Big data and analytics

Learning analytics is not an entirely new concept as the tools, terminology, and methods are largely borrowed from related fields (business intelligence in particular). The current explosion of interest in analytics, (big) data, and cloud computing is moving the conversation forward at a stunning pace. It’s difficult to get away from analytics and discussions of data. Unfortunately, it’s even more difficult to get a sense of the scope and domain of analytics.

Learning analytics are starting to develop their own identity. We no longer need to rely only on “look at what Amazon does” or “think of it like Shazam for learning” to describe analytics. Purdue’s Signals, UBMC’s “check my activity“, and WCET’s PAR are first generation examples of the education field presenting developing its own analytics tools and concepts.

Still, there are many resources outside of education that can provide insight, models, and classification schemes to consider in analytics discussion and adoption. I came across this image from Dion Hinchcliffe:

This is a reasonably simple model, providing an overview of tools/technologies, analytics vendors, so called “deep insight” as well as business objectives. With a bit of tweaking, I think it can provide a useful application for learning analytics as well…

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