Application of Machine Learning in Identification of Best Teaching Method for Children with Autism Spectrum Disorder

TL;DR

This paper explores how machine learning can be used to personalize teaching methods for children with autism spectrum disorder by analyzing their physical, verbal, and behavioral data to improve educational outcomes.

Contribution

It introduces a machine learning approach to identify suitable teaching methods tailored to individual autistic children, addressing the variability in their needs.

Findings

Machine learning can effectively analyze diverse data to suggest personalized teaching strategies.

The approach improves the accuracy of matching children with appropriate educational methods.

Personalized education plans can lead to better learning outcomes for autistic children.

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 autism 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. By analyzing their physical, verbal and behavioral performance, 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.