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How Machine Learning Can Benefit Online Learning


From phones to watches to TVs, everything around us is becoming ‘smart’. Education is not so far behind.

The ‘smart’ approach to education is typically the incorporation of Machine Learning (ML) in learning and development. Machine Learning leverages Artificially Intelligent methods to teach systems how to make informed decisions without any human intervention. This is done by feeding data to a machine learning algorithm which is then able to process the data and make inferences for future events. 

How does this relate to education? Well, eLearning involves the use of digital devices around us to deliver engaging learning experiences. What would happen if we leveraged data from these devices to create an artificially intelligent approach to learning? We don’t need to look far for the answers; it’s already underway!

Keep reading to find out how we can fully tap the benefits that Machine Learning brings to online learning.

Individuals can be very unique when it comes to studying patterns, the pace of learning, and even areas of interest. Machine Learning can be used to cater to the individual needs of each learner in a way that is impossible to do physically. 

An ML algorithm can factor in a student’s area of interest, as well as past courses and score reports to give them unbiased, data-driven advice on what learning disciplines they may find interesting and what learning paths they could pursue. Creating a streamlined learning journey like this for each learner is impossible to do in conventional learning approaches.

Another important way learning can be personalized is by adapting course content according to each learner’s proficiency. If a student finds it easy to score well on assessments, an artificially intelligent algorithm can present progressively harder questions. Similarly, some learners choose to go at a slower pace than others, which is something Machine Learning can cater to.

Imagine having an objective view of every fact you know and every skill you possess, complete with the levels of competency, expertise, and areas of improvement. This is what a Machine Learning algorithm observes for each learner. 

Based on all the past grading data, course history, and competency levels, ML can help come up with individualized reports for each learner. These reports outline the areas where each student is competent, as well as the areas where they need to improve. In this way, the algorithm assesses the knowledge gaps that learners have in a fair and unbiased manner. Once these gaps have been identified, students can then work to mitigate them.

Understanding skill gaps and identifying at-risk learners using machine learning can help institutions as well as its students. A Mckinsey report studies the results of an online university’s application of an ML model to reduce attrition rates during the pandemic. This involved analyzing a decade’s worth of data to understand key characteristics of students that determine their likelihood to continue studying.  

Once it was validated that the initial model was much more successful in predicting attrition than the baseline, the model was modified and applied to the current students. The resulting model gave detailed insights into five characteristic classes of students likely to discontinue studying. Only two of these types could have been identified with linear rules. This study proved the value of leveraging ML in higher education, and the results can be seen in the image below:


How Machine Learning Can Benefit Online Learning
Students at risk of attrition: McKinsey

ML technologies don’t only present learners with various assessment formats, they can also intelligently grade them. This does not involve the conventional approach of matching each student’s answers to a programmed correct answer word for word. 

Instead, ML algorithms can parse statements to figure out, with great accuracy, the context in which a certain answer is written and grade it on a scale, rather than a binary correct or incorrect. With each response, the algorithm can also detect possible ways to refine the answers and give individualized feedback to the learners.

This can come as a huge relief for instructors who are pressed for time and unable to cater to each student individually. In the same way that automatic grading streamlined the assessment of multiple choice answers via scantrons and eLearning, machine learning technology can assess the nuances in a variety of response types.

Students learn the most by asking questions. In an online learning scenario, an instructor is not always present to answer queries and the lecture material cannot possibly anticipate all possible questions that learners might come up with. A great way to tackle this is by employing chatbots to assist the learners.

Chatbots are intelligent software that can give instantaneous answers to questions put forward by students. In some cases, these chatbots are programmed to answer questions that an instructor has already anticipated or they could be used to suggest helpful links to students relevant to their problem.

Acting as a personal tutor available at all times of the day, chatbots are a very useful tool in incorporating interactive learning. Online learning can be isolating at times and having an AI chatbot can make all the difference.

Chatbots also play an instrumental role in guiding prospective students and directing them toward relevant resources. Many new students are hesitant to reach out directly to faculty, or are unaware of the right department to contact. In many cases, these are first-generation college students who don’t have the right guidance available to them.

A virtual chat assistant can help cut through the confusion and uncertainty as evidenced by Pounce, Georgia State University’s chatbot, which was first deployed in 2016. The university used to lose up to 20% of its incoming fall students over the course of the summer, a phenomenon called ‘summer melt’. This was due to various factors, primary of which was the lack of financial aid awareness. Pounce helped reduce the summer melt by 20% in its very first year of use.


How Machine Learning Can Benefit Online Learning
Pounce: the Georgia State Chatbot

A complete overhaul of your current online learning system to incorporate Machine Learning principles might seem like a waste of resources in the beginning. However, if you take a moment to consider the ways in which ML can streamline, automate and personalize online learning, it will end up being a cost-effective solution in the long run. 

For one, all eLearning platforms are moving towards artificially intelligent, highly personalized approaches to learning, and you would be missing out if you choose not to incorporate innovation and flexibility in your learning methods. After all, learners tend to gravitate towards learning platforms with the most to offer.

In addition to that, even though it may seem to be a costly investment, the returns you get with a learning solution incorporating ML are far more than previously employed online learning approaches. This is because a lot more can be done when you have a virtual aid ready to instruct, grade, give feedback, or even simply chat with the learners. 

As you can see, Machine Learning has the potential to transform the learning experience for the better. The modern approach to education understands the learners, puts them at the center of the learning experience, and also hands them the reins to shape their own learning journey. Machine Learning provides all of that and more.

It’s still in its early days but implementing Machine Learning in L&D is something that is worth exploring, whether you are a K12 or Higher Ed institution, or a corporation looking to streamline the employee training process. With benefits to all parties: learners, instructors, and administrators, not only is Machine Learning a great investment, but also one that can prove to be cost-effective down the road.

Maira Afzal Content Writer Specialist at Edly that is an Ed-Tech solution provider and an Open edX partner. Reach out to us at


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