Visiting the Tweetorial archive

‘Archive’ is a strange term here. First, in light of Knox’s ‘Abstracting Learning Analytics’, perhaps this is a more a gallery, or even an installation. Second, archive implies some sort of fixed state, but this data is a moving, shifting event. Below are some screen shots taken after the Tweetorial (on the morning of 21 March, so a few days after the Tweetorial), with some commentary.

This first one, ‘Top Words’, strikes me as impressionistic at best. Many terms of either obvious, or non-illuminating.

Also, the data as progressed from when I first looked at it after the Tweetorial. Previously, ‘mean’ has been one of the top words (it’s now dropped off the visualisation). But what does ‘mean’ mean? Is it to be understood in an ethical, a statistical, or a semantic sense? Isolated words don’t communicate much meaning. I’d look for more syntactical analysis for that.

Top URLs (below) isn’t that illuminating. Relatively low counts (the ‘top’ one occurred four times) means the data is hardly analytically illuminating.

Volume over time: you can certainly see the Tweetorial as a standout event. I’d imagined there would be more resolution than this. I raised that in the Google hangout, and some people pointed me to other data analytics and visualisations – more of that later.

User mentions – James and Jeremy being most mentioned was unsurprising. Does this kind of analytic get beyond being a ranking-led analysis, though? On the Google Hangout, I was struck by Nigel highlighting the ranking discourse that runs through this analysis, suggesting it reflected commercial motivations for these analytics. I wonder what genuinely learning-led learning analytics would look like, though.

Some basic aggregate data (below). Perhaps useful for a report, but not in itself illuminating at any scale other than the most aggregate. And, again, a rolling ‘archive’. The politics of representation include timing, here.

Hashtags. I rarely use them. Suddenly I’m seeing why I might. If, that is, I want to be schooled by the analytics, and perform to their drum beat. That’s a decision to be made more dynamically than in a blog post, but the learning analytics are not neutral, and have a reflexive dimension to them. They perform, and they invite particular performances.

Influencer index: hum, an issue of quality compared to quantity here (and elsewhere, too)? Something quite naive about this notion of ‘influence’, but also very attractive, perhaps, for some looking for a register of it.

Now we switch to some analytics. I can’t remember who mentioned this during the Google Hangout session, but thank you for the lead.

First, some ‘statistics’. But they are not alone: note the left-hand side commentary. The rhetoric is objective, and directive, and pitched at the user by name (this is an analytic of my individual Twitter output). I feel judged; also, I think I’m gently but firmly directed. I should be following more people (and thus making more analytics, and becoming more like the analytic’s perfect Twitter user?). I’m unsure why it’s bad to be followed ‘out of good will’, but that seems to be the inference!

These are both interesting and bland. Again, I’m doubtful that individual words carry contextualised meaning but, hey, they’re good enough grist for an analytic…

Here’s a gallery of likely people… Hello, Twitter world:

Aha, ‘a deeper look inside’. The ocular promise that Knox spoke about. ‘And here’s what we found interesting’. Really? This really is non-representational art. And then ‘tips’ (a softer, but equally directive edge, cf. the ‘statistics’ screen-shot, above): ‘Mentions are good but replies means they really talk to people.’ Again, the analytics are both constructing performance, and also not travelling alone – they come with their interpretive entourage.

Pretty poor attitude. Two Tweets show it! Emoticons: surely these, above anything else, are context dependent. Or perhaps not. Is this just ‘happy’ and ‘sad’, or does the whole range of emoticons get picked up? Now that would be (slightly) more interesting.

‘Time’. Glad to know I do sleep! And have lunch. And eat tea. It all looks very human. Reassuring! Also, quite revealing.

Not sure what ‘engagement’ and ‘klout’ indicate. Also, most influential or me, or on them? I’d need to look further on this analytic (this is from Keyhole, another analytic mentioned during the Google Hangout – thank you, Nigel):

I’d come into the Tweetorial not having any idea what the learning analytics would pick up on. From these three different LAs, I’m impressed by what is picked up (and I imagine this is the tip of the digital iceberg), underwhelmed by some of the analysis, fascinated by the reflexive impact on performative use of technology, and sobered by the likely uses this could be – and is being – put to.


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