How Can We Apply Cognitive Load Theory To Our Instructional Designs So That Learning In Not Too Hard?
As we’ve all experienced, our brains have a limited amount of working memory – that memory capacity available to learning and other processes. When we reach our cognitive load limit, we’re done learning until we can refresh our brains. We not only stop learning, we may become overwhelmed, and lose motivation to continue. The entire learning program is at risk.
Not surprisingly, this cognitive load limit varies between people, and for an individual. For instance, someone who didn’t sleep well, or is trying to learn in the evening after a long day, will have a different working memory capacity than when they are well rested.
It is pointless to teach to learners who can no longer learn. So how can we apply cognitive load theory to our instructional designs so that learning is not too hard? The nature of e-learning makes it easier than for traditional face-to-face learning.
When learners control their learning schedule, they start when they are ready to learn, and can stop when their working memory capacity is reached. Designs need to accommodate a learner’s need to stop and restart a learning program. A progress indicator is helpful so that learners can easily restart where they left off.
A learning program should be divided into individual modules. Ideally, a module should not exceed a learner’s cognitive load. Understanding your learner’s previous knowledge state by pre-testing is helpful to understanding their learning capability. Including them in your development process by conducting formative testing ensures your instruction is well suited to your audience.
Information should be presented in easy to digest chunks, rather than in large blocks. That’s why books have chapters and phone numbers have hyphens. Think of individual bricks destined to be a house. Each individual one is discreet and easy to manage, and will eventually be an important component of the whole. E-learning authoring capabilities make it easy to chunk our instruction the way our brains learn best.
Once simple concepts presented in individual chunks are learned, more complex concepts that integrate the simple concepts can be presented. Think of the process of building a house from individual bricks. By building upon simple concepts, learners are ready to apply them to more complex knowledge construction. The learner avoids the experience of being overwhelmed with complexity they are not ready to manage. Systematic instructional models provide us a structure to accomplish this design concept.
A learning program needs to include all the learner needs and nothing the learner doesn’t need. Extraneous content takes up the limited cognitive load capacity without contributing to the learning objectives.
However, there is often related information that does not directly apply to the learning objectives, but may be useful to the learners. With e-learning, this information can be provided as supplemental sources, with appropriate links to locations outside of the primary instructional content.
E-learning provides opportunities for rich media content. However, we must use restraint to ensure our learners are focused on what they need to learn, without being overloaded with impressive, yet irrelevant, media content.
Minimize decoding needs
Decoding refers to the cognitive process of converting un-recognized information into recognized information. Think of a spy’s coded message that you need to decode before you can read and understand it. Learners experience the same process when presented with information they must first process to understand and acquire meaning. Learning becomes hard when the learner spends too much cognitive load having to decode instruction that could be provided in an easier form.
Imagine you need to learn how to operate a complex device with which you have no experience. There are two versions of instruction you can choose. The first is written by an experienced operator who assumes prior knowledge and uses terminology specific to the field. The second assumes no prior knowledge and uses common language. The first requires the learner to decode much of the instructions before understanding. The second requires much less decoding, and thus imposes a much smaller cognitive load.
A related example compares written instruction to a video of the device being operated. To use the text, our brains need to translate the abstract text symbols to words, and then assemble these words to comprehend their meaning. A brain watching a video has less work to do because it has specialized processing capabilities that do not require as much decoding as text. This is not to say that video is always better than text, since text has unique and valuable attributes. However, from a cognitive load perspective, we humans have relied on our visual capabilities far longer than the relatively recent availability of written language.
Yet another related example involves reading text vs. listening to a narration. Research has shown that listening to text requires less decoding than reading text.
When learners use your e-learning, they have to understand how your program works. Interface designs need to be simple and predictable. When they are not, learners must waste working memory to figure it out. It should also supply learners with signals to guide them on how to use the program. These concepts become particularly important for learners with limited e-learning experience. These concepts also apply to those with limited abilities. There are many resources available to ensure your designs accommodate the accessibility needs of all of your users.
Consider your past learning experiences where you experienced cognitive overload. What about the instruction caused you to become overwhelmed? How could have the instructional designers incorporated design concepts to improve your experience?
Applying these concepts of cognitive load does not change the intended learning objectives, but make it easier for learners to achieve them. By applying this and other established learning theories, our instruction becomes based upon a solid foundation that ensures our learners are successful.
There are other important learning factors that work closely with working memory, such as long-term memory and schemas. Stay tuned for future articles on these and other topics.
Want to learn more? Look up the works of Paul Kirschner, Richard Mayer, Fred Paas, John Sweller, and Jeroen van Merrienboer.