eLearning has become the new normal in this Covid-19 period. As schools and colleges remained shut, eLearning or online education came to the rescue. eLearning apps have changed the future of classrooms.
Technology is constantly evolving and boosting efficiency; making our lives easier and better than yesterday. One of the areas where we can witness this is online education. The combination of Information Technology and Education has reaped the benefits for the improvement of society, and this has been made possible with the help of Machine Learning and Artificial Intelligence.
Let’s dive into this topic and understand how Machine Learning is shaping the future of eLearning. But before that, let’s take a quick overview of what Machine Learning is and understand a few ways IT and Education have crossed paths with each other until now.
Machine Learning(ML), an application or subset of Artificial Intelligence (AI), involves algorithms that provide systems (machine learning models) the ability to learn and improve on their own; without being explicitly programmed. It tries to predict the outcome by recognizing the patterns in the provided data.
Machine Learning and Artificial Intelligence are closely related. When the outcomes aren’t accurately predicted, that’s when Artificial Intelligence comes into play. AI algorithms don’t require to be remodeled manually, it’s an automatic process where the data is analyzed to find flaws and prevent future errors.
The role of Machine Learning and Artificial Intelligence in eLearning involves predictions, algorithms, and analytics to create a more personalized eLearning experience.
Also Read: Can Machine Learning overcome human bias?
The combination of IT and Education has paved the way for-
Experiential learning is learning through experience. A couple of decades ago, a flight simulation software provided students with an opportunity to learn about avionics and instrumentation, however today, students have been using Virtual Reality headsets for even a more realistic experience. So, we can say that technology is trying to make learning convenient by focusing on learning by doing.
Ever since video conferencing apps such as Skype stepped into the market, one-to-one online teaching sessions became extremely popular. Today, you can attend live webinars from any part of the world without facing any inconvenience.
Let’s now come back to our main topic and look at how Machine Learning is making e-Learning convenient.
The algorithms of Machine Learning can analyze the performance of the students registered in the Learning Management System (LMS) and provide future sessions as per their past performance so that the students are delivered what they actually require in order to grow in an organized manner. It extracts and evaluates the data from the LMS to predict what learners need based on their past performance.
Let’s take an example where multiple students with different learning abilities and experiences are enrolled in an online course. Now, machine learning algorithms adjust the course as per the ability of the individuals and deliver the required valuable content. Imagine if the content tailor-made for the ones who are having high knowledge is presented to the ones who are yet to clear their basics, and vice-versa. That would disturb the flow as well as the user experience.
Many learners, when starting a course, find going through the unimportant and irrelevant lessons in the program, a tedious task. Machine learning algorithms eradicate this inconvenience/annoyance and provide a personal approach to learning where learners can acquire the knowledge they wish to seek and focus on filling the knowledge gaps instead of going through the redundant curriculum endlessly.
Ultimately, this helps them learn or achieve the desired skills in a lesser span. This makes eLearning exciting and encourages more involvement of the learners in the program.
By gathering and analyzing the data, Machine learning can help you identify the topics your students or learners are struggling with the most. Machine learning algorithms will adjust the course material in such a way that it would focus more on strengthening their weaker sides. This would help you save time and resources from getting wasted on training materials that are not going to benefit the students in improving their skills. Since machine learning might cut down on the course time (as discussed above), it would provide learners with more time to focus on the relevant material and polish their skills. The involvement of machine learning in eLearning also helps you save extra payroll hours put into training.
The support of computer vision in eLearning can enhance the ability of tutors to detect, monitor, and respond to the learning behavior of students, which could be further assessed to provide educators with feedback on their teaching methods.
Let’s understand this with an example. In a physical classroom, a teacher can easily identify when a student is bored, distracted, or stressed based on their facial expressions or body language. This is challenging when it comes to online learning, but that’s where AI can provide a helping hand. With the help of computer vision, eLearning platforms could obtain real-time behavior of learners, based on which further decisions can be made whether to provide some easier or engaging materials to the students, redesign the lessons or segment students. In the near future, such technology would be available for mass usage.
Crowdsourced learning is when two or more people or learners come together to achieve a better understanding of a topic or subject. This type of learning refers to learning content requested and developed by individuals rather than specifically creating learning content. Wikipedia is one of the most successful crowd-sourced learning concepts combined with AI.
Brainly is another example which is an educational Q&A platform having a community of 200 million+ students and teachers. The platform has a machine learning layer to act as a moderator, filter spam, and set the quality of the content. It’s been said that in a few years, Brainly will be able to offer automated answers to certain questions. The concept would be similar to that of the Google search engine, but on a smaller scale as it would cover concerns related to primary and secondary education.
There have been learning platforms focused on providing a fun learning environment for kids and have captured high success. This has compelled AI developers to make the applications smarter. The upcoming e-Learning software involves machine learning features that are tracking the way students navigate programs along with their reactions and proficiency. To be precise, AI routines would be learning at the same time as children are. The aim is to utilize AI technology and develop future apps that are more efficient.
The integration of Machine Learning and AI into eLearning will form a more effective educational infrastructure and will not only help students and teachers but also parents and communities.
Can we expect to have a personalized tutor or a virtual assistant who would help us with the queries of our desired topic? Maybe in the distant future, in the era of advanced AI. But one challenge that always remains when it comes to Artificial Intelligence and Machine learning is the liability. If Machines start making conclusions that are completely unacceptable, who would be held accountable for it? The machines certainly cannot be! Well, that would be our topic of discussion for some other day.
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