Machine learning is known to be that arm of computer science that utilizes mathematical principles to help computer systems become markedly better performing on detailed data-driven tasks without any ambiguous programming. Good examples of the incorporation of machine learning into education are the use of artificial intelligence and learning analytics.
Some important machine learning applications in the education sector include:
Adaptive learning: In this educational method, a student’s performance is analyzed in real-time, and teaching methods and the curriculum are modified based on that data. It tries to adapt to the individual student for better education and helps to build a personalized engagement. The software helps in advising learning avenues that the student should take.
Increased efficiency: Machine learning appears with the capability to better organize and manage content and the curriculum. It helps to bifurcate the tasks accordingly and understand everyone’s potential. It helps to identify what works for the student and what work is ideal for the teacher. It also has the ability to make teachers more efficient by completing tasks such as scheduling, classroom management, etc. Therefore, the teachers are free to concentrate on tasks that machine learning cannot accomplish and require a human touch.
Learning analytics: Sometimes, teachers get stuck while teaching. As a result, the students cannot properly understand the insights and gist. Learning analytics helps teachers gain insight into data and perform deep dives into it. They can sift through lots of pieces of content, interpret them, and then make connections and conclusions.
Predictive analysis: Predictive analysis in education revolves around knowing the needs and mindsets of the students. It helps to make conclusions about the occurrences that may happen in the future. With the half-yearly results and class tests, it can be understood which students will have a tough time in the exam and which students will perform well.
Personalized learning: This is one of the best usages of machine learning in the education field. It’s customizable and individual requirements can be taken care of through this. The students can guide their learning through this educational model. They can make decisions about which subjects to learn and how to learn.
Evaluating assessments: Machine learning is used to grade exams and student assignments more accurately than a human can. While some inputs from educators are required, the final results will have higher reliability and validity when a machine does the work as there’re fewer chances of errors.
