Big learning data that matters: Bringing the excluded back in

10 August 2013

I recently read an excellent post from Knewton on the five different types of Big Data in Education that matter. Very briefly, the five types are classified as identity data, user interaction data, inferred content data, system-wide data, inferred student data.

While I certainly agree that these types do matter, one of the possible interpretations might be that other types might not matter. Although I am sure that this was not the intention of that post, mine is to come rescue the guys who were left out. The list is huge, and my goal here is not to cover them all. So, here is a first shot:

Before learning happens
- What does the market wants people to know?
- What do people themselves want to learn?
Within the learning process
- How immersed are the students by the learning experience?
- How much do students like the learning experience?
- Did it the learning experience allow them to keep learning after the course?
- Were the students able to put the learning content to practice beyond simply absorbing information?
After learning
- How useful was the learning? Did students get a job? Did they get a promotion? Did it lead the student to do social good?

I guess my point is that big learning data is certainly about the act of absorbing information, but what comes before and after as well as all the other factors that come along also do matter.

by Ricardo Pietrobon

About Me

My name is Ricardo Pietrobon and I am interested in big data and situated cognition applied to immersive distance education.