Teachers monitoring children’s development involves a lot of work. However, it’s such an important task. I think the future of education will see a greater reliance on machine vision systems and artificial intelligence. With the aim to enrich the learning experience. Education in the future will hopefully become more personalized to each student. All of this requires so much time, the only way to do it effectively is by unlocking the power of computers.

Teachers have a lot to juggle, setting and marking work. If machine vision systems could make their lives easier identifying which students have disengaged for learning they could act much quicker. The issue with disengaged students is they can be troublesome for the rest of the class. But hopefully, when issues are identified early on, they can hopefully be remedied. Providing additional help and support instead. Disruptive behaviour might actually be down to them struggling in class. The naughty behaviour just a cry for help. The danger is that disengaged children will do the bare minimum in class and will soon fall behind.

Needed More Than Ever Before

It’s even harder now with COVID as teachers aren’t able to observe children so closely. Distance learning makes it even harder to see how engaged students actually are. If distance learning is the new future of education, then machine vision systems need to be in place quickly analyzing facial responses. Checking on emotions from afar and feeding back the results to the school would give schools the information they need.

Recognises Emotions

Machine vision can recognise emotions such as when a child looks disengaged, sad, or confused. Then teachers can adapt the structure of the lessons to meet their needs more closely. It really does transform the whole educational experience. Schools can make modifications much quicker to better cater for the needs of their students. Fortunately other problems can be identified too, such as bullying.

Picking Out The Best Learning Strategy

Being able to access the level of engagement gives teachers a guide as to which classroom strategies the child responds to best of all. School lessons can be adapted to match the learning requirements of the group. A classroom is full of lots of different personalities. What strategy works for some, might make others switch off and become disconnected. This earlier post about learning styles might help explain them a bit more.

However, that’s the problem with teaching a big class, teachers try and teach them all the same. But the students are all individuals with their own needs, feelings, and learning styles. Once a problem has been identified teachers can try an intervention strategy. This school did this through art, photography, drama and music workshops to try and inspire students again.

Identifying A Buddy System

Machine vision systems are much more sophisticated accessing which students a child might work most effectively with. If children are comfortable at school, they are more likely to thrive. Often children are grouped together without rhyme or reason. It would be far better to look at the results of the analysis from machine vision systems. Seeing which children work together the best with each other. Negative interactions with other students would only disengage them further. So instead identify a buddy system that works.

The children who are gifted will be frustrated to be teamed up with children with no interest in school. Likewise, someone needing extra help will be annoyed when their partner seems to know everything. It is important to get that peer mix right and machine vision system could be the way to achieve that.