What Is Machine Learning?

Machine learning and artificial intelligence are related concepts but different at their cores. Machine learning is a branch of AI, which deals with building algorithms and selecting models designed to provide outcomes based on data fed to the machine.

The machine learning algorithm recognizes patterns in the data to later deliver a more refined output based on a particular model that determines the type of expected results. The more data the algorithm receives, the better it gets at pairing patterns with previously collected results.

It all starts with writing an algorithm and building models.

Here’s a more technical explanation of machine learning by Jeff Catlin, CEO at Lexalytics:

“You select a training set, choose what reflects a positive or a negative for that set, then select a type of model to use. The size and quality of your sets, along with the model type, will depend on what you’re working with and will influence the outcome you get.”

So How Is Machine Learning Different from Artificial Intelligence?

As we mentioned earlier, the two terms are closely related but perform different tasks. You now know that machine learning, in short, recognizes patterns to deliver predefined results.

To understand machine learning, it’s best to imagine feeding its algorithm with data on dogs, and then, based on the model used, the algorithm matches dogs to their appropriate breed. But the thing with machine learning is, even if the algorithm recognizes that a certain picture contains a dog but fails to match it with the right breed, it’ll never learn why. Because to learn why, the algorithm has to be remodeled manually by a programmer who sees faults causing this problem.

Artificial intelligence solves this problem. When the expected results aren’t correct, AI algorithms don’t need to be remodeled manually—they do it automatically by analyzing the data and finding flaws in the algorithm to prevent future errors.

AI capabilities are still far from the possibilities of the human mind, but we do share one trait—trying to find the answer to what caused the incorrect result. After locating the problem, AI reshapes its algorithm, making it more accurate. How? By going over the data, adapting to it, aggregate errors, and seeking new data to derive refined conclusions.

AI, in short, is an algorithm programmed to be curious to seek an answer.

Now let’s go back to the question from the title.

Machine Learning Shaping eLearning

Let’s make one thing clear first, we don’t need AI in eLearning just yet. In the future, this technology might be commonly used in eLearning, but for the time being, machine learning is perfectly enough to make a flexible, customizable, and learner-centered learning management system (LMS).

#1 Recognizing Patterns in Learners’ Past Performance

Machine learning algorithms can track the performance of the students registered in an LMS and shape future sessions and their topics so that they respond to the actual needs of the learners.

It’s a huge advantage in situations where there are, for example, twenty students with varying abilities and experience enrolled in a course. The machine learning LMS proactively adapts the course to these fluctuations and delivers tailor-made content valuable to individual students.

This benefits the students in two ways—more knowledgeable students progress faster with content tweaked to target their actual skills and abilities. The less versed have a better chance to work on the material that is difficult for them to grasp.

#2 Getting Students’ Motivation

To teach effectively, eLearning platforms have to be fun, engaging, and learner-centered.

With “machine eLearning” learners are acquiring the knowledge through a tailor-made and personal approach to teaching where they focus on their knowledge gaps instead of endlessly going over the same unified and redundant curriculum.

Most learners starting a course dread going through the dull and unimportant parts of the course program. In fact, this is one of the most common demotivating aspects of eLearning.

With machine learning that demotivating nuisance is eliminated because the algorithms monitoring student progress actively refine the curriculum, removing those redundant parts. As a result, the learner spends significantly less time on the training to acquire desired skills and reach his or her goal.

The learners will know that the course is tailor-made to fill their knowledge gaps without any additional hindrances in form of irrelevant lectures. Since they know the course responds precisely to their needs, they are more interested in participating in it.

#3 Improving ROI

Since machine learning might significantly cut down on course time, it gives more time for your employees to focus on their job-related tasks. Also, having access to your learners’ progress can help you better schedule your online courses for the employees that need to polish their skills to work better.

By analyzing the data generated by eLearning courses, you can quickly spot the topics your students are struggling with the most—machine learning algorithm will adjust the course material to help learners focus on their weaker sides more. This saves you from wasting time and resources on training materials that will not benefit your current goals nor improve your employees’ skills.

#4 Saving Resources

With eLearning courses refined and tailor-made to an individual as can possibly be, you’re saving on additional payroll hours devoted to training. Also, your Learning and Development department doesn’t have to spend countless hours poring over graphs and learners’ results but instead devote more attention to populating eLearning materials with more valuable and up-to-date content.

How Machine Learning Could Change the Future Way We Learn

In the distant future, it’s possible that all of us will have a personalized tutor teaching us the intricacies of our desired topic. And who knows, maybe it’ll even be a custom rendered VR assistant. But that’ll probably be in the era of advanced AI, and as we explained in the introduction, we’re still quite a distance away from there.

So before the mystical AI robots will rule the world, let’s take into consideration one of the most pressing drawbacks that boggles the minds of scientists when it comes to AI and machine learning.

The liability.

Will AI become a threat one day?

If machines come up with conclusions that are completely unacceptable by any standards, ethical or religious, who should take the blame for those opinions? The machines cannot be held accountable. Maybe the scientists responsible for the algorithm? Or maybe the data they fed to the machine? And it doesn’t take long for an AI to formulate completely abhorrent conclusions.

Think about Microsoft’s AI chatbot, Tay, which in a matter of a day turned from a friendly bot to a racist arrogant. Of course, Tay was just an experiment, but it forces us to carefully think about what type of data gets into the machine’s algorithms and what can be the consequences.

But don’t worry. These are just the far-flung ruminations on machine learning, AI, and their influence on the future world.

What Machine Learning Can Do Today for Your eLearning Course

Machine learning can be effectively introduced into eLearning systems to facilitate the absorption of valuable, learner-centered knowledge. If you want to help your employees acquire essential skills that will improve their work, let’s get in touch. We’ll help you develop a custom eLearning platform that will increase the performance of your employees and leave them enough time to get their tasks done.