Debunking Myths in Instructional Design, Part I
Myth 2: More Information = Better Learning
Like any field, instructional design is rife with persistent myths about learning that, no matter how hard we try, we can’t seem to shake. Some of these myths have even become part of our colloquial vocabulary, such that everybody thinks they have the key to understanding how people learn. And if only teachers and instructional designers would implement these strategies, everyone would just learn everything taught to them no problem!
Sorry, but it’s just not that easy.
Now listen, I’m not saying I’m an expert in the cognitive processes involved in learning. I wouldn't be able to tell you the difference between the frontal lobe and the ganglia if my life depended on it. What I am saying is that there’s a lot of research out there that debunks many of the most common sayings about teaching and learning, and it benefits all of us to dig deeper into things that seem like “common knowledge.”
Particularly as so much education legislation is built upon these faulty beliefs, which has broad and scary implications for vulnerable populations.
Disproving myths about ID allows us to make room for new ways of thinking about teaching and learning, rather than relying on the outdated, conventional baloney we’ve been eating for most of our lives. Plus, if I could make a career out of debunking things I’d be happy as a clam, so really this is more for me than anything else!
Anyway… let’s get into it.
Myth 1: Learning Styles
Learning styles aren’t a thing. I know, it’s weird, it feels wrong, but I swear to you I’m not making this up.
The theory of learning styles is that everyone learns in different ways, with each person falling into a type or “style” of learning: visual, auditory, or kinesthetic.
For example, a visual learner might prefer to learn through diagrams and images, an auditory learner might benefit more from listening to lectures or podcasts, and a kinesthetic learner might prefer hands-on activities or physical demonstrations.
If learners are given opportunities to learn concepts in their given style, they’re more likely to be engaged and retain information, or so the theory goes.
Unfortunately, there's little to no evidence supporting this claim and it has been widely debunked.
Instead of stuffing learners into neat little boxes and worrying about providing ALL of your content in EVERY format possible, it's more effective to focus on evidence-based instructional design strategies that cater to diverse learners and incorporate multiple modalities when appropriate.
My grad school department was *obsessed* with the concept of multimodality, so I learned a lot about it, and many of my colleagues were enthusiastic supporters of this line of teaching.
Multimodality is an instructional approach that incorporates multiple modes of information presentation. Unlike learning styles theory, this approach is grounded in research which demonstrates that understanding, retention, and application are all enhanced when various sensory formats are deployed.
For example, a multimodal instructional approach for teaching a new concept might include a combination of a written explanation, a video lecture, an infographic, and an interactive simulation. By presenting the same content in various ways, learners have multiple opportunities to connect with and internalize the material, which can lead to a more effective and inclusive learning experience.
This might sound pretty similar to learning styles theory, but the two are actually quite distinct. Here’s why:
Multimodal theory doesn’t put people into specific boxes. That is, I’m not *just* an auditory learner. I’m a learner who will benefit from all kinds of content, depending on the context.
Learning style theory overemphasizes the personalization of content based on preferences. Rather than worrying about personalizing every learning opportunity—which is not only extremely difficult, but also massively time-consuming—simply vary the format of delivery from one lesson to the next.
Universal Design for Learning
A more recent (relatively speaking) theory has made its way into the mainstream which neatly encompasses some of these concepts: Universal Design for Learning (UDL).
UDL is an educational approach based on the principles of universal design, aiming to make learning accessible and engaging for all learners, regardless of their abilities, backgrounds, or learning preferences.
UDL focuses on three primary principles:
Multiple means of representation: Presenting information and content in various formats and modes, ensuring that learners can access and engage with it in different ways. This principle aligns with multimodal theory, as both emphasize the importance of using diverse methods of information presentation to cater to a wide range of learners.
Multiple means of action and expression: Providing learners with various ways to demonstrate their understanding of the content, and allowing them to choose how they express their knowledge. This principle acknowledges that learners have different strengths and preferences in terms of communication and problem-solving, and it encourages flexibility in assessment and expression.
Multiple means of engagement: Offering different ways for learners to engage with the material and fostering motivation, interest, and persistence in the learning process. This principle recognizes that learners have diverse interests, motivations, and learning preferences, and it encourages the use of varied strategies and resources to keep them engaged and invested in their learning.
By incorporating the principles of UDL and the grounding of multimodal theory into instructional design, we can create learning experiences that more effectively serve a wide range of learners.
Myth 2: More Information = Better Learning
Now, if you’re a learning design professional, you probably don’t need to be told this. In fact, debunking this idea is one of the most common tasks we do in our jobs.
For the uninitiated, here’s what I mean: Inevitably, one of the very first things I do when I get a new job and start designing or updating learning content, is cutting the content in half. Often, it’s much more than that. Then I have to have a conversation with my boss that goes something like this:
Boss: Whoa! This how-to article is like, 500 words. Where’s the rest of it?
