In May we looked at some of Richard Mayer’s research-based principles of learning and training design. Another area of current thought in learning and brain science – closely linked to ideas around those discussed in May — relates to Cognitive Load theory. Prominent names here are John Sweller, Frank Nguyen, and Ruth Clark; I’ve included a reading list below.
The basics of the theory aren’t hard: it pretty much posits that there’s only so much new information the brain can process at one time. Why should we care? Because so often designers and trainers simply overload learners, hurting learning and learner motivation, and thereby undercutting the very thing we say we want to accomplish. Understanding cognitive load theory depends on an understanding of memory; in particular, the concepts of working memory and long-term memory.
Working memory isn’t to be confused with the idea of short-term memory, which is remembering something for a little while. It’s more about the amount of information the brain can hold and manipulate at once – what we can manage at a given point in time. Research data disagrees on the specifics, like the exact limit, issues with processing numbers and words at once, and indications that working memory capability improves with practice. But it’s easy enough to see the basic idea for yourself.
For instance, work this problem in your head:
Now, try this one in your head:
In the second instance, the “problem” is the same as the first. The processing is the same. The same skills are used. But for most of us, the second sum just asks for more than the brain can handle; we can’t hold and manipulate that much information at once. And my guess is that most of you didn’t even attempt to work the second problem: if you’re like me, the very sight of it destroyed your motivation to try and solve it. [Are you a math geek with lots of tricks up your sleeve? You’ve already figured out how to reduce your own cognitive load, and the second problem was no problem for you. OK, then, imagine a typical “wall of words” e-Learning screen and the effect it might have on learners and learning. That’s how the long math problem looks to them. More for you in the “novice and expert” section below.]
Long-term memory, on the other hand, is related to time — how long something stays with us and can be called upon. It’s presumably limitless, but it is also a funny, seemingly capricious thing. One of my favorite quotes is from American writer Austin O’Malley, who said, “Memory is a crazy old woman who hoards colored rags and throws away food.” It speaks to why we can remember a particular dress our first-grade teacher wore, but we can’t recall what we had for lunch last Tuesday.
So the trick for trainers and instructional designers: providing instruction in such a way that learning in working memory can be moved to long-term memory where it will, we hope, be called upon as needed, possibly in a not-very-conscious way.
Assuming other things are equal (but they never are) like learner attention, learning environment, and interruptions, there are a number of things designers and trainers can do to reduce cognitive load in their courses.
Chunk the content
Pay attention to how much information the learner is accessing at any one time. Consider the “rule of 7.” George Miller, an early researcher in cognitive load theory, suggested that the largest number of discrete pieces of information the brain could mange was seven, plus or minus 2. Think about things like phone numbers (in the U.S.: 123-456-7890; that is, groups of 3 + 3+ 4 numbers), U.S. Social Security numbers (123-45-6789; that is, groups of 3+2+4 numbers), and worldwide postal codes.
Be aware that “chunking,” though, isn’t just relative to breaking content apart in some scattershot way: the chunks need to represent something meaningful. In more academic terms, we intend chunking to help the learner build and add to the schema, or framework for organizing and making sense of the information.
Also, employ simple visual design basics: Use white space and fonts as organizing tools, and make use of meaningful (not decorative) images that teach.
This is, essentially, another form of chunking, and it is usually easier for those creating asynchronous e Learning to justify to stakeholders. Those working in traditional or virtual classrooms often see training events made longer in order to have learners travel to a training site only once, in order to minimize instances of pulling essential staff from the work site, and in order to maximize opportunities to virtually accommodate time-zone issues. The danger of that should be apparent by now: Cognitive overload. When possible, use modules rather than pack everything into a one-shot show.
Consider Novice and Expert
So often training and e-Learning are designed as one-size-fits- all endeavors. This may make sense in terms of economics and in getting something developed and launched quickly, but ultimately can hurt the learning experience. What is adequate for the expert may overload the novice, while what is comfortable for the novice may bore the expert. In the arithmetic example above, the “math geek” type likely has learned tricks or has practiced this sort of thing enough to manage the cognitive load it presents. Me? I can barely do the 2 X 2 addition in my head. Consider developing different programs for different levels of learners, or at least branching tracks or modules leading from critical content to different levels of practice based on expertise. Clark, Nguyen & Sweller (2006) suggest providing novices with worked examples and experts with problems to solve, gradually fading the worked examples as novices move toward expertise.
Remove extraneous material, including that which is there only for “interest” or decoration
In my workshop on this I ask participants to think of the brain as having only so much “bandwidth”; information enters the brain through two channels, visual and auditory. The channels can carry only so much data at a time. (Add to this additional but less formally recognized “channels” that sometimes come into play during learning: touch, as with a machine assembly task, and sometimes smell, as with cooking lessons.) Removing extraneous material is hard to do, especially as every stakeholder will have ideas about what is “extraneous.” For more on that, visit my earlier column on finding your critical 20% of content (http://www.learningsolutionsmag.com/articles/472/nuts-and-bolts-find-your-20 ).
Once you’ve culled out the critical content, look at the design itself. Is it a pretty but irrelevant template, with ¼ of the space taken up by irrelevant elements? Lose the template. Are there pretty, irrelevant, and unhelpful clip art, photos, screen/slide transitions, and sounds? Lose them. They don’t engage anybody, and furthermore they confuse people trying to make sense of why the items are there. I am thinking of a screen I once saw: a cartoon of an ant lifting a barbell illustrated the idea of employee loyalty and it was accompanied by a “wind chime” sound with every screen change … the learner didn’t have a chance.
You can’t control how intrinsically hard the task is. You have little control over learner motivation and effort to learn it, but you have complete control over extraneous information.
I’ve observed with past columns citing research and data that some people bristle at ideas differing from their own. While I certainly believe in the role of intuition and gut feeling in good design, I also think we need to pay attention to what the data show as working or, especially, harming learning.
Think of it this way: The question shouldn’t be, “How can I teach this?” but “How can they learn it?”
This Was Intended to Be An Overview. Want more?
Clark, R. , Nguyen, F., & Sweller, J. (2006). Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load. San Francisco: Preiffer.
Clark, R. & Mayer, R. (2007). Elearning and the Science of Instruction: San Francisco: Pfeiffer.
Miller, G.A. (1956). "The magic number seven plus or minus two: some limits on our capacity to process information." Psychological Review 63 (2): 81–97. doi:10.1037/h0043158. PMID 13310704.