3 Ways Neuroscience Improves Learning Design

Much of what we do on a daily basis, your strengths and weaknesses can be attributed in some way to the nervous system. Neuroscience involves the study of the brain, the nervous system and its impact on behavior. It may surprise you that although we tend to think we have full control of our brains, that may not be the case. This article highlights some findings in neuroscience that can help learning designers maximize the capacity of our brains for learning.

Feedback Matters

There’s an experiment known as the Super Mario Effect run by YouTuber Mark Rober where 50,000 viewers took on his challenge to learn coding by beating a puzzle. It turns out Rober split viewers in two groups and provided them with different feedback for the same tasks. One group was given the feedback “That didn’t work. Try again!” while the other group’s feedback said “You lost 5 points”. Surprisingly, 68% of the viewers performed much better with the “Try again!” feedback.

Mark Rober 2018 TEDx Talk

What’s the lesson here? According to Neuroscientist Dr. Andrew Huberman, the focus adjustment learners experience in games facilitates a higher number of repetitions and therefore the motivation to learn. So, instead of punishing learners, reward them with the opportunity to beat a challenge.

Do Nothing After Learning

Yes, this is a cool one. A couple studies suggest that after a learning event, the brain replays the sequence of neuron activations required during such activity. It does this in sleep and also when awake. As a designer, you may want to consider the possibility of short 10-min meditation breaks after lab exercises to maximize this autonomic brain response.

Training Density Makes a Difference

Density of training refers to the focus intensity of a learning activity. In this case, Dr. Huberman refers to the Ultradian and Non-ultradian learning cycles which describe a lapse of 90-minutes in which learning happens while we are asleep. He also mentions that learning can be very effective in shorter bouts as long as the opportunity for repetitions is vast in the learning activity. This means designers can apply a microlearning strategy to maximize these cycles and increase retention.

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