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What Role Does Machine Learning Play in The Education Industry in 2020?

What Role Does Machine Learning Play in The Education Industry in 2020?

Today a lot of processes and events gradually move online. For various reasons it becomes much easier and more effective to do things remotely, hence we work online, we study online, we even visit museums, music concerts, and theatres online. Due to this trend, the competition in the e-learning market has become tougher. Educational platforms need to offer the easiest and the most engaging ways of delivering knowledge to its users, and reflect the education system offered by schools and universities — have different levels, scores, and, the most valuable feature for today, possess personal track of each student’s learning process.

To ensure these features work properly, the online learning process incorporates lots of smart technologies. One of those is Machine Learning. Over the past decade it has become an inevitable part of every well-established e-learning website, as they need2efficient processing of big data. So what are they? What are the applications of machine learning in online education? Let’s get inside this topic step by step together.

What is Machine Learning?

The technology is a branch of Artificial Intelligence. In simple words, ML gives IT systems an ability to learn or interpret data on their own and self — educate relying on the experience they gained. A quick example — a machine translation service called DeepL, which is a great competitor to Google translate. The application technology studies different ways of interpreting texts into English compares most popular patterns and based on this data offers texts that need a minimum or no editing at all.

The main principle of machine learning is to access the necessary data, analyze it and create a pattern «problem + solution» or cluster it by different categories. No human aid, no external coding — all is done by machines only. Among the most common examples of how the technology works in real life are the image and voice recognition. By using a set of statistical tools these technologies allow IT systems to interact directly with consumers and render them the necessary services.

There are several algorithms of machine learning that are considered to be the most widely used. Structurally they are divided into four categories — supervised, unsupervised, semi-supervised and reinforcement machine learning.

Types of Machine Learning Approaches

Supervised

These algorithms apply already received knowledge to the new data using labeled examples. It allows them to use the existing experience to predict situations in the future. In other words, supervised machine learning allows us to collect data or produce a data output from the previous experience. The simplest example here is email filtering when the algorithm recognizes undesirable incoming messages and puts it into the spam folder.

Unsupervised

This algorithm is applied in those cases where the data used in the machine learning process can be neither classified nor labeled. It uses two types of information processing — clustering and association. No answers are given, no relevant experience to repeat. This algorithm explores the data and makes conclusions based on the examined information, discovers and presents the interesting structure in it. The main asset of unsupervised machine learning is to find all kinds of unknown patterns in data and segment them, divide into groups by type. It is widely used in data mining.

Semi-supervised

This algorithm is applied in cases where some information can be labeled, but only partially and the main set of data cannot be determined. In other words, semi-supervised machine learning helps to classify some of the unlabelled data with the help of the information that can be labeled. The classic example is a speech analysis.

Reinforcement machine learning

This algorithm is based on the interaction of the IT system with its environment and discovering errors and rewards. It helps to outline the best behavior models for different scenarios relying on simple reward feedback, that teaches the system to choose the most appropriate actions. To give an example, let’s imagine a system encounters an error while performing the action or a reward for achieving the most favorable outcome. Thus, the program is able to learn the most effective approach via «reinforcement signals».

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Machine Learning is deeply integrated into almost every large-scale IT service platform like Google (that improves its search engine functionalities with ML), Amazon (it uses ML to suggest you the products you might want to buy) or Netflix (it builds personal playlists using ML technologies). In the sphere of online education, machine learning and artificial intelligence have been there for almost 20 years already. The most common ways to apply these technologies are learning analytics and educational data mining. With the development of virtual personal assistants like Siri, in the nearest future the function of guiding the users and offering them support by request will become another inevitable part of the e-learning process.

What Benefits Will the Education Industry Gain From The Introduction of Machine Learning?

Machine learning for education. So, what does it bring? It is already changing the global landscape of online education. As per the research report released by Technavio, the global market of online courses based on machine learning technologies will grow with the CAGR higher than 16% by 2021! This rapid growth is bound to happen due to affordable large volume data storage and its efficient processing.

