Cognitive Load Theory and Data Literacy
Can a better understanding of Cognitive Load Theory lead to improving our audience’s data literacy?
As the scope of data continues to evolve while becoming more intricate and detailed, breaking it down into more digestible terms grows more challenging.
Yes, to the data scientist, it all seems so utterly transparent. But that’s their specialty. The moment they see the data, the story is already in their heads.
However, the truth is that the people who benefit most from this information need it to be broken down into its most digestible terms. Of course, I’m not telling you anything you don’t already know.
At the same time, regardless of our experience in this industry, it’s easy to fall into traps. As the data becomes more nuanced, so should our ability to simplify the insights.
Plus, there’s no accounting for learning styles. Each person has their own way of absorbing and applying knowledge. Limiting your storytelling techniques mitigates your ability to reach everyone.
So, we must all stay on top of the industry trends, such as familiarizing ourselves with the application of Cognitive Load to data storytelling.
Table of Contents
- What is Cognitive Load Theory?
- Understanding how the Mind Processes Information
- Delving Deeper into the Cognitive Load
- The Two Ways to Extend Our Working Memory
- How Does Cognitive Load Apply to Data Stories?
- Finding the Balance Between Data and Ink/Pixels
- Prioritizing Visuals
- Be Selective with Your Graphs
- Diversifying Your Data Storytelling Toolkit
- Additional Resources
- Got Feedback or Wanna Talk?
What is Cognitive Load Theory?
When discussing the Cognitive Load Theory (CLT), it’s crucial to point out that it applies to all forms of learning.
In the most general of terms, this mode of teaching centers around the fact that most information is dry and nearly impossible to absorb at first glance. CLT takes that material and gradually imparts it to a learner in a way that can be fully understood.
Understanding how the Mind Processes Information
It’s important to know that it works off the most commonly accepted human model of human information processing.
In this model, the process is seen as having these three main parts:
- Sensory memory
- Working memory
- Long-term memory
Here’s a brief breakdown of these parts:
- This filters out most of the overwhelming sensory information we’re bombarded with every day.
- However, it holds onto an impression of the most essential items for a duration that allows them to transition into working memory.
- This holds approximately five to nine items/chunks of information at any one time.
- Initially, our working memory will either process the items passed down from our sensory memory – or get rid of them.
- Upon its processing of information, the brain then categorizes and moves the info into long-term memory.
- Long-term memory stores knowledge into structures called “schemas.”
- Schemas organize information according to how it’s used.
- This helps us develop concepts such as dog, cat, mammal, and animal.
Delving Deeper into the Cognitive Load
The amount of working memory we can hold at once is often referred to as our “Cognitive Load.”
When we look at a design that’s intricate, unfamiliar, or non-conventional, we experience our cognitive load. It has to do with that natural desire to stick to what’s familiar, combined with our fear and anxiety of what’s new and scary. This creates something of a barrier when trying to acquire new skills or absorb previously unlearned information.
The Two Ways to Extend Our Working Memory
John Sweller, the developer of CLT, explained that working memory has very distinct limitations. Therefore, while teaching, overloading this facet of the brain should be avoided—specifically with activities that don’t help with learning.
There are two primary ways to extend the working memory with CLT:
- Modality Effect
The modality effect is based on the separate processing of visual and auditory information in our minds.
When the working memory is dealing with auditory items, visual items don’t act as an obstacle. This is unlike how it would be in the case of two visible items (e.g., a picture and some text).
For instance, if we present information to an already complicated diagram, it’ll over-extend the working memory. Conversely, adding narration to this detailed diagram would be much less strenuous on the working memory.
Something that becomes an established schema—that’s pretty much 2nd nature, doesn’t take much of a toll on our working memory.
As a storyteller, if you can work the new information into existing knowledge, it can enhance someone’s working memory. This might mean that people need to be pre-trained a foundation of pre-existing knowledge before delving into more challenging, intricate topics.
That foundation of knowledge will mean there are established schemas that make it easier to absorb and digest more in-depth facts.
How Does Cognitive Load Apply to Data Stories?
Our audience’s Cognitive Load will dictate both their desire and ability to engage with your data story.
Yes, I understand that bar charts aren’t the pinnacle of excitement, but they’re as straightforward as it gets.
More specifically, bar charts are an understood schema that people can digest. As such, this reduces that friction that scares people away from learning anything. This notion, or principle, is at the very pinnacle of who we are as data storytellers. We need to remove the barriers of understanding, so keeping it simple (even if it’s a touch dull) is vital to our cause.
Here are some ways you can incorporate Cognitive Load-reduction techniques into your data storytelling:
Finding the Balance Between Data and Ink/Pixels
- White spaces can be used to avoid saturation and direct people’s attention.
- Eliminate visual elements that do not add information to make your data stand out more.
- Use more ink or pixels when displaying data.
- Visual elements that don’t add information should be removed.
- Only once the brain realizes that the content is visually based, instead of text-based, can labels or data points, etc., be noticed.
- Prioritizing visuals prompts audiences into focusing on the most critical parts of the story.
- Smaller data sets don’t need graphs. The audience benefits more from a focus on the intended message that’s meant to be imparted.
- Overly simplified visuals should be summarized into text with a number or stat.
Diversifying Your Data Storytelling Toolkit
As a student myself, I’m only scratching the surface when it comes to applying Cognitive Load to data storytelling. From what I know so far, it’s a highly useful method in which all data storytellers should take a deep dive.
- Cognitive Load – Wikipedia
- Gestalt Principles and Storytelling with Data
- The Data Story: It’s Not About Tools. It’s About The Audience
Got Feedback or Wanna Talk?
Reach out using the form below: