Education - 4 Ways Machine Learning Is Shaping the Future of Education - B-AIM PICK SELECTS

October 13, 2020

The education industry has embraced recent technological advancements with open arms with the inclusion of online modules, topic-based discussion forums, and the option of communicating with lecturers after hours. While these developments have made the learning process more comprehensive and simplified for students, there’s still a lot of untapped potential in the industry.

Machine learning is blazing paths for new, more personalized learning experiences that have the potential to improve student engagement, create clearer communication channels between lecturers and students, and to develop less biased grading systems.

Robotic process automation (RPA) is an essential part of where ML is headed in the education industry, as this technology can amass large chunks of data pertaining to students and offer them an experience that fits their needs.

For example, intelligent automation company WorkFusion has an advanced platform called RPA Express that uses smart algorithms to determine which teaching methods are likely to work on each student. Technological advances such as this one are allowing lecturers to aid students who may have a disability or a different learning background to grasp the concepts of their classes with higher accuracy.

This ultimately leads to better grades, the development of more applicable skills in the real world, and a higher chance of finding career paths that suit each student.

Here are four ways ML is transforming education as we know it today:

A More Customized Learning Experience

Machine learning has the potential to develop detailed logs for each students, delivering them concepts and establishing goals that fit their strengths and learning backgrounds.

The technology will soon be capable of helping lecturers gain an understanding of how every concept is being digested by every student. The idea is to give educators an idea of what methods are working the best with their students and which aren’t, offering adjustments that may help students grasp course material better.

The future of machine learning is on the edge. Subscribe to the Fritz AI Newsletter to discover the possibilities and benefits of embedding ML models inside mobile apps.

Predicting Career Paths

Advanced ML platforms can gather information from a student’s college application, their essays, their standardized tests, and recommendations from teachers in order to determine what they are likely to excel in.

Simultaneously, the technology can predict trouble areas for students and offer them additional assistance on a particular topic in the form of tutoring or writing workshops to help them achieve their professional goals.

ML will ultimately help students maximize their potential in their areas of strength and interest, while also patching up their weaknesses in order to transform them into more well-rounded professionals in the future. The technology can also look at a student’s grades and extracurriculars in order to identify potential career paths for them moving forward.

Less Bias in Grading

Machines will soon be able to assist teachers in examining student assignments and detecting whether or not there is any plagiarism or other infractions. These robots will be able to offer a potential grade for students, as well as areas in which they could improve a particular assignment in order to help them achieve an optimal grade.

Because we’re all humans, bias is an inevitable part of the grading process. ML has great potential in ensuring that grades will not be affected by the attitude an educator has towards a particular student. These machines will essentially offer a grade based solely on their performances. Nevertheless, a professor’s wisdom will still be necessary to assess whether or not a student fulfilled a prompt successfully or other factors such as in-class participation and behavior.

Setting Up Appointments

Scheduling appointments between students and teachers can be a difficult process, but ML has the potential to remedy the logistical issues involved. By automating the process of scheduling meetings, machines can create an organized schedule for both students and teachers.

This kind of software would require students to click on a particular date and time for an appointment with a teacher, and smart ML algorithms will do the rest. This way, students can have personalized schedules based on their commitments, their needs, and their pace of learning. Such technology will ultimately reduce the pressure on students and creating a more comfortable learning experience for all parties.




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