Visual learning tools are being used more and more in today’s classrooms because of their proven effectiveness in helping students understand complex concepts. Some studies have shown that 65% of us are visual learners, and that visual aids can improve learning by up to 400%.
Earlier this month, Dr. Anselm Spoerri of the School of Communication and Information at Rutgers University presented an ACRL/Choice webinar: Teaching Visual Learners with Data Visualization—Delivering an Active Learning Experience for Engineering Students.
In this webinar, Dr. Spoerri emphasized the importance of selecting the right display format to make data patterns visible. “It’s all about being able to tap into the perceptual capabilities of the viewer so that they can gain insights into the data sets,” he said. Dr. Spoerri outlined key data visualization design principles:
1. Interactivity. When the user interacts with the data, the display gets updated instantaneously. The New York Times delegate calculator is a great example.
2. Immediate Feedback. This function is essential for supporting interactivity. Immediate feedback allows the user to instantly extract meaningful information from the visualization.
3. Linked Displays. To explore a large information space, such as 5 – 10 data variables, multiple displays are needed to show subsets of the data space. Linked displays enable the user to explore how one selection in one display plays out in another display. An example is a stacked scatterplot.
4. Overview > Zoom + Filter > Details-on-Demand. This three-pronged approach is also known as Shneiderman’s mantra. The overview allows the data to be seen all at once. The user can then interact with that data with zooming controls and filters to focus in on a specific aspect of the data. Finally, the user can mouse over a specific item to get details-on-demand.
5. Dynamic Queries. This principle is related to interactivity, immediate feedback, and the ability to filter. For example, when a user specifies a query in a parallel coordinates display, instantaneous feedback is shown as the query is being executed.
6. Focus + Context. With a very large data space, it’s important that the user is able see the overall context when zooming in on specific data. A fisheye distortion is a good example of this principle in action.
7. Animate Transitions. This design technique makes it easier for viewers to assimilate changes in data. The transitions are animated, rather than just showing the beginning state and the end state. Bubbles and motion charts achieve this.
8. Increase Information Density. The goal here is to pack in as much data as possible. “Leave no pixel behind,” says Dr. Spoerri. With a treemap, the user gets a big picture of the hierarchical structure, and is able to zoom in and explore the data in more detail.
Watch Dr. Spoerri’s full webinar here to find out more about integrating data visualization techniques into your engineering classroom. You can also download Dr. Spoerri’s free white paper: Using Interactive Data Visualization to Promote an Active Learning Experience for Engineering Students.Tags: STEM learning, AccessEngineering, data visualization, visual learning, DataVis, visual learners