e-Learning Ecologies MOOC’s Updates

"Manual Instructions for each Student" by Artificial Intelligence - A new form of Ubiquitous learning

I would like to create a new ubiquitous learning method called "The Individual's Manual Based Learning" which is a separate manual created by artificial intelligence through data analytics of the student's learning pattern and behavior.


A very simple example of ubiquitous learning is when you're training the employees of the company. For example, let us suppose that I have created a system which is some new technology for the betterment of the employees, now there are two ways to train the employees. One is to take a session for them and train them their individual functions but that cannot be possible here because of the time and space confinements

How?

1. Every employee cannot be available at the same time because of their work.
2. Every employee has something different to learn
3. Every employee has different learning speeds

So addressing them all in the same classroom, to teach them their roles doesn't seem a good option as far as learning of their role is concerned. But there is something else that we can do, we can first make a training manual for each of the different employees and then distribute the manual to them.

Now individually each of the employees can read and try to implement the manual and in case of doubts, we can provide them with the assessment. Just like different employees each having different roles, talents, skills, and specialties, even we can consider the kids and students to be the same way.

I mean what exactly is a student? Do you define a student by his age? I would say that anyone who is learning is a student, no matter what age they are of. So if I have to teach these students with individual differences, I would definitely choose "The individual study manual" that is designed for the students (employees in my example) made with the help of data analytics, machine learning, deep learning and artificial intelligence.

 

  • Jenn Meacham
  • Naman Munot
  • Jenn Meacham