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Why Machine Learning Will be the Key to E-Learning in the Future

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E-learning and online education are highly effective learning methods, proven to increase knowledge retention rates substantially in comparison to face-to-face training. The growth of cloud-based learning management systems (LMS) has helped instructors impart their knowledge to students in any part of the world. And, when you add personalization in LMS to learning, it makes the experience even better.  

But one aspect that holds the key to e-learning in the future is machine learning. This is a set of algorithms that machines use to perform a specific task without the use of explicit instructions – instead relying on patterns and inferences.

Machine learning has been around for a long time. Google uses it to improve its search engine functionalities. Amazon uses it to suggest products you might want to buy based on your purchase history and the insight it gathers from other shoppers. And, Netflix and Spotify use the same method to build personalized playlists.

 

Why machine learning will be the key to e-learning in the future

 

Machine learning in education is not a new concept, but it has been growing in popularity of late. Learning Analytics (LA) and Educational Data Mining (EDM) have been there from the start of the 21st century. 

As computational power and data storage becomes cheaper and more abundant and giant tech firms invest heavily in machine learning, the future of learning is set to scale new heights. What seems to be leading the way is deep learning – which is a type of machine learning inspired by biological neural networks like the brain. Here, large datasets are required, which are generally used to carry out complex tasks like speech and face recognition, as well as scene parsing. It eventually may be used for such jobs as real-time speech translation.

 

Machine learning is already impacting the LMS landscape. It would become a norm for virtual personal assistants such as Cortana and Siri to guide employees through training and offer support when they need it. But for more interesting predictions and accurate behavior detection, richer data logging would be required. For this to happen, you will see a growth in the use of central learning repositories like Learning Record Stores for Experience API.

 

With these in place, both formal, as well as informal data about the learners, can be collected comprehensively. It would allow you to track learning on the move and even when you are offline. It is vital to understand that the LRS can persist between devices and systems and collect data about a learner for his or her entire lifetime.

 

Personalization for each student in the system

 

Machine learning makes software applications more intelligent, which improves their performance automatically. When machine learning is integrated into learning management system applications, it takes the e-learning landscape to the next level. Machine learning revolutionizes eLearning systems, it makes learning more effective.

The combination of machine learning and LMS applications allows better optimization of course content as well as delivery. Online course creation on any cloud-based LMS is an ongoing process. You need to revise the course content as and when you get the feedback from your students.

 

There are two ways of getting the feedback – qualitative surveys or student’s comments and quantitative data such as quiz results, ratings, and other course metrics which you get from your LMS. The instructor needs to process and analyze this data to understand the trends and patterns to improve the effectiveness of the course. The entire process is a time-consuming and tedious task, and it becomes difficult for the instructors to do this job with perfection.

 

LMS machine learning can use artificial neural networks or deep learning algorithms to process the data and modify the course content without human intervention. Based on the patterns and data of every student, an intuitive and intelligent LMS can quickly learn the weak areas of the student and improve the course content automatically.

 

Also, the integration of machine learning and LMS applications facilitate personalization of the course for every student. Each student has a different educational background and cognitive ability. It is, therefore, vital that you provide them with case studies and examples that they relate to easily and understand quickly. Such a high level of custom learning management system is only possible if it is integrated with machine learning. Machine learning personalization can cater to the individual needs of every student.

 

There is plenty of data available in the LMS about the students, which can inform us on the use of our learning materials better, and predict the behavior of students. Machine learning allows you to can classify and correlate behavioral data in a corporate LMS with an employee’s performance review. With this, you can get to know the behaviors associated with poor performance and flag early warning signs for timely intervention.

 

You can make these recommendations more relevant by using Netflix-style machine learning algorithms and studying the behavior of students. By using the early intervention model, you can recommend courses to students or employees to improve their performance.

 

User engagement is an important factor when you plan to introduce machine learning to your LMS. When there is a lack of user engagement, you don’t get sufficient data for analysis – which inhibits the accuracy of the predictive analysis. Also, user engagement is a vital factor determining the return of investment for any platform. It is important to use techniques like personalization and gamification to increase user engagement – as well as create fresh content on a regular basis.

 

The integration of machine learning and LMS applications enhances the effectiveness of courses. Apart from optimizing and personalizing the course content, LMS machine learning can also look after routine tasks – such as grading, onboarding, providing initial instructions, etc. This gives instructors more time to spend on creating new courses.

 

The combination of machine learning and LMS applications also identifies the need for new courses. Machine learning is more about data and pattern identification. A machine learning-enabled LMS allows you to collect a wide set of data and statistics of several students across a variety of courses, which can identify the courses that have not yet been covered.

 

Machine learning-enabled LMS helps you improve the marketing of your courses, and allows you to reach targeted students by creating a detailed profile of those who would benefit the most from your course. This kind of high-level detailing allows you to hone your marketing messages and improves the effectiveness of your campaign, thereby increasing your reach and conversions.

 

A machine learning-enabled LMS allows a highly interactive and immersive learning experience. Remember robot assistants like Temi, Cloi, and Zenbo? You can integrate these robots with a cloud-based LMS to assist students, making their experience highly interactive and immersive.

 

Machine learning will be the key to the future of LMS and e-learning

Machine learning can make your LMS more flexible, customizable, and learner-centric in the following ways:

 

It helps you recognize patterns in students’ past performance – With machine learning algorithms, you can track the performance of your students who are registered in your LMS, which can inform you in creating future sessions and topics factoring in their needs. For example, if you have 15 students in a course with varying abilities and experience, LMS machine learning proactively delivers tailor-made content that is suitable to each.

 

It improves your ROI – Machine learning can substantially cut down on your course time, giving your employees more time to focus on other tasks. Also, when you have access to your employees’ progress, you can better create online courses geared toward development of weak areas.

 

You can save on resources – When your e-learning courses are customized to suit the needs of employees, you can save on extra payroll hours that go into training. Moreover, your L&D department can channelize their energy on creating valuable and up-to-date content because they don’t have to spend their time on assimilating employees’ results.

 

This is how machine learning will perhaps change the future of e-learning. In times to come, it is possible that you’ll have a personalized tutor to guide you through your desired topic. You can effectively introduce machine learning into e-learning systems that aid in the delivery of valuable, student-centered knowledge.

Obviously, creating online training content from scratch isn’t easy. eServe is a leader in LMS education tools that can complement your existing LMS and allow you to customize your corporate training materials to suit your specific needs. Contact us to learn more.

 

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