From big data to mundane data; from learning analytics to everyday life: many routine shapings of motivation #mscedc

I like Sarah Pink’s focus on the everyday, and this article is no exception. It’s a thought-provoking counterpoint, and accompaniment, to discussions of Big Data: Continue reading “From big data to mundane data; from learning analytics to everyday life: many routine shapings of motivation #mscedc”

Lifestream summary, week ten

This week, what do you see? I’m using some optical illusions to illustrate the Lifestream journey.

It’s not a straighforward journey, but the illusions are in honour of Jeremy Knox’s blog post on ‘Abstracting Learning Analytics’, which has infused a number of Lifestream posts and has informed a lot of my thinking behind them.

Learning analytics can be illusion-inducing…

… and not always clear. Is there a shape there, or not? That was a question within and behind the analysis of the Tweetorial.

Do things inter-relate, or not? And, if so, how – and am I sure?

What am I seeing, exactly, and is that correct – and the only way to see it? Are others seeing something I’m not – and what happens when politics and power (as well as the market) are embroiled in these interpretive steps? [Non-representational art is still bound into contexts of production and consumption.]


Or, alternatively, seeing something when there is nothing there. Or, put another way, seeing something in the nothing, but the ‘nothing’ is important – and is made into a ‘something’? [Even Guardian readers – particularly Guardian readers – have their own filter bubbles, even filter baubles.]

But, in the digital sphere, is it ever creation ‘out of nothing’?

Learning analytics becomes creations (plural) out of something. [And we might not know what we’re creating – cross your fingers, and hope for the best?]

But throw enough data and metadata into the mix, and will anything become something, or something become anything? [Is the art gallery of learning analytics actually a hall of distoring mirrors?]

Learning analytics: perhaps it all depends on what contextual surface you lay it all down…

… and the comparisons you look to draw…

…or draw to look…

…and the scale, scape and perspective from which you stand.

Perhaps, into the twenty-first century, we should expect a bumpy ride ahead?

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. Continue reading “Visiting the Tweetorial archive”

Whither analytics, learning or otherwise, if society faces huge restructuring how will ordinary peop. react? #mscedc

I’m reading this piece through an educator’s eyes. This report from PwC appears to suggest that the education sector survives relatively well within the tsunami of digital unemployment to come. Perhaps a sense of relief, perhaps not. But also the piece continues to emphasise the importance of education in helping people to cope, suggesting “an argument for government intervention in education, lifelong learning and job matching to ensure the potential gains from automation were not concentrated in too few hands.” Hum: I wonder it’s that’s a fair and sustainable expectation to put on education. Even allowing for possible media hyping of the report, the projection seems bigger than simply an education quick-fix. Something more societal is in possible view here. If a new industrial revolution is underway, then education will be part of the mix, not the totality of it.

I’m also reading this piece with Jeremy Knox’s ‘Abstracting Learning Analytics’ in mind, and his analogies to art. Visit any historical art gallery, and one will see works of art which are, to varying extents, separated from their circumstances of production. Likewise architecture (and digital cultures, analytics included, do and will create their own architectures): aesthetics take on an after-life of their own, when beautiful artefacts produced under less-than-beautiful conditions are preserved and curated for a later, often selective, audience.

Will analytics become – or some become – thus separated from their production? How will people, undergoing perhaps radical restructuring of their jobs and lives, react? It might be some time before such things settle down and become national treasures, preserved for posterity, and even tourist attractions. There will be much action and reaction ahead of then.


@HerrSchwindenh_ @nigelchpainting Also @j_k_knox ‘Abstracting Learning Analytics’ in a nutshell. #mscedc

“I could be bounded in a nutshell, and count myself a king of infinite space, were it not that I have bad dreams.” – Hamlet, II.ii What does this mean? Not just the obvious, literal explanation,…




@HerrSchwindenh @nigelchpainting Musing: presumably no meaning without interpretation; possibility of interpretation with’t meaning? #mscedc

I don’t think we got very far with this exchange. I’m including it as a sample of the loose ends which Twitter allows, and which the learning analytics patiently gobble up, process and expound. In itself, that’s telling.

Dirk, should you travel this way, I’m wondering how this fits with your early Lifestream posts about singular and revealed identity, and living publicly on the web. Many meanings/truths sounds like it might cut against it – or at least problematise it.



@james858499 Many thanks – and great to find it on MyEd. Looking forward to checking it out, perhaps ahead of a final assignment. #mscedc

The Fenwick, Edwards and Sawchuk volume looks very interesting. I’m looking forward to getting my head a little way round complexity theory, and also relishing the latter chapters on space and geography.

It’s a great spin-off from the Google Hangout, and the good conversation and exchange of ideas and experiences. Many thanks…


@nigelchpainting Thank you for this. Very helpful to see a different ‘take’ on learning analytics. Another ‘abstraction’ (cf. Knox) #mscedc

I’d imagined ‘learning analytics’ would be a bit dull. I’m lying if I was to claim it’s my most interesting element of the course, but it’s much more interesting than I imagined. I’m enjoying the poetics and the aesthetics of the visualisations, and also the surprising elements that get picked up, highlighted, even commented upon.

In the last day or two I’ve also read Jeremy Knox’s blog piece on ‘Abstracting Learning Analytics’. It’s given me fresh eyes for the area, and I’m actually looking forward to looking further and commenting on the Tweetorial archive.

It all feels like discovering a box of photographs of you, taken by someone who is sometimes insightful and composed, sometimes a little careless with the composition, sometimes just, well, a bit random. Each is strangely illuminating of something. It’s just a matter of what, exactly.


Back to the future, EDC style: a review of a forthcoming book on transhumanism The cyborgs are coming (back) #mscedc

A colleague pointed this book out to me. It’s not out yet, but I’m looking to buy it. Along with Adam Alter, one to read after the course, perhaps. But with different eyes and ideas, in light of having been on the course. I’m really appreciating the new connections I’m drawing and being drawn into here.


In algos we trust. YouTube/Facebook’s ultimate solution: “cross your fingers and hope that AI will solve it” #mscedc

The above quote comes from this article:

To quote further:

“The problem is one of scale. YouTube didn’t grow to the size it is by manually checking every video, and it’s not about to start it now. For one thing, it would be hugely expensive: 300 hours of video are uploaded every minute. Even assuming staff members did nothing but watch videos for eight hours a day, it would take more than 50,000 full-time staff to manually moderate it.

So the company relies on tricks which do scale: algorithmically classifying videos, by scanning the titles and video content itself; relying on users to flag problematic uploads; and, in large part, by trusting creators themselves to correctly label their work. That trust is backed up by force, though, with YouTube reserving the right to pull channels entirely from the site if creators consistently miscategorise their work.

But those tricks are showing their limitations, now. It’s taken a while, but Google has waded into the same battlefield that Facebook’s been losing on for years. At a certain size, it’s impossible to run a censorship regime that won’t produce a steady stream of errors indefinitely.”

Here, in one piece, are many of the hopes and fears for algorithmic regulation – and regulation of algorithms.