Data visualizations are an indispensable tool in eLearning and performance support. They communicate stories, help people explore data, and clarify complex information. The most attractive format is not always the right one for your data; being able to choose the best data visualization format for your eLearning can be challenging. Here’s some help.
Consider your goal
Not all data visualizations in eLearning accomplish the same goal. Some are great for showing change over time or comparing two variables. Others show a bigger-picture perspective or indicate trends. In 1984, William Cleveland and Robert McGill applied the science of visual perception to the creation of graphs. They came up with a hierarchy of encoding precision—chart types—with chart types that allow humans to visually perceive precise information at the top, ranging down to less-precise types that excel at presenting an overview or trend view.
Most humans can easily perceive differences in line length, height, or position along an axis, making bar charts and scatter plots highly accurate ways to enable readers to compare values. Area, size, and slope are more difficult for humans to perceive visually, so pie charts and bubble charts are good for showing a trend or big picture but make comparing exact values difficult. Which type to use and where to land on that scale depends upon the goal of the visualization.
When comparing sales results, test scores, participation rates, or similar numerical data, whether among learners, between divisions, or over time, a chart type that offers accurate, precise perception is ideal, whereas a pie chart or bubble chart might not be the right format. When showing regional differences, though, a map with bubbles or colors representing greater or lesser values can convey the needed information; if readers need fine detail as well as the big picture, multiple charts will be needed to tell the complete data story.
Chunking content in data visualizations
A common error that designers make when creating visualizations is putting too much information into a single chart. Chunking content—useful in presenting large amounts of complex information—works in data visualizations as well as in text.
Imagine that you want to show the sales results of five regional teams, each with four sales representatives. You’ve got quarterly data for the past five years. That’s a lot of information. You could create a single visualization with a line for each sales rep (20 lines), using a different color to show each region or an additional line for each region’s average. You might want to create this one comprehensive chart—but it could be very hard for a learner to tease out the salient information from all those colored lines.
Instead, you might consider “sub-setting” the data—create the comprehensive chart, but replicate it five times. Each one would emphasize one team’s performance with a bold, striking color and show—but gray-out—the remaining data. This way, in five separate images, you offer more a focused view of each team’s performance, showing the individual members’ results. A sixth chart could compare the averages of the five teams. This presents all of the data but does so in a way that offers manageable chunks for readers to study and understand.
Choose the right chart—or charts
Before designing a data visualization, decide whether the readers should be able to:
- Compare values
- Identify averages and outliers
- Figure out proportions or relative parts of a whole
- Analyze trends
- Understand the relationships between two (or more) variables
- See changes over time
- See rank or hierarchy
- Compare values with a fixed reference point
- Understand flow or movement between conditions
- Understand spatial relationships or geographic patterns
Several free online tools show a variety of data visualization formats that can accomplish each of these goals.
- The Data Visualisation Catalogue offers a search by function or a list of visualization types. Each one includes an illustration and an explanation.
- The Financial Times graphics department created an enormous poster, titled “Visual vocabulary,” that explains various chart types and their purpose. It’s available online and for download.
- Ann K. Emery, a data scientist and teacher, created an interactive online catalog of what she terms essential chart types. Each has a brief explanation, an illustration, and examples. These can be sorted by purpose.
With practice and experience, it becomes easier to choose the best data visualization format for your eLearning. The next step is learning to create them! Start by mastering three basic data visualizations that are likely to be useful over and over in eLearning, and learn more about how to use data and analytics in eLearning at The eLearning Guild’s Data & Analytics Summit, August 22 & 23, 2018.