Machine Learning in Education: How ML Is Empowering The Industry

6 min
Eduard Grigalashvili
Technical Writer
Mary Novak
EdTech Digitalisation Expert

Machine learning (ML) is used in the development of predictive AI and generative AI models that help a myriad of industries.

When it comes to education, ML offers invaluable benefits: from personalizing learning paths to tracking student performance to automating administrative tasks to identifying at-risk students. Machine learning algorithms transform traditional education, helping organizations streamline educational processes and deliver a better learning experience.

As a company that specializes in educational software development services, AnyforSoft would like to discuss the role of machine learning in education. In the following paragraphs, we will talk about how ML models are empowering the education industry while demonstrating the applications of the technology.

Without further ado, let’s get started!

Use case #1: personalized learning

Personalized learning experience is arguably the most significant advantage that machine learning and artificial intelligence bring to the education sector.

As discussed in our article about the future of EdTech, it’s challenging to personalize the educational process in a traditional learning environment. A teacher can’t physically pay attention to every student in their class while recognizing their preferences, needs, interests, strengths, and weaknesses.

With machine learning, however, the situation is different.

By implementing machine learning algorithms and feeding them data about students (historical performance, attendance, engagement, etc.), educational institutions can build student profiles and then use these profiles to design individual learning pathways for each student. This is what is known as adaptive learning. With this approach, each student will be able to learn at their own pace, which will most likely result in increased academic performance.

Khan Academy is a good example of successful personalized learning implementation. The platform uses machine learning and artificial intelligence to analyze students’ performance in practice exercises and provide personalized content recommendations based on this factor. This allows the company to ensure high student engagement and retention rates while increasing profits.

Use case #2: administrative task automation

Among the use cases of ML in education, we can’t fail to mention administrative task automation.

Every educational organization has to cope with tons of administrative work: student enrollment and registration, application processing, course management, attendance tracking—you name it. Such tasks are boring, tedious, and time-consuming. Yet, they’re quite important for an institution’s proper functioning.

Luckily, we were born at the dawn of technology and by applying machine learning in education, can automate most, if not all, administrative work.

Let’s take application processing, for example. Large higher education institutions receive thousands of applications a year. Harvard, for one, got over 56,000 applications for the class of 2027 last year, of which only 1966 were admitted. Now imagine yourself as a Harvard employee. You have to process each application and ensure it meets your institution’s criteria. Even if you could process 200 applications per day, it would still take you 280 days to finish the work.

That’s why machine learning models are so critical. ML can assess incoming student applications and extract relevant information from documents and forms in a matter of minutes. By doing so, they automate application processing and initial screening, taking the load off human employees.

Now let’s take attendance tracking. This process can also be automated, in a rather fancy way. We’re talking about facial recognition technology. This technology uses machine learning to identify patterns in our facial features and thus recognize us.

Organizations have long been using cameras with facial recognition for security concerns. However, these cameras can be also used for attendance tracking. Instead of manually documenting each student’s attendance, educational organizations could install cameras with facial recognition software on campuses, which would allow them to automatically track student attendance. Even though this approach is not quite common in the educational sector yet, we believe it will gain more prominence in the future.

Take any administrative task of your organization—we would wager that with proper implementation of machine learning, this task can be easily automated.

Use case #3: identifying at-risk students

Student success is imperative for every reputable educational organization. Many use machine learning to identify at-risk students and take preventive measures before they drop out. Yes, machine learning can assist even with such a complicated task. By collecting academic records, attendance records, behavioral data, socioeconomic factors, and demographic information, machine learning algorithms can identify students at risk of dropping out, allowing teachers to intervene timely.

There are already some successful examples of using machine learning in education for that purpose. In Illinois, the State Board of Education has designed an early warning system for at-risk high school students. It uses machine learning. This system is sophisticated enough to not only identify at-risk students but also provide teachers with suggestions as to how they should intervene to get these students back on track.

This particular application of machine learning in education finds its use not only in identifying at-risk students. It can also determine the factors that contribute to their dropping out. Since there may be a great number of such factors, a machine learning model can be helpful in narrowing them down and even indicating the most prominent factors that will likely lead to the dropping out of a particular student. If educational organizations address these factors properly, they will be able to significantly reduce the likelihood of the student abandoning education.

Benefits of machine learning in education

Based on the applications of machine learning in education described above, let’s sum up the benefits the technology brings to the education sector:

  • Personalized learning experience: ML algorithms can easily analyze the preferences, learning styles, strengths, and weaknesses of each student and recommend educational content that best aligns with their personas. That will allow organizations to increase student retention and engagement, leading to higher profits.
  • Automation of administrative tasks: Using machine learning in education, institutions can automate most of their administrative tasks, including application processing, performance monitoring, attendance tracking, and so on. This will reduce the workloads of human employees, allowing them to focus on more important and creative tasks.
  • Early intervention for at-risk students: By analyzing academic, behavioral, and demographic data, ML models can identify at-risk students and suggest intervention strategies that will help retain these students.

In addition to the already-mentioned benefits, machine learning in the education sector also helps with resource allocation. By analyzing data on resource usage, classroom occupancy, and equipment utilization, ML can suggest ways to optimize resources, helping educational organizations cut their expenses.

Machine learning for education: implementation challenges

Machine learning is not all roses. Even though the technology brings a myriad of benefits to pretty much any industry, its successful implementation is challenging and requires significant investments. Let’s review some of the challenges associated with ML implementation:

  • Data quality and data security concerns: To train an ML model, one needs a lot of data, and that data must be of high quality. If the model is based on inaccurate data, it will make faulty predictions. Obtaining quality data is difficult, but it’s even harder to ensure there is no sensitive or personal information in the training datasets. Learning experience enhancements shouldn’t come at the expense of personal data security.
  • Lack of technical expertise: Even though machine learning is by no means a new technology, it is hard to find developers with enough expertise to build sophisticated ML algorithms. And even if one manages to find them, they come with a high price.
  • Need for constant supervision: Biases and inaccuracies in the algorithm can go unnoticed if the system is not supervised by someone who understands the technology and knows how it works. ML models require constant supervision, audits, and maintenance to work properly. If an institution delegates a complicated task to ML software, this can lead to disastrous results. To illustrate: the International Baccalaureate implemented a fully automated ML-based scoring system in a classroom during the COVID-19 lockdown, which often gave incorrect grades, causing outrage among students.

Wrapping Up

Machine learning and education perfectly complement each other. With the help of ML algorithms, educational institutions can streamline many processes while improving the learner experience. But despite all the benefits of the technology, many organizations still remain “old-school” and don’t rush to embrace technological advancements. Better for you—if you implement machine learning, you will get a significant competitive advantage over them.

So if you’ve been considering empowering your organization with machine learning for a while, consider this article your wake-up call.

AnyforSoft has over 12 years of development experience and can help you with projects of any complexity. As a client-oriented company, we put our customers’ product vision at the forefront, developing solutions that fully match their needs and requirements. Over the last decade, we’ve successfully delivered over 150 projects, helping companies achieve digital excellence.

We can help you as well—just contact us and tell us about your project.

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