Machine Learning courses market research

From the perspective of making the e-learning process more engaging and result-driven, machine learning and artificial intelligence are already turning it into a more convenient way of getting the knowledge as compared to usual courses or corporate training. What are the features that they can offer to both sides of the process — students and universities? Let’s go point by point.

1. Learning process customization and personalization

Machine learning and education processes are closely interconnected. ML algorithms analyze how the students perceive and explore the information they are given. It helps the system to either draw the user back and go through some learning points again or let them step further. ML also helps the teachers to monitor and trace the learning process individually. As compared to traditional methods in classrooms, where the goal is to deliver the course, but not ensure that everyone got it, ML gives an advantage of more profound information perception. This type of learning is offered by EdTech and MagicBox learning systems.

2. All-round content analysis

Using machine learning in education helps to move the industry of online learning to the new level by making the content more up-to-date and relevant to an exact request. How? ML technologies analyze the content of online courses and help to figure out whether the quality of the offered information meets the applicable standards on one hand, and on the other, it shows how the users perceive the data and do they understand what is taught. Hence users receive the information under their personal needs and abilities, and the overall learning process improves greatly.

Have a quick look at Udemy, one of the world’s largest educational marketplaces, which connects students and instructors and offers over 150K different courses. It boasts a great feature of personalized recommendations. By answering several simple questions, its users get an optimized set of different courses and relevant content upon their request. The feature is a good contribution to platform usability.

3. Better Grading

There is another application of machine learning in education that deals with scores and grading. As each online course reflects the learning abilities of a large number of students, they have a lot of experience in grading them. ML technologies turn the grading process into a couple of seconds issue. We’re speaking more of the exact sciences in this perspective. There are spheres, where machines cannot replace teachers, but even in those cases, they can help to improve the existing grading and evaluating approaches.

In addition to that, applying machine learning in education today is the easiest way to detect cases of plagiarism. The application of ML technologies in this sphere turned out to be highly revenue-generating. Would you believe that a company that uses ML technology to reveal plagiarism costs $1.735 billion? A year ago a project called Turnitin was purchased by a privately held media company exactly at this price.

4. Higher ROI of e-learning

Machine learning in the education sector is another driver to increase ROI. How would you ask? Let’s go back to the retention rate problem. As per the Massachusetts Institute of Technology research, over 96% of MOC (Massive Online Courses) students give up their courses for different reasons. Here’s the area where ML changes the situation drastically.

When the technology is applied, the users can get personalized support from the machine learning software and they don’t have to waste their time online waiting for the information they need. Advanced course platforms offer multiple benefits for students. For greater details on the real-time benefits that e-learning brings, check our e-learning in education guide.

Going back to the advantages of machine learning in online education, it’s worth mentioning predictive analysis, that helps to track the progress of each user. This information gives an exact input to e-learning companies on how to produce the necessary content and utilize their eLearning resources at the required time and place. This benefit is crucial for the corporate sector where the issue of budget and time resources matters much.

A quick example of predictive analysis use based on machine learning technologies — a project called My Learning Hub. It offers a bunch of different corporate training programs and solutions. Each of them has a personal track of the students' progress and their current needs and requests.

5. Personal e-learning courses

Here both machine learning and artificial intelligence help to give each student a personal course. It is compiled based on the exact user’s request — rather a time-saving solution! The program gives precise answers to the users whenever they need support.

As still machines cannot replace humans, this kind of studying is applicable only for those disciplines, where there are exact answers and it will not help to explain some philosophical questions, for example. But the amount of data it can offer to precise requests is astronomical! Have a glance at the Wizcabin project, in which the e-learning automation tool converts its users' storyboards automatically into interactive courses.

