A new coined term “data exhaust” has become popular. This implies that the data that is collected without a specific need or a specific routine, is also useful even though it is being “exhausted” like waste gases. Take an example of massive online courses: Coursera.
Massive online courses (MOOC) are the latest trends in learning. A notable school offers free online courses for anyone in the world. This is a great opportunity for learning for free for most individuals but what is the incentive for the university?
They gain by using the “data exhaust”.
If you are a university, you cannot experiment with different teaching techniques and try different techniques without spending a lot of money and effort. But, on the other hand, you can experiment with different styles online with people from different backgrounds and see how the course material works.
Andrew Ng, while conducting a course for Coursera made an observation that many people made the same mistake in an online course. He rightly concluded that people were making a common mistake of inverting the equation and could fix the course so that people did not make that mistake. This helped him with his courses at school.
It is relatively easy to also figure out which learning method works the best. But in addition, it enables the school to determine which material is difficult, what can be changed, what teaching styles are useful in different geographies. The interaction in forum’s by different nationalities taking the course also helps highlight teaching styles that work and would in the future help the universities spread their learning methods worldwide.