The twinning of EDM and Learning Analytics
After listening to Ryan Baker’s presentation on Educational Data Mining (EDM), I am more convinced than ever that EDM and Learning Analytics are actually the same side of the same coin. Despite attempts being made to explain them into different zones of influence or different missions, I fail to see such differences, and from reading other LAK12 participants’ reflections, I am not alone in this. Baker’s view that Learning Analytics are somewhat more “holistic” can be refuted with a simple “depends”. What is more, historically, EDM and LA don’t even originate from different scientific communities, such as is the case with metadata communities versus librarians, or with electric versus magnetic force physics – now of course known as electromagnetism.
Both approaches (if there are indeed two) are based on examining datasets to find ‘invisible’ patterns that can be translated into information useful to improve the success and efficiency of the learning processes. A good example Baker mentioned was the detection of students that digress, misunderstand, game the system, or disengage. It’s all in the data.
I would also like to believe that predicting the future leads to changing the future, at least it could give users the air of being in control of their destination. As a promotional message this has quite some power. But even in support of reflection the same can be postulated: knowing past performance can help your future performance! So, once again a strong overlap between predictive and reflective application of data analytics.
For me, all of this can only lead one way: instead of using efforts and energies to differentiate the two domains, which would only lead to reduced communities both ends, and friction in between, we need to think big and marry them into one large community and domain: Let’s twin EDM and LA!