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Machine learning for education industry: algorithms, advantages, predictions

Education

Machine learning

Machine learning seems to be another buzzword in software development from the first glance.But if we look at the statistics and predictions, it looks like utilizing ML and AI in our daily life will be the only future we can have. According to Grand View Research, the market size for artificial intelligence in education was valued at around 1.8 billion US dollars in 2021 and is expected to increase at a compound annual growth rate of 36% from 2022 to 2030.Even though it seems a bit too ambitious, the numbers are pretty likely to be real because the spread of ML and AI-based products and their efficiency has already shown a certain impact on the market and existing businesses. Most modern educational platforms implement machine learning solutions in one or another way to enhance customer experience and increase revenue at the same time, let alone for edutainment or pure entertainment.

It’s too costly to ignore ML for your EdTech app.

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Advantages of machine learning for EdTech

EdTech refers to online education, which boomed in 2019 as a result of the COVID-19 pandemic. That’s generally true because EdTech utilizes computer software and hardware to augment traditional educational theory and practice in the learning process.

Implementing machine learning and artificial intelligence was just a question of time. But how does it help students and tutors?

Automated text analysis

Automated text analysis allows tutors to give their students feedback in a swift and more precise form. As studied by several researchers, by utilizing supervised machine learning algorithms educational organizations can build systems that would be able to extract the informative part from the students’ texts, analyze them, and give certain feedback on their knowledge.Such solutions can not only increase the efficiency of learning by giving more precise and timely responses but also wipe out any misunderstanding or conflict between tutors and students by excluding any possibility of biased attitudes.

Personalized and adaptive learning

Such solutions bring more versatility into the learning process which can affect the overall efficiency of the educational process.

By utilizing personalized and adaptive learning tools, tutors and students can adapt to each other’s needs by adjusting the learning materials, schedule, and tempo. Students can pick the subjects they’re truly interested in while tutors are empowered to create more personalized learning experiences with increased effectiveness.

Increasing efficiency

Also, automating administrative processes with ML tools can dramatically increase the efficiency of education. Algorithms can do almost anything from manual routine work to tracking attendance to automatically sending homework and lectures to the students’ devices. With all of the resources saved, educators can dedicate more time to personal consultations or create more complex and practice-focused tasks for students.

Learning analytics

Learning analytics is also a great tool for providing more informative feedback to both students and tutors. By investigating an individual’s environment and progress, an analyst can see strong and weak points of one and bring them up which should result in adjusting the educational process to current conditions and increase the efficiency of studying.

Predictive analytics

With this tool, tutors can assist their students in pointing out strengths and weaknesses. For instance, such frameworks can predict certain success in learning one subject and flaws in another. As a result, students will be able to distribute their efforts much more efficiently during the learning process and, overall, education will be of higher quality.

Evaluating assessments

By utilizing the technologies mentioned above, machine learning and artificial intelligence tools can also improve the assessment process. For example, several types of tests can be automated and randomized with no loss in evaluation precision. Also, a combination of text analysis tools and learning analytics can be used to show students’ performance during written exams and how their environment affected their results.

Key technologies used in EdTech development

Python

Python is one of the most popular programming languages for creating and setting up ML and AI applications. It allows developers to build and deploy such solutions in a rapid and cost-effective manner.

Java

Java is a cross-platform language, which makes it great for web servers for educational platforms. Given a large number of libraries for machine learning, Java can be used not only for the web server of educational platforms but also used to build a system that helps to choose the right training course (as an example). Java servers keep the load well, so they are a good tool for creating streaming lectures, courses, and online learning.

Node.js

If compared to Python, Node.js can win if a product needs a swifter back-end. Also, this programming language has a great number of libraries that allow deploying complex solutions with integrated machine learning algorithms.

Google Cloud ML

Google Cloud is a great choice for a cost-effective solution that should be swift and easy to rescale. It won’t take any hardware capabilities while remaining fully functional. Their Vertex AI has built-in capabilities for swiftly deploying and easily maintaining literally anything in terms of ML in the cloud.

Computer Vision API

Microsoft Azure’s computer vision API can enhance learning by giving the tools for analyzing visual data such as pictures and videos. ML models trained with computer vision can perform a wider array of tasks in a range of fields.

AWS

Amazon provides software developers with an array of machine learning and data analytics tools. The most widely known are AWS Sagemaker and AWS Lex.

AWS Sagemaker is a convenient tool for developing, deploying, and managing machine learning and data analytics applications.

With AWS Lex, developers can create any dialogue-based extension to the current learning system from chatbots to voice-operated virtual assistants. This is a powerful tool for creating more immersive and effective self-educational tools.

Machine learning in education: success stories

As mentioned above, ML and AI-based solutions have already invaded our lives and they won’t change. Netflix’s recommendation system isn’t the only thing going on, nor is Google’s live captioning. Such solutions are already helping people learn.

Grammarly

Grammarly is an online writing assistant that can help you write clearer, more accurate, and more engaging texts.

They’re considered the best grammar-checking application for now and have millions of users across the globe.

SchooLinks

SchooLinks is a college and career readiness platform that assists school tutors in preparing students for real future life. It mixes traditional methodological tools with brand-new experiences for students and easier organization and maintenance for tutors.

Quizlet

Quizlet is an American company that empowers learning through flashcards. Students can learn topics on any subject in a personalized gamified form that increases the efficiency of gaining knowledge.

Innowise’s online coaching platform

Our software engineers have rebuilt from scratch an eLearning platform that allows students to engage in online courses and mentors to spread their knowledge.

The solution presents web and desktop applications that allow tutors to share their courses with students across the globe. To make the courses more suitable for each particular student, Innowise has implemented an ML-based recommender system as well as several data analytics tools that allow adjusting the content according to the users’ needs. As a result, the platform provides users with more personalized course recommendations and has shown an increase of 2700% in data analysis processes. You can learn more about the project at the link.

Machine learning is a part of Education 4.0

Technological advancements emerge daily and it’s pretty hard to predict whether a new tool or framework will fit into our lives perfectly or vanish in a blink of an eye. However, several things have made it and are not going to leave.

Machine learning is one of those. With so much information on the web and so many activities going on in the educational sphere, we all need some assistance in a brand new world of Education 4.0.

The thing is we already have tools for acquiring such assistance, we just need to accept them and introduce them to our daily lifestyles.

FAQ

How is machine learning being utilized in the education sector?

Machine learning is revolutionizing the education sector by personalizing learning experiences, automating administrative tasks, and providing data-driven insights. Adaptive learning platforms use machine learning algorithms to tailor educational content based on individual student progress, optimizing comprehension and engagement.

What are the benefits of integrating machine learning into educational tools and systems?

First of all, the integration of machine learning in education results in a more efficient, adaptive, and data-driven learning environment. The ML technology enables personalized learning experiences by adapting content to individual student needs, enhances student engagement, and provides real-time feedback. Machine learning automates administrative tasks, such as grading and assessment, freeing up educators’ time.

Are there any challenges or limitations in applying machine learning in education?

Yes, applying machine learning in education comes with challenges. Ensuring data privacy and security, addressing biases in algorithms, and maintaining ethical considerations are primary concerns. Additionally, integrating machine learning requires significant infrastructure and resource investments.

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Dmitry Nazarevich CTO

Date: Dec 08, 2022

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Machine learning for education industry: algorithms, advantages, predictions