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.

翻译：良好的教学方法对自闭症儿童而言难以理解。自闭症谱系障碍是一种高度多样化的现象，据称没有两个自闭症儿童是完全相同的。因此，对某一儿童有效的方法可能并不适用于另一儿童。这一情况同样适用于他们的教育：不同儿童需要采用不同的教学方法，但识别恰当的教学方法相当困难。正如术语本身所阐释的，自闭症谱系障碍如同一个谱系。决定儿童自闭症类型的因素有很多，有些儿童甚至可能到9岁才被诊断出患有自闭症。对于这群年龄跨度大、差异显著的儿童，专业教育机构却往往采用大致相同的方式对待他们。而机器学习技术正可应用于此——通过分析儿童的身体、言语和行为表现，相较于诊断结果，能够更精准地建议适合每个儿童的教学方法，从而帮助更多自闭症谱系障碍儿童获得最贴合其需求的高质量教育。