusions from them; and recognizing when they are being used in misleading or inappropriate ways.

Teaching data literacy in social studies is critical for three primary reasons. First, students regularly encounter data visualizations in social studies texts and other resources: Timelines are used to support our sense of chronology and historical time. Maps help us visualize spatial relationships and movements across local, regional, and global scales. And graphs allow us to see patterns, trends, and anomalies in large amounts of data. If students don’t know how to make sense of such data visualizations, they will miss out on important information about the topics they study.

Second, datasets and data visualizations are among the historical records and artifacts we use to understand the past. Primary-source data visualizations provide insight into people’s worldviews, and enable us to see which historical events or political, social, or economic issues mattered to them.

Finally, social studies is supposed to prepare students for civic life, and data visualizations are everywhere in our society. People use data visualizations to inform us about political, social, and economic topics, and to persuade us as we make decisions about policies to support, candidates to elect, or products to buy.

In our “datafied” society, you cannot be an informed citizen, one who is able to make reasoned decisions, unless you are data literate.

Although it’s often assumed that data visualizations are easy to understand—the whole point of them is to illustrate data—making sense of them can be quite effortful and challenging for students to analyze and interpret. To make sense of a data visualization, a learner must see all its visual elements, map between the visual elements to determine what information is being provided, and consider its source, context, and distortions or deletions before drawing conclusions or using it as evidence. In other words, students must learn to read the data, read between the data, and read beyond the data. This guide provides a summary of three strategies that can help students attain and apply those skills, whether working with a timeline, map, or graph.

Hierarchical Questioning

Developing a series of hierarchical questions to help students read the data, read between the data, and read beyond the data is one relatively simple strategy to support data literacy. Similar to OER Project’s Three-Step Reading approach for text, this strategy encourages students to engage with the material at multiple levels. Here, though, the strategy involves formulating questions that are specific to a particular data visualization. It may be a good approach for students who need more support in making sense of data visualizations. Once they are more comfortable, they can engage in Three-Step Reading for Data on their own. Consider this Our World in Data graph on worldwide deaths in wars from 1800 to 2011:

Read the Data

Viewers can clearly see the numbers of deaths increasing during the world wars, but there is additional information students might miss if they’re not encouraged to engage with the graph. Begin with questions focused on helping students read the data. These questions should encourage students to notice all the important visual elements, should ensure that they have the necessary background knowledge to decode language and labels, and should help them extract basic information. This first level of questions for this graph might therefore include:

  • Where did this data come from? That is, what is the source?
  • What kinds of wars are represented by the blue bars?
  • What’s the difference between a civil war and an interstate war?
  • When did combatants’ deaths in war first surpass one million?

Read Between the Data

The next level of questions is also about extracting information, but at a more holistic level. These questions should focus on comparisons, changes, trends, or patterns in the graph—that is, they should help students read between the data. Questions at this level might include:

Approximately how many more people died in interstate wars than civil wars in the twentieth century?

How do deaths from interstate wars compare between the nineteenth and twentieth centuries? What about deaths from civil wars?

During which time periods was it relatively peaceful? Where do you see the fewest deaths from conflict?

Read Beyond the Data

Finally, it’s important to include questions that help students read beyond the data. This line of questioning helps students reason beyond the graphical display to think about the conclusions or inferences they can draw from it. Such questions might include:

  • What events explains the deaths from conflict in the twentieth century?
  • Why did deaths from civil wars increase when deaths from interstate wars increased?
  • How might the data change if we looked at deaths as a percentage of the population? What if we also consider the deaths of noncombatants?
  • What would the graph look like if we extended it to the present?

Slow Analysis

The slow analysis technique is, as the name implies, a mechanism for slowing down the otherwise quick cognitive processes involved in decoding a data visualization’s message. It helps learners who might be overwhelmed or unsure of how to begin analyzing a data visualization methodically process information by addressing each visual component one by one, in an order that makes sense for optimal comprehension.

Slow analysis uses signaling, which entails providing visual and verbal cues—in this case, in the form of circles, arrows, or highlighting—to focus students’ attention on different components of a data visualization.

This process ensures not only that students pay attention to all the important visual features, but also that there is ample opportunity to address terminology or conceptual vocabulary; connect the information to students’ prior knowledge; get ahead of possible misconceptions; and answer students’ questions.

Designing a slow analysis lesson involves simply using the shape tools on slide presentation software (such as Google Slides or Microsoft PowerPoint) to focus attention on a single element in a graph, and then designing accompanying questions and explanations.

For example, using the Our World in Data choropleth map showing 1945 political regimes below, you might first call attention to the title display, emphasizing the importance of looking at a data visualization’s title and subtitle to determine what it’s about, and then discussing the definition of political regime and the significance of 1945 in world history.

The next logical step might be to cue students to examine the legend to see which categories of political regimes are represented, and then go on to discuss their definitions.

From there, you might use cues to call students’ attention to the notes, which have information on how the designations were determined, and the source, so they can discuss its credibility. From there, you could cue students to connect the colors in the legend to the shading of the countries, perhaps focusing on a particular region, country, or regime to drive discussion, and again using circles or arrows to focus students’ attention.

Slow Reveal

The slow reveal technique is also about reducing students’ cognitive load as they try to make sense of a data visualization. It entails covering all visual elements of a data visualization (again, you can use shape tools to do this) to show only the most stripped-down version of the data visualization first, and then slowly revealing additional features one by one. All that is visible to students in the initial presentation of the data visualization are the signifiers and colors that encode the data, such as the line and tick marks on a timeline, the color-coded political units on a choropleth map, or the lines or bars on a graph. By stripping down a data visualization, students can focus on making observations about patterns or trends they observe without being overwhelmed by all the information. Consider the graph on population growth below. The graph provides a lot of useful information, but it may seem like too much for students viewing it for the first time. 

 

Using shape tools, you can cover all but the axes and line of the graph.
Next, encourage students to engage in discussion about what they notice and wonder about in the stripped-down data visualization. In this case, you want them to notice dips and spikes in the line. After this initial discussion, you can methodically reveal a new visual element in the data visualization. To continue with our example here, you might show students the years along the horizontal axis so they can see how big the temporal scale is and make predictions about what the graph is about.

The order in which you uncover elements is dependent on your instructional goals, but regardless, each time a new element is uncovered, it’s important to pause and allow students sufficient time for observation, deep thinking, and class discussion. Questions you should ask with each reveal include:

  • What new information did we learn?
  • How does this change your thinking about the graph?
  • What do you wonder about now? What new information do we need?

This line of questioning allows students to make connections across visual elements, to activate their background knowledge, and to make predictions or prompt inquiry.

Data Literacy in OER Project

To build data literacy, OER Project has developed a wide selection of Data Explorations, which can be found by clicking on Topics in the navigation and scrolling to the Data Literacy page. Every Data Exploration begins with an introductory article that introduces students to the charts included in that exploration and provides historical context. These articles were written in collaboration with Max Roser and the team at Our World in Data (OWID). (Learn more about OWID here: https://ourworldindata.org/.) Each Data Exploration centers around a selection of thematic charts from the OWID website. Students should spend the bulk of their time during Data Explorations “reading” the charts. We’ve developed a Three-Step Reading for Data tool to help support student analysis, and you might also use the Slow Reveal and Slow Analysis strategies with these charts.

The Data Explorations cover a wide range of topics, allowing you to choose which will work best for your course and class. Below is a list of the Data Explorations along with some recommendations for historical topics that align well with each.

OER Project: World History & OER Project: Big History

OER Project: Climate

For an example of data storytelling in the classroom using the video The Fallen of World War I, check out this blog.