What Educators Need to Learn From Machine Learning.

Machine learning. For someone who dreams of being an educator, this was one field which I did not expect to teach me anything with respect to Education. Never have I been so wrong. The very name should have told me something.

Machine Learning is centered around the idea that you can teach machines how to learn from the data it receives. The difference between teaching humans and machines is that for the machine you have to spell it out to completely. A human child already knows how to learn. A teacher's goal is to enhance that ability. What I present here are some things I learnt from studying Machine Learning.

There are two types of training in Machine Learning(ML). The first is supervised and the second is unsupervised. During supervised training, the computer is given input and shown expected output. Essentially what we are saying is "When you see this do this." The computer then learns on it's own to relate the input to the output. This method is fast, efficient and generally the road people take.

The other type of training is unsupervised. You give the computer some data and ask it "What do you see in this data?" generally speaking. This technique might not teach the AI what you intended but it allows it to find patterns that you yourself might have missed.

What educators must take away from this field is the fact that there are a lot of errors when teaching an AI anything. There is the problem of over training, causing the AI to perfectly know the training data but perform badly on new data which is previously unknown. This is akin to the habit of rote learning among students. While it allows you to perform incredibly well on the data you have been trained on, the performance drops drastically once new data is introduced.

Another facet is that AI are generally geared to do different tasks. If a Feed Forward Neural Network is asked to model a  time series it will fail. Not every student is the same. That does not mean that the current education methods count for nothing. What it means is that the educator needs to be especially perceptive of what the student is best suited for. Especially developing that need and letting the student develop that very skill is what an educator must do.

Coming back to rot learning, let me tell you about neural networks. Neural networks were inspired by the nervous system of humans. The brain is essentially a mass of neural networks. This lets us come to a conclusion that can help us teach better. The only way neural networks can learn anything new is by recalling that information again and again. However they are also incredibly good at building shortcuts. Recalling something the same way again and again slowly loses it's effect.

The challenge for the educator is to allow the student to recall the same information in different ways. One might take the example of the number PI. PI is the ratio of a circle's circumference to it's diameter. It is approximated as 22/7. A better approximation of PI is 355/113. Since PI is irrational it contains every possible number within it's never ending and never repeating fractional part.

With that we have created four different ways for the student to access the number PI. This increases the student's actual recall of the number PI itself. The human memory is unique in the fact that the more things you remember the more you can further remember. Let me illustrate. Your task is to remember this next drawing.

With the following bits of information you will know more about this character, thus giving you more ways to recollect it. Ultimately your memory of this character will be impeccable.

• It is a mandarin character
• It signifies the sun
• Originally it did look like a sun.
• It also signifies a single day