ATD Blog

Science of Learning 101: Key Learning Science Concepts and How to Apply Them

Monday, December 14, 2015

I recently had a conversation with a prospective client who was looking for a speaker. “Perhaps I could do a session debunking common learning myths, including learning styles,” I mentioned. We were on Skype, and her tone went from attentive to cold. “We spent weeks discussing how to adapt to learning styles in one of my graduate classes,” she replied. 

Since that conversation, I’ve seen numerous mainstream L&D sources discussing the “learning styles myth.” Not because people don’t have preferences for how they like to learn, but because research has clearly shown that self-reported measures of learning aren’t valid (see the resources below for more) and self-reports aren’t correlated with how people do learn. 

What does this mean, really? Should we design learning experiences and resources however we want? No. It primarily means that we should design so people can more easily learn. Learning sciences tell us how to accomplish this—not fads. 

I started blogging for ATD about the science of learning (SoL) in December of 2014. A year later, I thought I might recap a few of the highlights of the series (so far.) 

So People Can More Easily Learn 

In my first post, in December 2014, I gave a definition of the science of learning: “An interdisciplinary field of study that examines how people learn, and how the L&D field can improve learning and instruction.” 

I would assert that learning professionals interested in the science of learning care deeply about making learning easy, are willing to change practice while learning about learning, and are invested in always learning more. The good news is that while the science of learning is interdisciplinary and often complex, it is quite possible to learn the foundations—and more, if you want. 

Because deep learning is the ability to apply what you know in flexible circumstances, it’s critical that we avoid teaching people how to simply recall information. Today’s jobs require more. Employees  must pay attention to what they have to be able to do, as well as all the circumstances under which they have to be able to do it. If all someone is doing is following directions or reading from a script, they don’t really understand what they are doing. 

Deep learning goes further. It requires understanding what is really going on, applying knowledge in different circumstances, being able to solve difficult problems, recognizing what resources to use, identifying how to keep learning, and so on. This is how far people need to go to do a job well.


Unfortunately, training teaches people how to follow directions or a script. Then, when the circumstances aren’t like those directions or the script, they have no idea what to do. 

In my April 2015 post, I delve into another problem we see in training today is developing and delivering too much content. Indeed, the study of cognitive load contends that people have mental “bandwidth” restrictions. In other words, our brains can only process a limited amount of information at a time. Therefore, when we overload people with too much information, too many visuals, or too complex multimedia, they don’t learn as well or they stop trying. 

Another significant problem for L&D is that we build training as if everyone is starting from ground zero. In my May 2015 post I discuss research that shows when we connect learning to prior knowledge, people learn more and retain that knowledge more easily. For adults, all learning is a refinement of prior knowledge (what we already know). Of course, there can be issues with prior knowledge. Some people have inadequate prior knowledge and some have inaccurate prior knowledge and there are specific ways of handling these circumstances. 

What does this mean for developers? We should determine what people know, help people recollect what they know, fill in gaps as needed, and fix misunderstandings. For example, in the second set of classes on analyzing delinquent patient accounts, we can assume that people have forgotten some of what they learned in the first set of classes. What can you do to help them recollect what they might have forgotten? (Or what might you do to help them remember in between the two classes?) 

What Should We Measure? 

If we are going to take the time to build instruction (a resource intensive activity), we are usually looking for evidence that people learned. In my February 2015 blog post, I discussed direct and indirect evidence of learning.  Direct evidence includes tangible work products like product knowledge and Excel spreadsheets. Indirect evidence includes people’s perceptions and rankings such as supervisors’ ratings or customers’ survey data. An ideal assessment might combine direct and indirect measures. This is called “triangulation” of evidence. 

What would you consider direct and indirect evidence of learning for the course on analyzing delinquent patient accounts? Direct evidence might include actual results from stepwise analysis, while indirect evidence could include supervisor perceptions about how well people are doing three weeks after the last part of the course. What we are talking about falls under the realm of “assessment.” 


Too often, we leave out questions about the quality of the actual training. I discuss Robert Brinkerhoff’s book, Telling Training's Story: Evaluation Made Simple, Credible, and Effective in my March 2015 post. Brinkerhoff’s model identifies the most and least successful trainees in order to find the impact from training in such information. It’s a very rational approach, while putting you in touch with actual trainees after the training. 

The Best Outcomes from Training

 In 2015, I used three posts to discuss the terrific study by Eduardo Salas and fellow authors in their research study, The Science of Training and Development in Organizations: What Matters in Practice. The study used meta-analyses (statistical methods for combining and contrasting results from numerous research studies) and found that when the science of learning is used, training has better outcomes. 

For example, the research found that needs analysis (July 2015 post)is one of the best things that L&D professionals can do before training to ensure positive outcomes. Meanwhile, their research examines powerful strategies that aide learning during training (August 2015 post). Finally, according to the study, good communications with supervisors is one of the best practices to ensure positive outcomes after training (September 2015 post). 

Help! (Please) 

Not surprisingly, I didn’t get the speaking gig that I mentioned at the beginning of this post. 

I’d be most grateful if you’d let me know if 1) you read my blog and if it’s valuable to you and 2) whether you want to learn more about key principles of learning and how to apply them. That’s what I’m hoping to concentrate on in the new year. 

Additional Reading 


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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