As a part of my professional development for the academic year, I’m participating in OLC’s Advanced Instructional Design Certificate. For each of the three courses I’ll be taking, I will post a blog recap of lessons learned and how it can be applied to teaching and learning in Lindner. This is the first in the series.
Data visualizations can be an excellent way to convey complex information quickly and reduce the amount of text on a page. They can also be constructed in a way to tell stories to your learner or audience. However, when created without cognitive load or visual design principles in mind, it’s easy to muddy up the message and cause confusion. In my short course called Data Stories, I learned some valuable tips I’ll be using with any visuals I create for a work or classroom setting.
Cognitive Load
Introduced in the 1980’s by Sweller (1988), Cognitive Load Theory, explains that our working memory is limited, but our long-term memory is infinite. Knowing our working memory can only hold a few bits of information, it’s imperative that we construct visuals in a way that doesn’t overload the brain. When we do hit the working memory limit additional information is simply lost. So, if you’ve ever had that feeling of information overload, it’s not you, it’s your working memory hitting its limit.
Having an awareness of the three types can help you improve your visualizations (and your class instruction).
Intrinsic Load
This is load caused by the complexity of the information itself. In a classroom context, calculus would have a higher intrinsic load than addition. This is hard to manage for your learners.
Extrinsic Load
These are typically “extras” that don’t contribute to the learning. Think bright colors, animations, sounds, etc. Easily managed.
Germane Load
How easy is it for your audience to incorporate this information into long-term memory? To some degree, this will depend on your learner’s familiarity with the content, but it can be managed by how you organize and chunk the information.
The ultimate goal is to manage load in a way to keep learners focused on your content and make it as easy as possible for them to learn.
Visual Design Principles
Now that we have a baseline for what impacts how we take in new information, we can think about how we can create visualizations that stick. Ones that both manage cognitive load and communicate the information we need to. There is a wide variety of principles one can employ when creating data visuals, however, we’re going to keep it simple by focusing on three pre-attentive attributes you can use to better organize your information and help the brain process it. When used strategically, these attributes can help reduce cognitive load by directing your learner’s attention to where it matters most.
Multiple attributes can be used to communicate your data story but be mindful. Too much manipulation will add to load! The demo below is using a bar chart, but these attributes can be used on any type of chart.
Let’s take a look at an example of pre-attentive attributes applied to a bar chart of Lindner Summer 2024 online, undergraduate course offerings by department. Here’s our starting point:
Spatial Arrangement
Using this attribute, we can utilize depth (shadowing or three dimensionality) or 2 dimensional proximity. How you order or highlight your information can help tell your story and draw attention to the important elements of your visualization.
My data story here is to highlight and explore departments with the most summer online offerings. We can see the contrast by lining them up from fewest to greatest, or by using depth to make similar disciplines stand out.
Color
Selectively including elements of color will draw attention to specific components you’d like to highlight. Intensity of the color or shading/patterns are options here too.
Adding too much color will dilute the message and leave your learner unsure where to focus.
Form
Similar to color, you can use shape or added marks to draw attention to certain elements. Changes in shape and added marks can signal attention to the main point.
Effective data visualizations can be powerful tools for communicating complex information and enhancing learning in business education. By understanding cognitive load theory and applying visual design principles like strategic use of color, form, and other pre-attentive attributes, instructors can create visuals that optimize cognitive processing, direct attention to critical elements, and facilitate long-term retention. Incorporating these principles into your existing or new visualization will help you manage cognitive load, convey information clearly, and ultimately help your students learn.
As always, feel free to connect with us if you’re interested in applying these concepts to your visuals, or taking a look at how cognitive load in your course content.
References
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285. https://doi.org/10.1016/0364-0213(88)90023-7
Young, S. (2022, January 4). Cognitive load theory and its applications for learning. Scott H Young. https://www.scotthyoung.com/blog/2022/01/04/cognitive-load-theory/
Victoria Buckley 