Brain Science and Learning Effectiveness by Bryan Austin

Recap from:  Elearning! Magazine’s Insights Article

Bryan Austin

Why is most corporate learning not optimally effective? Many long-time L&D experts including Elliott Masie, Clark Quinn and Will Thalheimer have frequently lamented that much of the corporate learning they see is not really effective learning, because it is not mentally challenging.

Like them, I’ve found the issue is not that learning professionals don’t know how to create learning that is challenging enough to be effective. Instead, it’s usually the harsh reality of the time, resource and cost constraints within which most learning professionals work.

Every organization provides training to its workforce primarily to improve performance and drive business growth. So let’s discuss some brain science and how to leverage it to create learning that really engages learners and actually improves performance.


A.G.E.S. is an outgrowth of neuroscientific research that examines the link between training retention and how strongly each learner’s brain is activated during training. The A.G.E.S. model focuses on four key categories that reduce distraction during training and dramatically improve retention:

Attention (focus) – Are your employees prone to multi-tasking during training? For the brain to fire at the level required to transfer learning from short-term memory to long-term memory (that is, necessary for retention to happen), learners need to pay close attention during a learning task. Deep focus is a critical factor for learning retention. Employees will engage if they intuitively understand how the learning is relevant to their success. Engagement is making the learning (and the brain!) active, not passive.

Generation (each learner makes his or her own meaning) – This means taking the “active” learning described above to the next level. Learners must generate their own mental links as they learn, not just passively listen. Training will be highly retained when learners create their own mental context to embed the knowledge.

This is most effectively done by involving multiple senses during learning. Not watching or reading, but thinking, listening, speaking and doing. If our training uses multiple senses (like playing an interactive game), we are activating different sets of the brain’s neural circuits to more tightly embed the meaning each learner creates during that learning.

Emotions (better recall) – The stronger the emotions each learner feels during training, the higher their retention of the material will be. These emotions can be either positive or negative. How can your training engender feelings of success? Or fear of failure? Are the majority of your employees inherently competitive? Trigger that competitiveness to create learning experiences that makes them want to win. Or avoid losing.

Spacing (learning blocks) – Remember how you “crammed” for that big test in school? This was effective in massing a large amount of knowledge, but only for short-term memory. Long-term recall/retention improves dramatically when we learn over several sittings. The longer employees must remember the knowledge that makes them successful, the more learning must be spaced out.

How often does your training initiative include a reinforcement strategy that extends well beyond the foundation training? Unfortunately, most strategic corporate training initiatives are more of a “Big Bang” with little to no reinforcement. Spaced repetition is key to learning effectiveness and retention.


The A.G.E.S. model in neuroscience is essential to create more effective learning. The best part: there are now technology solutions built around A.G.E.S. that can enable your organization to produce “A.G.E.S.-level learning” within the time, resource and cost constraints you face.

—Bryan Austin is vice president at mLevel, which produces award-winning game-based learning. More info:

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We’re excited to announce that we are now a part of the Axonify family. To all of those who have supported the MLevel journey, we thank you!  Here’s to the next chapter and to furthering data-driven learning.