Machine Learning in Education: Its Definition, Application and Role

Schooling as we know it has been typified by its aspect of physical grind on the part of both educators and students. Teachers cull and prepare educational materials, manually grade students’ assignments, and provide feedback on the learning progress. They sometimes grapple with an unmanageable number of students or a wide gulf of varying student learning capabilities and levels in one classroom. On the other hand, students have been generally pushed through a one-size-fits-all gauntlet of learning, not personalized to their abilities, needs, or learning context.

However, as the contemporary classroom becomes more digitized, recent advances in artificial intelligence (AI) and machine learning are closing in on addressing historical ‘hand-wrought’ challenges. These technologies not only collect and analyze data that students generate when interacting with digital learning systems but also pull in large swaths of data from other learning areas, such as students’ demographic data, educators’ performance data, and admissions information, and so forth.

But what is machine learning to begin with?

Machine learning is a method of data analysis that automates analytical model building. Machine learning algorithms that iteratively learn from data enable computers to find hidden insights without explicitly being programmed where to look. In simple terms, it is a concept that lets machines learn from different examples and experiences. As long as data flows, the application of machine learning is endless, and we already see it everywhere, from Facebook algorithms to self-driving cars. Today, let’s examine machine learning and its application in education.

Application of Machine Learning in the Education Sector

Machine learning in education may seem like a buzzword aimed at forcing entrepreneurs to dig into their pockets and innovate for innovation’s sake. But in reality, it’s far from it and quite the opposite. Organizations have already introduced machine learning into their practice and recognize its effectiveness in bringing numerous benefits to the table. And what’s more important, it is true for popular brands with huge budgets and promising startups with minimal funding. Simply put, any organization can squeeze the most out of this innovative technology.

Machine learning is transforming education and fundamentally changing teaching, learning, and research. Educators are using it to spot struggling students earlier and take action to improve success and retention. Researchers are equally accelerating machine learning in education research to unlock discoveries and insights. Machine learning in education is expanding the reach and impact of online learning content through transcription, localization, text-to-speech, and personalization. Companies like Amazon are working with leaders in the public sector to adapt to the new world of machine learning and better equip students with the skills and expertise they need to succeed.

The Use of Machine Learning in Education

Education and machine learning revolves around the same concept – learning. Now that we have established that, let’s cover the most prominent applications of machine learning in the educational sector.

Predictive Analytics

Predictive analytics is an everyday use of machine learning in education that is all about knowing students’ mindsets and needs. It helps educators make conclusions about things that might happen in the future. With class tests and half-yearly results, educators can identify students who will perform well in the exam and those likely to underperform. This information alerts faculty and parents, which helps them take appropriate measures. Therefore, machine learning in education can provide the following important insights:

  • How to increase engagement in class
  • Who is likely to perform best or worst in exams
  • What percentage of students will drop the course
  • What percentage of students will ace upcoming tests
  • If students can sufficiently undertint and engage with course materials

For example, Western Governors University in Utah uses predictive modeling to improve retention by identifying at-risk students and developing early-intervention programs. Initial efforts raised the graduation rate for the university’s four-year undergraduate program by five percentage points between 2018 and 2020.

Personalized Learning

Personalized Learning remains one of the best, most valuable practices of using machine learning in the education sector at the moment. Before machine learning applications, it was nearly impossible to process and adjust learning material to fit each individual in class unless it were one-on-one private lessons. With machine learning, there is a possibility of a more targeted approach that allows students to follow the material at their own pace or even choose their course progression and preferences.

Professional Paper Writing Services

Although AI is a relatively new technology frontier since it has been around only for a few decades, it has already made significant breakthroughs in professional essay writing services and other forms of online content generation that could be easily considered AI-generated. While this newest advancement may seem like something from a sci-fi movie where computers program themselves independently, you need not be left behind in today’s fast-paced world. There are plenty of services you can use to get your piece written by AI academic writers or intelligent programs. For instance, Custom Writings is a top-rated academic essay writing service that creates unique, plagiarism-free, and grammatically correct papers for you on numerous distinctive topics, such as education, medicine, technology, history, and literature. The company uses machine learning technology to check for similarity indexes, ensuring plagiarism-free of your essays and research papers. They also apply grammar checkers to enhance the quality of their custom writing services.

Adaptive Learning

As its name suggests, adaptive learning analyzes students’ performance in real-time and modifies teaching methods and curricula based on given data. It provides a personalized engagement and tries to adapt to the individual for a better education. The software assists in suggesting learning paths that students should take. It recognizes if students are struggling with educational materials or perhaps those that are too easy for them, allowing necessary and early adjustments to prevent performance issues. It can also detect more minor details in class, including outdated curriculum or uneven distribution of teachers’ attention. Adaptive learning can be integrated into a supplementary technological solution for a classroom or a separate educational system.

Learning Analytics

Machine learning in education helps handle vast volumes of data generously accumulated across all establishments and e-learning platforms. This data is so immense that a human specialist cannot process it in years even if they wanted to. Machine learning is a real-life saver in collecting and analyzing such big data rapidly. It often happens that educators get stuck while teaching, making it difficult for students to understand insights and gist. With learning analytics, teachers can gain insight into data and perform deep dives into data. They can sift through immense content, interpret it, and then make connections and deductions. This process can significantly impact teaching and learning processes.

The Role of Machine Learning in Education

Many higher-education institutions now use machine learning as an integral part of their academic processes. Educational institutions are seeing the value of data-backed solutions, whether the goal is to identify and better support pain points in the student journey, efficiently allocate resources, or improve student and faculty experience. Machine learning can play a critical role and bring tremendous benefits to the learning environment

Improve student outcomes

Machine learning is transforming how education institutions track student performance and spot issues. Machine learning-powered personalized learning approaches enable educators to tailor learning pathways to individual students.

Increase online learning reach

Deep learning-powered translation, transcription, and text-to-speech services provide accurate and low-cost options to make content accessible to students around the globe.

Accelerate research and discovery

Machine learning is moving out of the computer science department across all research disciplines. Machine learning in education research helps scholars without deep data science, and machine learning expertise can leverage the technology to accelerate research and drive discovery. Machine learning is improving content search and discovery for scientific and research document repositories.

Improve operations

Machine learning can be used in education to attract the right students for admissions and accurately forecast enrollment to optimize capacity. Prevent fraud and protect student and staff safety both online and offline.

Customize and personalize the learning process

Machine learning and education processes are closely interconnected. It helps the system either draw the user back and go through some learning points again or let them step further. The technology also allows teachers to monitor and trace the learning process individually.

All-round content analysis

Using machine learning in education helps to move the online learning industry to a new level by making the content more up-to-date and relevant to an exact request.


Those at the forefront of the machine learning trend are focusing on harnessing analytics to increase program personalization and flexibility and improve retention by identifying students at risk of dropping out and reaching out proactively with tailored interventions. Indeed, data science and machine learning may unlock significant value for learning institutions by ensuring resources are targeted toward the highest-impact opportunities to improve access for more students, as well as student engagement and satisfaction.