AI is teaching us how to learn
The exact figure is 15.87 per cent; it means that you need to fail 15.87 per cent times to optimise your learning.
Being successful is a process that goes through many failures. But we don't know exactly how many failures it takes to be successful.
But now researchers taking inspiration from machines are suggesting that a 15 per cent failure rate is the stage where the learning is the fastest. If we figure out at what stage we will be learning the most we might be able to speed up our steps towards success.
How can it be used?
The exact figure is 15.87 per cent; it means that you need to fail 15.87 per cent times to optimise your learning. Knowing the perfect spot for fastest learning can be used for training courses, teaching in classrooms, and at any other place where learning happens. Scientists suggest that this is the sweet spot between finding something too easy and something too difficult.
Robert Wilson, a psychologist from the University of Arizona, and his colleagues ran a series of machine learning experiments. These included teaching computers how to do simple tasks, such as putting patterns into categories or recognising the difference between odd and even numbers.
On the flip side, computers are fastest when they are making the right call 85 per cent of the time. The paper was published in Nature and also mentions that the figure seems to match up with previous studies carried out with animals too.
The human connection:
The team says that this 15/85 per cent split is more likely to apply to humans when it comes to perceptual learning as we also learn through experience and examples.
But, before we draw any conclusions, the study only covers basic and binary choices. So we cannot conclude that obtaining 85 per cent should be the target to score in future exams. More research is required to figure out how this applies more broadly to education.
Researchers say that this is a starting point for finding the balance for learning so that things don’t get boring if they are too easy, which will make us give up. The expectation is to expand this work and find more complicated forms of learning.
Wilson says, "If you are taking classes that are too easy and ace-ing 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."
"These ideas that were out there in the education field – that there is this 'zone of proximal difficulty', in which you ought to be maximising your learning – we've put that on a mathematical footing," he added.