There’s a serious danger, I think, in Learning Analytics, of producing and processing ever more data in ever more exciting and attractive ways, but which aren’t connected up to making things any different for learners.
As Marx famously put it in his Theses on Feuerbach,
Die Philosophen haben die Welt nur verschieden interpretiert; es kommt aber darauf an, sie zu verändern.
(Philosophers have only sought to interpret the world in various ways; the point is to change it.)
This is nowhere (!) more true than in learning analytics. Analysis without action is wasted effort – which we can ill afford now. Those of us with a penchant for data, sums and code (I know, I know, but we have our uses) can get terribly excited about new sources of data and new analyses. It’s great to come up with new ways of understanding and interpreting what’s going on. But if that effort doesn’t translate in to better action – it’s pointless.
On the other hand, action without analysis may be making things worse – you just don’t know. (A point sometimes lost on misinterpreters of Marx who focus only on the ‘change it’ bit. You can say many rude things about Marx – and I sometimes do – but not that he was short on quantity of analysis of capitalism.) We’re certainly going to be seeing a lot of dramatic change in universities in the coming years. If we don’t have the right evidence supporting decision-makers in that process, not only will the decisions be made in the dark, we won’t even know how wrong they are.
That’s why there needs to be a learning analytics cycle:
The process from learners to data to metrics/analytics is the core of what learning analytics is. But those outputs need to feedback to learners if the process is to be of any real value. (As Tony Hirst mentioned in his Learning Analytics Pre-Workshop, in technical terms, we’re talking a closed-loop control system.)
For me, this means engaging with all the frustrations of internal systems, processes and decision-making. If I was just interested in research outputs, I could just dive in to the systems I can get access to, grab some data, do some analysis, and go. It’s much harder to arrange and endure the endless meetings, papers, persuasion, and frustration that is what happens when you try to make a substantial change to a large bureaucratic organisation like a university. But that’s where the real value and potential lie.
Learning analytics starts with the learner, and their ‘data exhaust’. We need to make sure it gets back there again.
NB For clarity (thanks anonymous tipster!) that last sentence is meant entirely figuratively and I am emphatically not advocating gassing students. That would be Evil and Wrong. I’m trying to make things better for them.
(This post brought to you by my desire to bolster my resolve and enthusiasm for helping to work up an internal project along these lines. Will say more if and when there’s things to say.)
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