Artificial intelligence and machine learning are reshaping almost all industries, including online education. Implementing AI and ML technologies allows creating customized content and personalized learning paths, enhances performance tracking, and improves management processes.
As online learning became more widespread because of the global lockdown due to the COVID-19 pandemic, more and more people began to look at it with interest, and its development accelerated. In this article, I want to talk about the ways e-learning benefits from machine learning technologies.
Learning Management Systems Improvement
For distance education to be effective, a particular virtual environment and special learning tools, such as learning management systems (LMS), are needed. Using machine learning and data analysis in the development of LMS features makes the LMS more flexible and overall improves the learning experience. For example, it optimizes the teachers’ work since it makes resource management easier.
Moreover, it can provide students with virtual assistants to help them learn. This is important because, according to a study on the instructor role in dealing with mixed contents, students may lose interest or adapt to the learning process worse if they are on their own. While in a traditional education model, the tutor owns the knowledge and plays a learning role in the learning process, in remote education, a student takes on the whole responsibility. Convenient environments that include virtual assistance can increase students’ adaptability and lower stress.
Content Customization
Materials adjusted to the needs and requirements of every student are going to be the new normal for e-learning. ML technologies allow collecting and analyzing a lot of various data and creating more relevant content.
Let’s take an online educational platform as an example. One can use machine learning to examine the content of the online courses and evaluate their quality. Moreover, ML data analysis can show how students interact with the content, whether they understand the materials well, and whether the difficulty matches the given student’s level. The next step is to adjust content based on this information, which makes learning more effective. Such ML applications as pattern recognition also make recommendations of new courses more relevant.
Personalization of Learning Process
Not only content can be customized. With ML, we can make the whole learning process more personalized. If you have ever learned foreign languages with a mobile app, you might have noticed that cards with words you already know tend to be shown less and less frequently. It’s a well-known system which is called spaced repetition. Its work is based on the user’s input. The system comes in handy when one needs to memorize words or other pieces of information.
Machine learning may give us an opportunity to create more complex algorithms and build programs that analyze the way users behave overall. Then it can slow down or speed up the learning process, provide more information on a particular topic, or suggest skipping to the next level faster.
Accurate Feedback
Arguably (and thankfully), ML and AI won’t be able to replace teachers. But these technologies may take learning assistance to a new level. For instance, big online courses usually can’t provide every attendee with personalized feedback since instructors don’t have the resources to deliver so many proper reviews. Advanced technology can change the situation because ML makes it easier for tutors to trace the learning process individually. Say, the system will collect all the data, create infographics, and provide it to the teacher, who, in turn, will be able to come up with recommendations for students. Some of the learning systems already offer this type of interaction.
It’s possible that further development of machine learning in natural language processing can help build software that follows a student’s progress, makes relevant comments, and gives meaningful feedback even without a human mediator.
Administrative Task Automatization
Managing learning processes online is as time-consuming and stressful as doing it offline, but here you usually deal with even more data. And yes, such routine duties are exactly the sort of tasks that come to mind when we think about something to hand over to machines.
Well, smart automation accelerates routine administrative work by giving instructors more time to create better materials or communicate with students. Software taking over all the boring managing tasks is probably one of the biggest dreams of teachers who want to devote their time to actual teaching.
Examples of Using Machine Learning in Educational Projects
The e-learning platform Netex Learning built a learning cloud that allows creating learning systems with content customization, self-assessment, gamification, and more.
Edtech project MobyMax used ML to implement a system that helps teachers organize a learning process, create personalized lessons, make better assessments, and gamify classrooms.
Education tools platform Quizlet Learn uses machine learning to process data from users’ study sessions. Based on the data analysis, the platforms adapt studying sets to make learning more efficient.
An automated text analysis solution M-Write was developed by The University of Michigan for students to learn complex material better through writing. It analyzes writing introductory tasks to prioritize students who need assistance.
An ML-assisted scoring and grading tool Grade scope is built by UC Berkeley. This teaching assistant automates the workflow for grading exams, tests, and assignments in large classes of several hundred students.
Conclusion
The industry of e-learning is growing bigger and stronger as the demand for it is rising. The implementation of machine learning is enhancing e-learning in various ways. It is becoming a competitive feature of online courses and is transforming online classroom experiences. Today, people who buy online training not only want to get some information, but they also want it to be personalized. In this context, integrating machine learning technologies becomes a requirement to improve student performance and manage online education.
