TUCSON, Ariz. — English poet Alexander Pope famously wrote “to err is human” centuries ago, and the saying still holds true today. Despite the fact that we all would love to immediately succeed in everything we do, the simple truth is that just isn’t possible. Failure isn’t all bad, though, and often works as a great motivator and essential ingredient to learning a new skill or piece of knowledge. So, what is the perfect amount of failure to facilitate learning? According to a new study, it’s 15% of the time.
Another way to interpret the findings would be that learning is optimized when we get answers right on a given subject 85% of the time.
Educators and scientists have long believed that there is a “sweet spot” of sorts when it comes to learning and failure. A simple task that doesn’t challenge us at all isn’t going to result in any real learning, and conversely a super challenging task will likely only result in frustration.
“These ideas that were out there in the education field – that there is this ‘zone of proximal difficulty,’ in which you ought to be maximizing your learning – we’ve put that on a mathematical footing,” explains lead study author Robert Wilson, assistant professor of psychology and cognitive science at the University of Arizona, in a release.
Wilson, along with co-researchers from Brown University, Princeton, and the University of California, formulated their “85% rule” hypothesis after performing various machine-learning experiments. More specifically, they “taught” computers how to perform simple tasks such as how to identify and separate different patterns into set categories, or how to separate photographs based on odd versus even numbers.
The computers were able to pick up the tasks and learn most efficiently when they responded to the tasks with 85% accuracy. Besides just computers, the research team also analyzed previous research that had focused on animal learning. They discovered that the 85% rule largely held true among animals as well.
“If you have an error rate of 15% or accuracy of 85%, you are always maximizing your rate of learning in these two-choice tasks,” Wilson says.
The study’s authors say that their findings are especially relevant regarding perceptual learning, or the process of gradually picking up skills and learning through experience and examples. They gave an example of a radiologist learning how to accurately identify tumors in an x-ray.
“You get better at figuring out there’s a tumor in an image over time, and you need experience and you need examples to get better,” Wilson explains. “I can imagine giving easy examples and giving difficult examples and giving intermediate examples. If I give really easy examples, you get 100% right all the time and there’s nothing left to learn. If I give really hard examples, you’ll be 50% correct and still not learning anything new, whereas if I give you something in between, you can be at this sweet spot where you are getting the most information from each particular example.”
These findings may be interpreted by some as encouraging a “B” average in academics, but Wilson and his team say they aren’t quite prepared to come to that conclusion. This study focused on straight-forward, simple problems with a clear cut correct and incorrect responses. Meanwhile, in higher levels of academia, the line between correct and wrong is often more blurred. However, the study’s authors still believe their research could prove useful to educators and students alike.
“If you are taking classes that are too easy and acing them all the time, then you probably aren’t getting as much out of a class as someone who’s struggling but managing to keep up,” Wilson concludes. “The hope is we can expand this work and start to talk about more complicated forms of learning.”
The study is published in the scientific journal Nature Communications.