ATD Blog

Science of Learning 101: One (Important) Reason We Can’t Train Everyone the Same

Friday, November 11, 2016

In the last few months I’ve been discussing expertise—the building of knowledge and skills that allow people to perform at much higher levels of performance in their organizations. I’ve also explored how the mental effort (cognitive load) required in some training or learning situations makes it hard to learn. Consequently, we need to reduce pointless (extraneous) cognitive load in these situations to allow people to expend more mental effort on learning and not be overloaded.

If these are new terms to you, you may want to skim through my posts that mention expertise and cognitive load/mental effort. Then come back. I’ll be waiting for you, I promise.

Today, I’m going to connect some important dots between expertise and cognitive load. This is a truly fascinating and valuable concept in learning sciences because it helps us tailor learning to two audiences who have very different needs: people new to a topic and people with much more understanding of that topic. Cognitive science tells us why their needs are so different. We’ll start with a brief recap on how memory works.

Memory and Chunks

All new learning must make it through working memory (WM) to be stored in long-term memory (LTM). WM processes information in chunks to store in LTM in schemas (I described this process in a recent post.) WM also retrieves information from LTM when we need to remember something for use on the job and elsewhere.

Here’s a simplified picture of this process (Figure 1).

Figure 1. Simplified flow of information in and out of WM and LTM 


(Source: Patti Shank’s Make it Learnable series,

WM can only handle a few chunks of new information at a time, and it can only hold those chunks briefly. People who are new to a topic have less well-formed schemas about the topic in LTM. They are developing those schemas as we teach. When people know very little, the chunks in WM need to be small. (Part of our job as learning designers and instructors is to make sure that we design the right size chunks for the people learning.) This means we need to teach more slowly and make sure that there aren’t misunderstandings. In other words, we must make sure that the schemas they are developing are accurate.

People who have more prior knowledge about the topic have more developed schemas in LTM memory and because of this, the chunks through WM can be larger. Because of this, we can generally teach more quickly because of the more well-formed schemas.

It’s important to understand that what makes the difference is in these two cases is the amount and accuracy of prior knowledge, not the amount of time that someone has been doing a job. Many people do the exact same job for 20 years and do not gain expertise. Some people gain real expertise over time. (Check out more on this in another post.)


The big idea: When people have more prior knowledge about a topic, WM can handle larger chunks and the design of instruction should be different than when you are teaching people who have no or very little knowledge about the topic. In the learning sciences, we call this the “expertise reversal effect.”



Let’s explore what should be done differently when training people who have more expertise and others who have less expertise.

For starters, the table below lists a few of things research has helped us understand about the expertise reversal effect.

People who are less expert on a topic…

People who are more expert on a topic…

  • Need to go more slowly due to lack of prior knowledge
  • Can often proceed at a faster pace because of advanced prior knowledge
  • Tend to learn less from complicated text content
  • Can learn more easily from complicated text content
  • Need more explanation of images
  • Do better with no explanation for images they understand
  • Learn best from worked examples
  • Learn best from problems
  • Learn well from training content
  • Often learn best from other experts

But how do these differences apply to a specific course? A look at a specific example can offer some insight. 

For instance, in an anti-bullying course for organizations, staff would learn about specific laws that may become applicable, how to apply those laws in various situations, and how to prevent workplace bullying. Designers would need to determine who is new to this topic and who is more expert. Because management has more burden than staff in this situation, they would be required to become more expert. They would need to first learn what staff needs to learn and first demonstrate that level of knowledge.

A staff-level course would primarily use examples (or scenarios). Scenarios would cover many different types of workplace bullying to build a schema of the types of workplace bullying that are unacceptable and the line between bullying and acceptable behavior (such as disagreements and performance management from management, since research shows that most workplace bullying comes from supervisors and managers). Scenarios would be debriefed to correct misunderstandings.

To build higher levels of expertise, managers would be exposed to more complex and difficult scenarios involving supervisors and managers and staff with increasingly difficult lines between acceptable and unacceptable behavior. They would also work on real and realistic problems with additional debriefs with experts over time because expertise takes time and practice. As new situations arose, the expert group would convene to work on these real problems.


When learning expert skills, we often learn from people who are already expert in that topic. This is one of the reasons why, for example, lawyers teach legal topics to managers, the best managers teach leadership to other managers, and the best tech people teach tech topics to less-advanced tech people. It helps if experts are taught how to train and facilitate because these skills need to be learned.

Moving Forward

I hope that this discussion about the differing needs of people who are new to a topic and those who are more expert helps you see why it is best to use different training techniques and resources for each—as they have different needs because of differences in prior knowledge.

In the next few posts, we’ll explore how to build good worked examples for people with less prior knowledge. It’s one of the most important instructional design tactics we have for people with less knowledge, and I don’t see them used as often as they could be used. I would love to show other designers’ examples of these, so get in touch if you have something to share. (Find me via my website.) Worked examples are structured step-by-step demos of how to solve a problem. I am mostly looking for organizational learning examples of worked examples: tech, leadership, compliance, anything you teach in your company.


Blayney, P., Kalyuga, S., & Sweller, J. (2015). Using Cognitive Load Theory to Tailor Instruction to Levels of Accounting Students’ Expertise. Educational Technology & Society, 18(4), 199–210. 

Sweller, J., Ayres, P. L., Kalyuga, S. & Chandler, P. A. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23-31.

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review 19. 509–539.

Kalyuga, S. & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology. 96(3), 558–568.

Kalyuga, S., Rikers, R. & Paas, F. (2012). Educational implications of expertise reversal effects in learning and performance of complex cognitive and sensorimotor skills. Educational Psychology Review, 24, 313–337.

About the Author

Patti Shank, PhD, CPT, is a learning designer and analyst at Learning Peaks, an internationally recognized consulting firm that provides learning and performance consulting. She is an often-requested speaker at training and instructional technology conferences, is quoted frequently in training publications, and is the co-author of Making Sense of Online Learning, editor of TheOnline Learning Idea Book, co-editor of The E-Learning Handbook, and co-author of Essential Articulate Studio ’09.

Patti was the research director for the eLearning Guild, an award-winning contributing editor forOnline Learning Magazine, and her articles are found in eLearning Guild publications, Adobe’s Resource Center, Magna Publication’s Online Classroom, and elsewhere.

Patti completed her PhD at the University of Colorado, Denver, and her interests include interaction design, tools and technologies for interaction, the pragmatics of real world instructional design, and instructional authoring. Her research on new online learners won an EDMEDIA (2002) best research paper award. She is passionate and outspoken about the results needed from instructional design and instruction and engaged in improving instructional design practices and instructional outcomes.

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