6. Time-consuming tasks automatization

Administrative work is extremely important and extremely boring. Let the machines do this kind of task! ML helps to automate scheduling and content delivery processes and free your time and resources for other, more engaging parts of e-learning process development.

Eager to see how your e-learning project would look like, if you incorporate ML and AI technologies?

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How to use machine learning in e-learning?

Over the recent decade a great number of machine learning projects in education appeared, especially in the corporate sector, including enterprises, small and medium businesses, and even startups. The technology itself has a great resource for different applications in the sphere of online learning. It would be quite funny to try to enumerate all of its possibilities as its potential goes far beyond.

There are some applications that today can be named the must-haves for a competitive e-learning platform. Let’s say they are the most frequent machine learning use cases in education. What are they? Scroll on to see.

Applying machine learning in education

1. Introducing personalized learning

Using machine learning mechanisms you can focus on the individual needs of each student. It is already possible to offer them individual instructors, testing, and feedback. With the development of AI technologies, in the future, it might become realistic to consider and analyze user’s facial expressions, which would be a big breakthrough in the whole industry. Let’s wait and see!

2. Use of virtual assistants

«Hey, Siri! What’s on for today in marketing?» So many learning materials already exist in digital format. Texts, videos, tests, podcasts — you can offer plenty of options. To understand what is the exact request of a student, you can incorporate a learning assistant into the process, who will guide users and answer their requests.

3. Content customization

In the e-learning process, the information is not considered to be boring or useless in case it is relevant to an exact need. The use of machine learning can help you to sort out only the necessary data out of the big amounts of content and arrange it in a customized course. In other words, you can benefit from ML by making your content smarter.

What are the Challenges Faced by Artificial Intelligence and Machine Learning in Education?

Challenges Faced by Artificial Intelligence in Education

The use of machine learning for education enhancement and optimization leads to outstanding results! But there are not only benefits but also serious risks of machine learning and artificial intelligence application in e-learning. If these technologies could solve all the problems and replace the traditional system of education, it would already have happened. What are the problems of ML and AI in the education sector today?

1. Prediction accuracy

Machine learning for education algorithms analyze the interests of online students, make predictions about their further needs, and give recommendations on courses. The statistical tools here cannot always reflect real needs. Let’s imagine, we see a student, who sits in a noisy place like a cafe or some workspace, and he chooses a text format of information delivery, as right at the moment it suits him better. We cannot say that he would make the same choice at home.

Thus, the student’s preferences are not that vivid for machine learning and prediction scenario is not 100% accurate. The reason for that is that ML doesn’t have enough data for use. To make predictions, people use information that they see every day, remember from the past, dealing with it during some period. To make this information digitally accessible, we need to collect and describe almost every moment of a student’s life, which seems to be impossible in today’s world.

Furthermore, it is not that easy to recommend a course and be sure that you offer the content that is really needed. Quite often learning management systems rely on the student’s job title and role, the department he\she works in and some skills they list. Their existing experience and the true level of knowledge are not always reflected correctly by a machine and here such things as instructor observations and feedback from peers and managers cannot be done by a machine with the same accuracy a human can render.

2. Human factor and old-fashioned approach to e-learning

The methodologies used by teachers and professors are highly adaptive when used in a classroom. When it comes to the remote educational process, we face an issue of behavior patterns and individual approach. The real-life process allows us to see how two different people perceive the same data and amend the approach if needed.

IT systems can measure the progress of each student and correct the learning plan, but they cannot invent the methodology on their own, and in some cases the approach that ML and AI are taught is not the best and rather old-fashioned.

In future this issue can become a great driver of methodologies further development as many non-vivid problems of the present system will become obvious and IT systems will offer even greater education methodologies. Until it didn’t happen, the human factor here is an issue.

3. Adaptive learning development costs

This is the model when machine learning is used to run the interaction between student and e-learning platform, analyze the unique needs of each user and offer customized resources and learning activities. No doubt this is a great technology and in the nearest years it will be more widely introduced by the key e-learning web platforms.

