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. You can use adaptive learning to develop the perfect grading calculator.
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. You can use learning analytics to create the perfect high school GPA calculator.
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. For instance, machine learning can assess an argumentative essay with ease.
Concluding thoughts
The advent of artificial intelligence and machine learning is changing education as we know it. It makes the job of an educator much easier and makes it easier to help children live up to their academic potential. Some educators believe that artificial intelligence and machine learning will eventually take their job, but this is a fallacy. AI can never take the place of an educator, but it can make an educator’s life much easier.
Instead of fighting the AI wave that is sweeping the U.S, teachers should embrace it. AI does a good job of making a teacher’s job easier and automating mundane tasks such as grading. If I were a teacher, I would totally embrace AI instead of fighting it.
I hope we did a good job explaining the ins and outs of machine learning. If you have any questions, comments, or concerns, feel free to leave them below. We will be sure to get back to you in a timely manner.
Story by Matthew Lynch
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