Updated: Jul 25, 2020
We need to be a little bit careful about the latest trends in education, along with any claims made to being the Next Big Thing. Massive Open Online Courses (MOOCs) were set to transform learning at the beginning of the 2010s, but somehow that didn't quite happen. Participation rates have dropped, and only about 12% of people actually complete their courses. The learning landscape remains pretty much as it did before, and while MOOCs are great and do fulfil a need for many, they are not as transformative as we thought (hoped) they might be.
That said, we do need to look at emerging technologies and some of the conversations that are happening in learning for the future. This interactive Strategic Intelligence graphic from the World Economic Forum (WEC) is a comprehensive look at learning for the future as a whole, and it does a great job of providing a sense of just how large the scope of learning for the future is, along with how education fits with everything else that's important in our world.
There are some interesting articles about how learning can be future-proofed. Examples include this from La Trobe University, which describes the important of lifelong learning, frequent job changes, retraining and an eye on understanding that we're only as employable as the skills we have. This article from The Telegraph follows a similar vein. It looks at planning for at least five different work roles, and has a table indicating emerging and declining roles. One piece of advice is for people to think in terms of 'job families', such as 'something in agriculture' etc. Once the broad field is decided, a person can then start looking at trends and training in that particular area, and then make a decision.
As far as leaning goes, machine learning is something that does a good job of introducing students to the fundamentals of a digital technology that's prominent in our future. This website looks excellent, and goes further that the coding in schools models, to actually playing around with the principles of machine learning in ways that students can understand. From the website: "Machine Learning for Kids is a useful tool for introducing children to how ML systems are trained, how they are used, and some of the real-world implications of AI applications."
The site also provides some excellent links, with examples being Experiments with Google and teensinai - they are pretty cool. For the more traditionally-minded, these textbooks from Korea's Software Education Model are well worth a look, with some cutting edge student work, projects and learning possible. All of these resources are free and for educational use only.
Augmented Reality (AR) will likely emerge and start to feature in education over the coming decade or so. The first digital world is already being built, and it's designed to be the same as ours. The idea is that the digital and actual worlds will eventually merge seamlessly into each other, with is kind of quite cool when you think about it. Imagine walking along wearing a pair of AR glasses with information about buildings, places and events available if you want it. On the other hand, also imagine the advertising.
There is a slightly darker, edgier side to future trends in education. Facial recognition is in classrooms and schools in China, and the country hopes to use AI to boost its education system. Can the human experience of learning be reduced to this? Should it be? Social robots are also being developed as tutors. It appears from that research that physical robots themselves are a long way off from teaching classes, but using AI to deliver online learning certainly isn't.
Other technology that's being developed includes a headband that monitors student attention. This is interesting. Instead of making learning more engaging and interesting, the solution for some appears to be focusing solely on student inattention, and not the reasons for it. Fortunately this technology does not appear to be taking off in China, but it does give you an idea of what some companies are working on.
Another example from America is a surveillance technology which places microphones in schools and uses AI to detect 'aggressive' sounds and behaviours. The idea is to apparently detect school shootings and other violent events before they happen. Fortunately it appears that it's not working as well as it might, but again it provides a glimpse into how companies are thinking in their attempts to digitise to data and apply AI to the school learning experience.
Now for something that does look really interesting - robots that conduct health inspections at school. The Walklake robot is used by over 2000 preschools in China, and can detect common childhood illnesses such as conjunctivitis and hand, foot and mouth disease. I can definitely see instances of where technology such as this might be extremely useful in schools, particularly those without a school nurse or immediate medical access.
A lot is being invested in learning for the future, but are things actually changing? Structurally, schools along with education and other learning systems remain much as they have always been, with test or standards-based high-stakes assessment and a narrowing of the curriculum remaining the norm globally. The technologies and initiatives we currently see very much feel like tinkering around the edges of the challenges facing learning and relevance in the real world, and it's hard to see a way forward.
If education and skills for the future such as the strategic intelligence from the WEC really are the way we need to be moving to ensure that we all thrive in this world over the coming decades, then we have an enormous amount of work to do. But more than that, perhaps the biggest need for our collective futures is raising people who are decent human beings first. People who are kind and hard working, who are better human beings in an ever-changing world.
We'll continue to watch trends and technology in education and learning for the future, but being a decent human being sounds like a good place to start.
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