Application of machine learning in identification of best teaching method for children with autism spectrum disorder

Abstract

A good teaching method is incomprehensible for an autistic child. The autism spectrum disorder is a very diverse phenomenon. It is said that no two autistic children are the same. So, something that works for one child may not be fit for another. The same case is true for their education. Different children need to be approached with different teaching methods. But it is quite hard to identify the appropriate teaching method. As the term itself explains, the autistic spectrum disorder is like a spectrum. There are multiple factors to determine the type of autism of a child. A child might even be diagnosed with autism at the age of 9. Such a varied group of children of different ages, but specialized educational institutions still tend to them more or less the same way. This is where machine learning techniques can be applied to find a better way to identify a suitable teaching method for each of them. This paper first summarizes the research on previous researches on special educational needs of autistic children with a focus on the different behaviors that are involved in communicating with their teachers, peers and understanding capabilities that are most generally absent in children on the spectrum. The paper ends with a conclusion that uses Machine Learning algorithm in comparing different autistic traits with some suitable teaching methods, by analyzing their physical, verbal and behavioral performance. In this way, the proper teaching method can be suggested much more precisely compared to a diagnosis result. As a result, more children with autistic spectrum disorder can get better education that suits their needs the best.