- Adaptive learning
Adaptive learning capabilities made possible by advanced machine learning algorithms analyze students' performance in real-time and modify the curriculum and teaching methods based on data. This helps personalize the engagement and makes the curriculum adapt to a student's needs for better learning. The algorithms suggest learning paths that a specific student should follow and books or materials that are good for a specific student.
- Increasing efficiency
Machine learning can improve content and curriculum than any other method. It helps divide content accordingly by first understanding the potential of every student. This enables analysis and identification of what will work best and what is suited for teachers and students. Machine learning eases the work of teachers and students and makes them happy and comfortable with learning. It also increases the rate of involvement and their love for participation and learning, increasing the efficiency of education.
- Learning analytics
Teachers often encounter problems while teaching. Most of the problems are associated with the understanding by students and knowing if they understood what they were taught or not. With learning analytics, the teacher can gain insight into data and understand their students better. By going through data and interpreting it, they can make connections and conclusions that impact the learning and teaching process. Learning analytics can also suggest paths that a student can follow and the benefits of doing so.
- Predictive analytics
Predictive analytics entails knowing the mindset and needs of students. With machine learning, teachers can make conclusions on the things that are likely to occur in the future and design a syllabus to accommodate them. With the learning and tests that students take, the teachers can easily predict students' performance in the final exams and the students that will excel. Such information helps the faculty and parents take appropriate measures to help students improve in their weak areas and perform well.
- Personalizing learning
This is perhaps one of the leading advantages of machine learning in education. With machine learning, the learning patterns of students can be understood and their requirements taken into account. Through this method, students can learn on their own and at their pace. They can also decide what to learn and what not to learn. Students can choose the subjects they are interested in and the curriculum or pattern they want to follow.
- Evaluating assessment
Machine learning in the form of AI is used to mark exams and grade students accurately and faster than humans can do. This is a solution to the OMR answer sheets, which was the only technology available in schools to mark the exams. Apart from being faster, machine learning does not require too much human intervention and is reliable because of low chances of error.
With the increasing awareness of technology in education, changes are visible for everyone to see. Although the use of technology in education is still in its early stages, educators realize that machine learning can revolutionize the education field for the better. Apart from the above aspects, machine learning can also enable automation of tedious, time-consuming, and costly tasks. As e-learning continues to gain traction, the number of machine learning applications is likely to rise, which is likely to alter the education landscape.