But as for today, the development of this approach is quite costly and time-consuming. The most expensive part here is the granularity of content. This system requires constant updating and monitoring, as the developers need to understand how deep the information adaptation of each learner should be — curriculum, course, or model level? Rules and predictions also need continuous attention of developers, and these things altogether form quite a big budget.

4. Data cleansing

Speaking about technological challenges, it’s worth mentioning the problem of data cleansing. The process of figuring out which data is incorrect or unnecessary and its removal is crucial for making the educational content relevant and up-to-date.

But which data and in which volume is needed to improve the learning process? What are the criteria for its relevance? The methodologies are still not perfect and today they cannot replace humans in these processes.

Examples of ML Applications in Education

Machine Learning use cases in education

Moving forward from descriptions to examples, let’s check it out, how are machine learning and education interconnected? Who is creating these applications and how do they work?

  • Content Technologies, Inc. — one of the world’s leading AI and ML technologies developers that render solutions for business processes automation and intelligent instruction design, is offering two platforms that are interesting to look at. The first one is called Cram101. The web platform uses ML to segment the content of textbooks and outline the most useful parts of it, create study guides, make chapter summaries, and multiple choice practice tests.

    Quite a similar service is offered by JustTheFacts101 service. It also highlights the most significant parts of the text and makes the summaries of chapters. Then it archives the data and makes it available on Amazon.

  • The e-learning platform called Netex Learning makes it possible for the educators to create digital curriculum, and offer different content across platforms and integrate different forms of information delivery — video\audio\texts. The company has also invested in the development of personalized learning cloud platform, that is applicable in the workplaces, where employers can design customizable learning systems with apps; gamification and simulations; virtual courses; self-assessments and others.
  • A company called OTTER is offering a great service based on AI. They’ve developed an application that allows users to set the keywords in advance of recording a class or a course. It’s great to support for getting exactly what you need in a nice summary.

Companies That Use Machine Learning for Education Projects

Machine learning is widely used in many spheres. Let’s have a quick look at a couple of examples of how it is applied in education projects by businesses.

  • A Swedish EdTech company called Hubert.ai has developed an assessment system that they call a cognitive assistant. Its goal is to turn the surveys into speaking tests by asking students follow-up questions. It uses machine learning algorithms to make a deep text analysis that automatically categorizes feedback. It helps to make an assessment in education much more engaging and effective.
  • Getting ready for school examinations becomes much easier with ML and AI technologies. A project called MobyMax uses these technologies to compile a highly adaptive curriculum for K-8 subjects.

    It provides personalized lessons for each student based on their individual needs and helps to fix learning gaps. The users have their personal lesson plans and practice sheets. The application also features different contests, certificates, games, and daily smiles to motivate students not to stop their studies.

  • A popular education tools platform called Quizlet Learn has developed a service for students based on machine learning algorithms, that help not only to study but also to prepare for different tests. It does this by creating tailored study plans based on current knowledge levels and upcoming test dates. The core of this algorithm is books and paper summarization and the creation of smaller study guides for textbooks and revision materials.

Conclusion

The most competitive feature of any educational project today is the ability to offer customized content in an engaging format with personal guidance and support. Some 10 or 15 years ago, which is nothing for the conservative sphere of education, the online world wouldn’t be capable of offering this kind of education.

Massive implementation of machine learning in education has changed this situation and it has completely replaced humans in many spheres of the learning process. It’s not only better data processing that they can do but a real enhancement of the e-learning industry. The content becomes more relevant, place, and time do not matter anymore, students have virtual assistants that guide them all way long.

The capabilities of these technologies and their potential make them the most significant tools of transforming the whole e-learning industry and bringing the tomorrow of e-learning closer!

Our digital product development team knows how to make these technologies work for you and turn your e-learning project into the most annoying competitor to the websites that you benchmark today!

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