Machine learning (ML) is already widespread throughout the business world. ML helps companies in everything from anticipating market changes to streamlining administrative work, and its benefits may not end there. What about other applications, like using machine learning in education?
Education is an essential but notoriously complicated process, which makes it seem perfect for ML. AI applications like ML tend to excel at making connections that humans may miss. With that advantage, machine learning in education could have to potential to revolutionize the sector.
A Refresher on Machine Learning
To understand how ML can improve education, you should know what exactly it is. Machine learning is subset of AI that focuses on finding patterns in data to discover underlying truths. Whereas traditional tools find answers from data based on rules, ML finds rules from analyzing data and answers.
Finding a solution to any problem requires an understanding of what the issue is and how things affect one another. That’s where ML comes in, which makes it ideal for addressing complex issues like education. Here are three areas of potential for machine learning in education.
The first and perhaps largest advantage of machine learning in education is personalized learning. One of the most significant challenges in education is that students learn in different ways. Using ML, teachers can discover what teaching styles are most effective for each student.
Machine learning systems can identify how different students respond in various circumstances, then suggest how to best reach each one. With the system’s help, teachers can then create personalized learning plans for each student to help everyone succeed. Individualized education like this will ensure that the school system meets each students’ unique needs.
Teachers don’t necessarily need ML to create personalized lesson plans. ML is still an advantage here, though, because it works faster, and possibly more accurately than people.
In business applications, predictive analytics is one of the most attractive advantages of ML. Being able to predict outcomes based on seemingly disconnected data is also beneficial in education. Just like how businesses can use ML to determine future changes in sales, machine learning in education can predict student performance.
The American Institutes for Research tested this idea in 2017 and found that an ML-based early warning system could be effective. The study showed that the early warning and intervention system reduced chronic absence and course failure within a year. By detecting signs of failure with ML, staff can intervene and help students improve their performance before they fail.
Schools could use systems like this to help more advanced students too. If they find early signs of advanced development, they could adjust students’ learning plans to help sustain their higher level of learning.
If nothing else, machine learning in education can make administrative work more efficient. From scheduling to classroom organization, there’s a lot of non-educational work that teachers have to do. ML can streamline these tasks, giving educators more time to spend on their students.
Machine learning programs could find new and improved methods of organization and scheduling. By adopting these new approaches, teachers would both create a more conducive learning environment and save time.
Machine Learning Can Shape the Future
The high adoption rates of machine learning in corporations shows that this technology holds vast potential. That potential isn’t limited to just business, though. If the success of ML in these areas is any indication, machine learning could improve education through many of the same areas.
The education industry is no stranger to technology, so integrating machine learning may be the next logical step. AI may not take over for teachers, but it will help them better equip their students for their continued education and careers.