Understanding How We Learn That – Three Analogies to Help You Visualize Success
A good friend of mine was concerned about how hard it was for him to get through his doctoral research classes. He was beginning to panic a little about how difficult the material was. A support group of fellow students got together to offer him the following pieces of advice in order to help them appreciate the challenges of the learning.
Here are three analogies he was offered and maybe they will give you some insight in your own educational career.
Three analogies can help you understand how we learn: martial arts, rifle marksmanship and neural networks.
In martial arts the apprentice has to trust in the sensei that the drills and forms he learns through repetition will lead to deeper knowledge at some point. He trusts the sensei because of the sensei’s reputation and demonstrated mastery of skills. Trust and hard work are the essential elements to get through the uncertainty.
In rifle marksmanship we can talk about shooting all day long and the principles that will make you a great marksman, but until you actually get in there and do it and feel the shock and hear the noise and smell the gunpowder you don’t know anything about marksmanship. So there is an inescapable experiential quality to education.
In neural networks we can create powerful control systems by linking together nodes that each have a little bit of computing power, but which taken together have a great deal of power. When you first set one up you create a random initial state in all the nodes, then provide initial inputs and specify what the successful output should be. On the first pass through the network the random functions modify the inputs and you see what you come up with. You compare that result to the perfect answer and find th the error quantity. You then pass the error backwards through the network and make a modification take each processing node. You repeat that cycle many many times and each time make a learning adjustments to the processing nodes. Because of the feedback and learning function the network gradually gets smarter until at some point it’s very competent.
Sometimes I think that’s how we learn through our efforts and the feedback of others, adjusting our efforts to approach the correct answer which in our case is polished doctoral work. The funny thing about neural networks is that there is no formula ahead of time that can be specified to get you to the right answer. It is only through cycles of trial and error and feedback that it begins to get it right.
Adult learning is very challenging because of how set we are in our ways. The habits and practices that make a successful in our daily work life may not translate well into graduate level education. The use of analogies can sometimes help us make a bridge to the new learning style that is required.