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6 Good Examples of Implementing Machine Learning into the Education Process

6 Good Examples of Implementing Machine Learning into the Education Process-1

Most of us grew up reading loads of science fiction. Those usually were stories about robots and machines, which could ‘think’ as well as people do, meaning they could learn, adapt, build logical chains, understand emotions, and so on. Those were dreams of course. Sometimes they were pleasant fantasies about the future with no lousy jobs (as robots will perform them all) or time with no obligation to study hard, for you can have all the knowledge inserted in your head on a flash drive. Sometimes, of course, it was horror, picturing machines as emotionless monsters, who, one day, will try to kill all the humanity.

Still, those dreams were present among writers, and later scientists, for quite a long time. Even nowadays it is a hot topic for filming, writing and investigating. Have you seen the latest ‘Blade Runner?’ Or ‘Autómata,' not brand new, but still released recently. All these films, books, and articles we can find online or elsewhere show us that society is always willing to include Artificial Intelligence in their daily life. Science fiction has probably predicted all the worst variants, so people can consider themselves well-prepared to anything possible.

So, to cut a long story short. Have you ever thought that all this ‘magical’ and ‘super-techno’ future is happening already? Yeah, right now! Haven't you? Well, we hope then, this article will uncover some of the unbelievable things, that might seem quite unusual to us.

We live at the time when Artificial Intelligence (AI) is implemented practically in any working industry we have. Business, IT Projects and SEO, Financial services, and much more are already actively using Machine Learning as much as possible to make most of the processes run automatically. So, why not education?

In fact, we see Machine Learning well-implemented in education process too. It is a global try to move forward from the traditional education with its ‘one size’ teaching for everyone, for it is no longer relevant for our fast-developing modern world. Every person has different learning speed, as well as a unique background. Therefore, education systems should become more flexible. And they are doing that already.

Here are some of the top examples of implementing Machine Learning in education, to make it more useful to everybody.

Digital books and Adaptive Learning

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Those tools of the modern world allow teachers to create more tailored and customized learning experience, for now, they have access to a collected data on what students are currently into and how do they cope with all of the concepts they are trying to learn. When having this information, a teacher can understand whether a student, a part or the whole group is not getting specific ideas, topics or materials. So, they can adjust their lessons according to this data and help students with learning, and prevent students from falling behind or even drop out. High time to stop session madness and keep top grades all the time.

Predictive Learning Paths

Machine Learning is not only about gathering data for teachers to analyze. Its algorithms are already able to create predictive learning paths for each student while one is studying. It works in a quite simple way. Student goes through the course with an adaptive learning software and the algorithms, if needed, are serving up an additional content for a student to study or they can allow the student to move ahead if he or she have already mastered the subject matter.

Have you ever heard of McGraw-Hill Education’s ALEKS?  A brilliant example of a Predictive Learning Path. In short, this is a web-based AI assessment and learning system. It uses a graph theory to break a domain into concepts. For example, an algebra course would have 500 concepts. A student is tested for his/ her basic knowledge, and the programme afterward is creating a suitable path through the domain, basing on the starting point of the student. At the very end, each student gets the same amount of knowledge, needed to finish the course.

Content Analytics

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Grading Systems have relied on human beings for a very long time. Nowadays, with the implementation of Machine Learning, teachers get a great help with grading students, while doing it quickly and with a higher accuracy. In future, all the scoring will be automated. We already have a great example of plagiarism detectors, that automatically run the check of each students essay and grades it for teachers. Those tools are used worldwide for several years by now.

You probably know about Gooru and IBM Watson Content Analytics? Well, developers promise much more apps and tools coming quite soon. Bad news for those students, who practice cheating.

Repetitive Tasks Automatization

A lot of time each day is spent on repetitive tasks, like monitoring attendance or collecting assignments. Teachers could spend this time more efficiently, and that is why there appeared an idea to make those processes automated. This will allow a teacher to take their time when explaining tough topics or create broader discussions of essential concepts.

There are lots of back office stuff existing already, like EDULOG, which does school bus schedule. Soon we wait for each daily routine task to be automated.

Personalized Adaptive Learning

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Learning speed is individual for each person. That is fact. And that is the fact that was disturbing educators all over the world for the past 50 years. Luckily, Machine Learning has found a quick solution for this issue. Adaptive Learning becomes possible via algorithms of Machine Learning, so students are led forward based on their individual learning speed of grasping ideas and concepts. The use of such an Adaptive Learning in EdTech (Education Technologies) means a customized learning experience for everybody. Teachers finally can assess the understanding of an individual student or a whole class. They will be able to adjust their teaching plans according to students process, help each student individually, if required, spend more time on the topics the whole class considers to be hard, and less time on concepts that are efficiently processed. Also, new e-Learning modules ask students critical questions to boost up their imagination and show a deep understanding of the subject, which should be a suitable replacement for standardized tests with a high possibility to cheat and forget the material.

Adaptive learning systems include projects like DreamBox, ALEKS, Reasoning Mind, Knewton. There are also systems for game-based learning, like ST Math or Mangahigh.

Matching Teachers and Schools

Projects like MyEdMatch or TalentEd are aimed to find a perfect place for each teacher. They are speaking of ‘transforming careers, classrooms, and communities.’ Those projects help schools to recruit the best teachers, and the best teachers to find best places to work. It is good to know, that Machine Learning is implemented not only to help students or as a simple routine work optimization. No, teachers also get their benefits in the form of better job opportunities due to their skills, knowledge, and professionalism. Those Machine Learning systems are aimed to support a whole employee lifecycle with tools and insights that should boost efficiency, simplify workflows and enhance decision-making. Those, at the very end, are empowering districts to take education to new heights.

Bottom Line

‘Education as a Service’ becomes a new goal to the whole teaching process. A classroom is not a place for suffering anymore; students come here to gain the knowledge they really need and get the professional help on the points they can’t go through by themselves.

This is a natural process, that is seen in a growing number of industries, no wonder education is also involved. The modern classroom becomes more and more digitized; people are getting the opportunities to gather the practically unlimited set of data and analyze it in a few moments. Sure, it all needs to be well-purposed. But what is more critical, Machine Learning uncovers an excellent opportunity for knowledge discovery.

This means that education will finally be able to identify the essential concepts, meaningful patterns, and ideas. Education is about to transform into a perfect ‘User Experience,’ as it will be personalized and customized, it will cover the individual gaps in knowledge and include individual learning speed into the process. It is a great structured base for the future usage. The time of ‘lern for an exam and forget’ are passing by. We will get systems, which are asking the right questions, boost up creativity and have a deep understanding of whether the material was processed carefully and was fully understood.

We see only a few changes by now, like automated systems for daily repetitive tasks, adaptive learning programmes, content analytics and the automatization of grading. But who knows what future can bring? Remember science fiction? It might be the high time to read it all over again.

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