Data Visualizations in eLearning Facilitate Communication

Datavisualizations are an easy-to-use, essential tool for understanding andcommunicating the complex stories that data and analytics can tell. Creatingmultiple visualizations allows a developer to explore the data and learn someof the interesting ideas, patterns, and surprises it contains. And, using datavisualizations in eLearning provides a way to enhance learner comprehension andengagement while revealing the many stories hidden in otherwiseincomprehensible data tables.

Data visualization enables exploration

A dataset usually consists of numbers—hundreds, eventhousands of numerical values that can describe virtually anything. Datasetsare often presented in tables or spreadsheets with myriad lines and columns.

Looking at these numbers can uncover information—itis technically feasible to identify an outlier or a high value, for example, orto calculate an average using only a data table. But our limited human brainslack the ability to really grasp all of the information contained in a datasetsimply by looking at the sea of numbers.

“Tables alone are definitely not sufficient to give us anoverview of a dataset,” Gregor Aisch wrote in the Data Journalism Handbook.

Some analysts will look at a few descriptive statistics fora dataset and believe that they can pull the important information out of thosevalues, which could include mean, variance, and correlation between x and y.But, as Anscombe’squartet illustrates, datasets with the same descriptive statistics can tellvery different stories—differences that are obvious when the datasets aregraphed or turned into visualizations.

Data visualizations enable readers to explore data and seepatterns or identify trends and outliers. Perhaps most importantly, a datavisualization enables exploration of a dataset by raising questions. Looking atthe visualizations can, for example, trigger questions about why some datavalues are so different from the rest of the set (outliers) or what mighthappen if two or more of the variables were combined—or even why the numbersare what they are.

Data journalist AlbertoCairo, who holds the Knight Chair in Visual Journalism at the School of Communication ofthe University of Miami, emphasizes that data exploration does not provideanswers or explanations for the numbers; it offers a starting point. Theanalyst or developer must confirm the data by talking with experts and diggingdeeper into the trends, patterns, and information in the data.

Data visualizations facilitate storytelling

In eLearning and performance support, a key role for datavisualizations is telling stories—putting the information that the data captureinto a context and form that learners can understand and apply on the job.

Cairo cites four principles that data visualizations mustfollow in order to successfully communicate or tell a story:

1. Any good graphic—or act of communication—beginswith good data.
2. A successful data visualization attracts andengages readers’ attention.
3. Successful data visualizations do not frustratereaders; the graphic must strike a balance between an engaging or surprisingappearance and a clear, easily understood presentation of information.
4. Communicating data does not meanover-simplifying it; at the same time, it’s important not to overload readers.

A successful visualization has the right amount of dataneeded to tell the story fully and accurately. Too much information can wreakhavoc with reader comprehension. Including too little data risks omitting keydetails and communicating a misleading or even a false story. “Datavisualization clarifies information; it doesn’t simplify,” Cairo said in anonline lecture.

The challenge for designers is to create attractive,engaging, clear, accurate data visualizations. Doing so requires knowledge of visualdesign principles, instructionaldesign, and perception.

“Data visualization is only successful to the degree that itencodes information in a manner that our eyes can discern and our brains canunderstand. Getting this right is much more a science than an art, which we canonly achieve by studying human perception. The goal is to translate abstractinformation into visual representations that can be easily, efficiently,accurately, and meaningfully  decoded,”Stephen Few wrote in “DataVisualization for Human Perception.”