Role of Machine Learning in Education With Examples


Role & Examples of Machine Learning in Education

Machine learning is the science of getting computers to act without being explicitly programmed.

Educators today are open to try new technology in education. As witnessed, integration of technology makes learning and teaching efficient on both ends. Thus, there is rising likability of machine learning in the education. A part of Artificial Intelligence, machine learning is on rising scale of adoption. Other than education, we are so much around this technology that we may not even know. The recommendations from e-commerce that are so much similar to our preferences is all Artificial Intelligence that is working. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. It is all about innovation and improving the life of the millennial and beyond. If you have heard about automated card driving, you already know how machine learning can improve the life of the people.

In education, it does have its own impacts. In this post find out how machine learning is improving the education. Pointers mentioned below indicate the use of machine learning in education along with platforms that are using this technology.

Content Analytics:

Content analytics refer to machine learning platform that optimize content modules. Some of the brightest examples in this sub-niche are IBM Watson Content analytics and Gooru.

Learning Analytics:

Learning analytics is focused on tracking student knowledge and enhance their learning environment.

Some of the notable platforms in adaptive learning systems are ALEKS, Dreambox, Reasoning Mind, and Knewton.

Game Based Learning: ST Math, MangaHigh

Dynamic Scheduling platforms:

Learning can be a daunting task. And, that's why some platform follows the learning patterns of the students and asks teachers to step in when needed. New Classroom is a prime example which utilizes this approach and can easily schedule personalized math learning for the masses.

Grading Systems:

Current grading system relies on humans, but with machine learning students learning and knowledge can be scored. The computer assignments are score via peer grading or automated. WriteToLearn and Turnitin are two prime examples that utilize the simple machine learning algorithm to grade essays and detect plagiarism.

Process Intelligence:

Process intelligence is mainly focused on analyzing a large amount of data. It is all about finding the structured and unstructured data, identifying new opportunities and visualizing work-flow.

Three platforms really flourish in this section are Bright-bytes Clarity, IBM SPSS, Odoo, and Jenzabar.

IBM SPSS, Odoo, Jenzabar are ERP system that has better boost retention and automate and enhance another important business process.

Synchronizing teachers and Schools:

Not every school has the similar requirement. TeacherMatch and MyEdMatch handle school/teacher matching.

Predictive Analysis:

Predictive Analysis helps data mining jobs. It can be used to improve retention, learning, and application. Last, but not the least, machine learning is also making it past the back office software. Good examples would be EDULOG, PAR by Hobsons, etc.

Other Resources You Must Check:

Machine Learning And The Future Of Education

AI and the Classroom: Machine Learning in Education

What is machine learning and why is it important for education?

Share your views on the role of machine learning in education.

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About the Author
Author: Priyanka Gupta
Priyanka is a blogger by profession and has an increasing interest to write about the edtech space. While writing she keeps in mind the educators to come up with right resources and ideas which might be relevant for them in relation to effective use of technology in their profession and institutions/classrooms.
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