Data Visualization Shows Gender Discrepancies Among ACT Test Takers, Wins Award
Every dataset has a story to tell, and it’s Dustin Arendt’s job to uncover some of those stories.
As a data visualization scientist at Pacific Northwest National Laboratory, Arendt works on algorithms that convert data–like the type found in spreadsheets, databases, and online–into something with more style and utility. These visualizations can be used to monitor networks for hackers and viruses, or gain insights about complex biological or environmental systems.
But recently, Arendt used a technique called storyline visualization to show gender discrepancies among college students that took the ACT exam. The result won him and his teammate—then PNNL-intern Yanina Levitskaia from the University of Washington—the highest award of the 2015 International Data-Visualization Contest, sponsored by the IEEE Visualization Pioneers Group.
A storyline visualization may look like a bundle of cables or a crazy highway interchange, but it’s packed with observations about how data relate to each other. In Arendt’s work with data on more than 200,000 students that took the ACT in 2013, three visualizations revealed the following observations:
- Men that scored in the highest range (33-36) on the ACT usually went on to study engineering through college. This prompted Arendt to ask what paths high scoring women pursued instead of engineering.
- Focusing in on the top two score ranges (28-36), the data showed that women more often pursue liberal arts, health and biological sciences, whereas men pursue engineering and computer science.
- And finally, Arendt looked at an evaluation ACT performs to determine what fields are a good “fit” for students. About as many women were as fit for engineering and computer science, according to the evaluation, but as observed above, many of those women chose other fields instead.
“Let’s do more to make engineering and computer science more attractive choices for those individuals,” Arendt wrote on his poster.
For Arendt, inspiration to represent certain types of data as storylines came from a popular web comic showing the interaction of characters from movies like Lord of the Rings and Star Wars. Though the poster was meticulously hand drawn, Arendt wanted to develop algorithms that could automatically generate beautiful storyline visualizations from any data. The visualizations don’t have to be static, either. One of his main focuses for the Analysis In Motion (AIM) Initiative at PNNL is to create visualizations that update as new data comes in—a useful feature for those never-ending stories.
About the author: Eric Francavilla is a news and digital communications specialist at Pacific Northwest National Laboratory.