Me: That’s all of it. I was able to summarize all of the important information in a much shorter article.
Boss: But don’t learners need to know X, Y, and Z?
Me: Yes, absolutely! Which is why we’re going to make individual how-to articles for X, Y, and Z.
Boss: No, I don’t think that’s right. Let’s add all of the other content back in so that learners can get all of the information they need in one spot.
Me: I know it seems like we’re cutting out a lot of important information, but the research shows pretty clearly that learning content should be focused on just one thing at a time. It’s called cognitive load theory, and I’ve had really great results following it in the past.
Boss: If you say so…
[fast forward 60 days]
Boss: Dang, you were right, our shorter articles are getting much more views and interactions than they were previously!
Me: *smiling and NOT saying I told you so*
All of this to say (and cheeky example aside), the misconception that more information leads to better learning outcomes is a ubiquitous issue that’s hard to beat.
Cognitive load theory states that learners can only process a certain amount of information at one time, so it’s critical to focus on just one cognitive task at a time.
So let’s say you wanted to learn how to apply and synthesize the leading theories in cinematic critique. First you’d need to know what cinematic critique even is, then what the leading theorists believe, and then you can synthesize and apply all of that information. Trying to do all of this at the same time overloads your cognitive process.
Another example: Imagine you’re building a course that will onboard new users to your platform. Instead of one long course that goes over every step of the sign-up and navigation process, break all of that information into shorter chunks. Then—and this is the important part—order the material so that learners know what they’re about to learn, what the platform even is/does, and then how you can get value from it.
In other words, a learner cannot seamlessly take in both the definition of something and how to apply it at the same time. These are separate learning events. Trying to do it all at once adds in what’s known as “extraneous cognitive load.” (Importantly, this can also happen when you include images that are not directly related to the content, or voice and text at the same time.)
To decrease extraneous cognitive load, your goal should be to focus only on the material and tasks at-hand.
To do this, refer back to your learning objectives from the beginning of your writing process. Take it all one step at a time. Break everything—and I mean everything—down into as many smaller parts as possible. Reorder them so that the learner can first tell you the definition of something, then how to apply it and get results.
Myth 3: The Digital Native
I’m just gonna come right out with it: younger people are not inherently better at technology than their older counterparts. It may seem in direct violation of everything you know to be true, but that’s how these myths perpetuate so deeply for so long.
Let’s unpack, shall we?
The concept of the “digital native” posits that anyone born before 1980 hasn’t grown up with modern technology and therefore is doomed to be a “stranger in a computer-based strange land.” (Nature) The myth goes on to suggest that younger generations are inherently skilled in using technology.
Like most myths on this list, there is just no empirical evidence to support this theory.
First, the myth is harmful to young people as they receive less and less support for learning new technologies.
Second, as I'm sure you can imagine, it has negative effects on older groups of people who are assumed to be less tech savvy. Indeed, the rhetoric often paints people born before 1980 as completely hopeless and helpless when it comes to learning new tech.
And finally, the digital native myth can have immensely negative effects on marginalized populations who already receive so little support, which in turn only compounds social and economic inequity.
The solution here is fairly simple: Don't just assume that the age of your audience means they need more or less assistance with learning a new concept. Pretend that all learners are learning from the ground level, and build up from there. Learners who already know this stuff can pass over it, but those who don't will feel much more confident and satisfied with their training experience.
The important takeaway with all of this is that we as instructional designers have a duty to consider individual differences in our learner’s needs—and the empirical evidence that backs our ID processes. We should aim to provide appropriate support for all learners that is supported by actual science, not mainstream myths about how people learn.
This is the end of Part I of this debunking series. Part II will be up soon, and in it, I’ll tackle the common myths of multitasking, engagement, and the dreaded 10,000 hour rule. It’s gonna get juicy.
‘Til next time! 👋
The digital native is a myth. Nature 547, 380 (2017). https://doi.org/10.1038/547380a
Kress, G., & Van Leeuwen, T. (2001). Multimodal discourse: The modes and media of contemporary communication. London: Arnold Publishers.
Matthew James Capp (2017) The effectiveness of universal design for learning: a meta-analysis of literature between 2013 and 2016, International Journal of Inclusive Education, 21:8, 791-807, DOI: 10.1080/13603116.2017.1325074
OECD/Rebecca Eynon (2020), "The myth of the digital native: Why it persists and the harm it inflicts", in Burns, T. and F. Gottschalk (eds.), Education in the Digital Age: Healthy and Happy Children, OECD Publishing, Paris, https://doi.org/10.1787/2dac420b-en.
Yfanti, A., Doukakis, S. (2021). Debunking the Neuromyth of Learning Style. In: Vlamos, P. (eds) GeNeDis 2020. Advances in Experimental Medicine and Biology, vol 1338. Springer, Cham. https://doi.org/10.1007/978-3-030-78775-2_17