v6.2.3 - moving along, a point increase at a time

Analytics, and usage in Higher Education

It's week 4 of #cfhe12 so it must be time for Big Data and Analytics as the topic of discussion. It's interesting coming back to this topic of discussion because it was the topic of the first MOOC I took part in, LAK11, and it's a topic I've been thinking (or at least keeping on the back burner) since I was in business school. On of th things to keep in mind when talking about Analytics is that there are quite a few definitions out there, so, when talking about learning Analytics it is important to define what we aim to get out of our discussion about Analytics and how we wish to employ the potential insight that we get from this data.

There are two topics that have recently come up in my neck of the woods: knowing what sort of data one can get from the various campus systems, and knowing what it means (and accurately representing what the data tells us). First, it's important to know what sort of data you can get out of your systems, like the LMS. As I've written elsewhere, systems are designed with certain design parameters and certain underlying assumptions in mind. This, of course, affects pedagogy, but it also affects what sort of data the system keeps track of. If the system doesn't currently keep track of certain data you need, don't dwell on it. Put in a request to your system vendor and see what happens, don't say "we don't have this data? Well that stupid? Why not?" The "why not" does not Matt, what matters is how to move on from here. The other thing to keep in mind is not to make assumptions about what systems track and how they do it. This can get you, and your organization, in a pickle. You should ask your vendors what they track and what they don't so you can plan accordingly.

The second thing that needs really careful consideration of what the data actually means! Over the past 10 years I've worked in a variety of departments on campus and one thing seems clear: data collected is with poorly analyzed and understood; or departments are shedding the light they want to shed on their data they've collected in order to make their department the "hero" of this yeqr's annual report, or to get as many resources as they can for their department. This second part is a direct cause (I think) of th siloization and siloed nature of academia.

With more than 4 years of business intelligence and Analytics in my head I am not sure what to add. What do you all think? What would your elevator pitch be for learning Analytics?

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