Are we just all data end points?

I’ve had  two very contrasting data experiences this week which are both clarify and confusing my views on data and learning analytics.  Firstly there was the LACE (learning analytics community exchange) project webinar titled: Big Picture of Learning Analytics Interoperability. Brian Kelly has written up the event and his blog post contains a link to the recording.

If you think about it, interoperability is key to any kind of data and analytical work. However as the webinar explained, learning analytics has the added complication of the numerous levels and models it can work in and across. The project are very keen to engage stakeholders around concepts but I think they are suffering from the classic chicken and egg scenario just now. They want to engage with the community, but some of the abstract terms do make it difficult for the community (and I include myself here) to engage with, so they need real examples. However I’m not sure right now how I can engage with these large concepts. But in my next post where I’ll update on the work we;re doing here at GCU it might become clearer. I am very keen to be part/track this community so I guess I need to try harder to engage with the higher level concepts.

Anyway, as you’ll know, dear reader, I have been experimenting with visual note taking so used the webinar yesterday to do just that. It’s an interesting experience as it does make you listen in a different way. Asking questions is also kind of hard when you are trying to capture the wider conversation. This is my view naive of the webinar.

In contrast, the University of Edinburgh’s “Digital Scholarship Day of Ideas : Data” had a line up of speakers looking at data in quite a different way.  Luckily for me, and others, the event was live streamed and the recording will be available over the next few days on the website.  Also Nicola Osborne was in attendance and live blogging – well worth a read whilst waiting for the videos to be uploaded.

A common theme for most of the speakers was exploration of the assumption that data is neutral.  Being a digital humanities conference that’s hardly surprising, but there were key message coming through that I wish every wannabe and self proclaimed “big data guru”, could be exposed to and take head of. Data isn’t neutral, and just because you put “big” it front of it doesn’t change that.  It is always filtered and not always in a good way. I loved how Annette Markham described how advertisers can use data to flatten and equalise human experience, and her point that not all human experiences can be reduced to data end points however much advertisers selling an increasingly homogenised, consumerist view of the world want it to be.

This resonated in particular with me as I continue to develop my thoughts around learning analytics. I don’t want to (or believe that you can) reduce learning to data end points that have a set of algorithms which can “fix” thing i.e. learner behaviour. But at the same time I do believe that we can make more use of the data we do collect to help us understand what is going on, what works, what doesn’t and allow us to ask more questions around our learning environments. And by that I mean a  holistic view of learning environment that the individual develops themselves as much as the physical and digital environments they find themselves in.  I don’t want a homogenised education system, but at the same time I want to believe that using data more effectively could allow our heterogeneity to flourish.  Or am I just kidding myself? I think I need to have a nice cup of tea and think about this more. In the meantime I’d love to hear any views you may have.