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The Future of Machine Learning in Education
Artificial intelligence and Machine Learning have brought technologies to its peak. With both technologies touching the different aspects of our lives, including education, we have seen our lifestyles change right before our eyes. Online modules, discussion forums, and the communication between students and teachers after class hours, have helped shape education to an industry with a lot of potential. Even though it has made student life easier, there still are certain features of machine learning that have not yet been tapped.
Machine Learning is setting its own path for a newer, more personalized learning experience. It promises to help improve the engagement, communication and grading systems of students and teachers. ML as grown from being a simple thought to an entire industry, and continues to grow as technology makes progress.
Robotic Process Automation (RPA)
Robotic Process Automation is an important part of Machine Learning in the education sector. Through this technology, large chunks of data specific to a particular student, can be accumulated. With the collected data, an experience that fits the student's needs is offered, thereby creating a better learning experience.
The intelligent automation company WorkFusion has built a platform called RPA Express, that assists lecturers by using smart algorithms that decide which teaching methods are going to work on a student. These not only help in personalizing learning experience, but also help the teacher aid a student who is either differently-abled or a student who may belong to a different learning background.
This would lead to better grades, the development of a useful skill set for the real world, and a higher chance of finding a career path that would suit each student.
Here are 4 ways Machine Learning will transform the education industry:
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Customized Learning Experience. Machine learning for education was to built to develop reports and logs, deliver concepts and set up goals that fit a student's strengths and learning backgrounds. Soon, it will also give insights to professors about the courses that are being consumed and those that aren't, what methods work the best with their students, and how to make their course material better.
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Predicting Career Paths. Soon enough, ML platforms will be able to assess a student's career path using the long list of college documents they would have to send. This would also help to decide which career path they would succeed most in. These platforms would use standardized tests, letters of recommendation, essays, and college applications to come up with potential career paths suitable for each student. Furthermore, the technology would also predict areas that trouble the student, and suggest a form of tutoring to work on the student's weaknesses. Since students will be expected to have the required skills before setting foot into the corporate world, the ML platform would suggest methods to maximize on their strengths and interests, and work on their areas of weakness.
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Less to no Bias in Grading. Machines will soon find their way to the teacher's desk for assignment grading and report generation. When grading, the Machine Learning algorithm would help to detect for any plagiarism or other violations. These robots would be able to offer the right grade, along with a report of the student's performance and where they could improve to reach their desired goal. Humans tend to be a little biased, which further emphasizes why we need to improve existing technology. Grades will be offered based solely on performances, but a lecturer's suggestions would still be required to recheck whether the essay question has been fully answered, or other factors like class participation and behavior.
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Scheduling Appointments - a click away. Scheduling appointments between students and teachers can get a little messy. Machine Learning has a feature that helps fix logistical issues. By automating the process of scheduling appointments, these machines can now create organized schedules for both the student and the teacher. In this software, the student can select a certain time and date they would like to meet with the teacher, and the algorithm would then take care of the rest. This would make sure students can have personalized schedules based on commitments, needs and pace of learning.
Technology today has grown at an exponential rate, and continues to do so, as newer opportunities to better what now exists arise. I hope you found this article to be interesting and informative. Here are a list of related articles that you could check out: