@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…

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@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.

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Back to the future, EDC style: a review of a forthcoming book on transhumanism https://t.co/duv7KtpVBb 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.

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In algos we trust. YouTube/Facebook’s ultimate solution: “cross your fingers and hope that AI will solve it” https://t.co/cxHJwqLUst #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.

 

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From learning analytics to writing analytics: tracking writing a novel (or a dissertation, perhaps?) https://t.co/hG61XskPAs #mscedc

Learning analytics can also be deployed in other areas of education, such as writing. In a week when Stuart Elden has blogged about his priority for writing academic books, not articles, here’s a piece from the Guardian employing some simple analytics to the book-writing enterprise:

More complex analytics are imaginable. How they might change the writing process is a complex of issues akin to the recent Tweetorial discussions.

 

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Arcadia, a post-genre app-novel: “three publishers, two designers, four sets of coders and a lot of anguish” https://t.co/w53kfYT4Cd #mscedc

A digital extension from the ‘choose-your-own-adventure’ books from last century, Ian Pears wants to by-pass “the limitations of the classic linear structure” while “making the technology the servant of the story rather than its master”.

Does he achieve it? I don’t know. Novel reading will have to wait until after this course. What interests me here is the explicit engagement with what we might call the poetics of analytics (my term, although other might have used it).

For instance, regarding his use of visualisation to track evolving plot, Pears comments: “On every occasion, the more satisfactory the appearance, the better the story read, and I still haven’t quite figured out how that works.” This emergent quality recurs in the recursive evolution of story and app, and in Pears’ claim to be loosening, even liberating, from “from those shackles known as genres”. It’s quite likely he over-states at this juncture, but here is one instance of wider poetic strands within digital storytelling.

One paragraph of this article struck me as particularly suggestive in this regard:

Ebooks are now quite venerable in computing terms, but it is striking how small an impact they have had on narrative structure; for the most part, they are still just ordinary books in a cheap format. An analogy is the early days of cinema, when film-makers did little more than plonk cameras in front of a stage and film a play. It took some time before they realised that by exploiting the new possibilities the technology offered – cutting, editing, closeups, lighting and so on – they could create a new art form that did not replace theatre, but did things theatre could not. Computing power properly understood and used can perhaps eventually do something of the same; not supplant orthodox books – which are perfectly good in most cases – but come into play when they are insufficient.”

At the same time, the hyperlink in the paragraph regarding ebooks and consistent physical-book sales suggests no easy replacement, but complex multiple forms – a healthy context for poetic innovations, with analytics and with more than simply analytics.

 

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What does your media feed you regarding algorithms? Ben Williamson casts some light: https://t.co/UhpAtlMXN4 Diverse everyday access #mscedc

I found this piece via the ‘recent posts’ on the ‘Code Acts in Education’ blog site hosting the Jeremy Knox piece on Abstracting Learning Analytics, set for this week’s readings.

I think it’s one of the most useful meta-interpretative pieces I’ve come across for understanding my own Lifestream. It doesn’t take long, on my Lifestream, to see I read the Guardian. What Williamson does here is helps position that perspective / bias / source, and helps me see my own eyeball a little more, so to speak. While not strictly or only a digital algorithm, it is my own entanglement, and worth seeing as well as it can be seen, for what it is.

Williamson uses Google research results to pitch the editorial  line taken by several UK newspapers in their reporting (or not) of algorithms. Thus:

  • The Guardian: ‘the algorithm as governor’.
  • The Telegraph: ‘the algorithm as a useful scientist’.
  • The Sun: “largely disinterested in algorithms in terms of newsworthiness”.
  • The Mirror: “treat[s] algorithms in terms of brainy expertise.”
  • The Daily Mail: ‘algorithms as problem-solvers’.

For my Lifestream, and from it, I’m unsurprised that the Guardian “appears to take the most critical editorial line”, and is the most explicit in emphasising “the connections between algorithms and politics”. This is my feed, most certainly.

But critical reflection about the spread of options is fascinating and vital, especially if, as Williamson projects, “new digital media literacy approaches to news consumption and information access are going to be crucial in coming years.” We talk a lot about entanglements on this course; Williamson points to the uneven and variegated nature of entanglements, even within old-school, fairly monolithic editorially-curated discourses. Within and beyond that:

“if we genuinely are concerned that algorithms are involved in political life by filtering and curating how we access information, then it’s perhaps concerning that these issues are much less well covered in papers from alternative political perspectives. Even what we know about filter bubbles and algorithmic curation is itself filtered and curated.”

Education is drawn into this morass, in that people learn their everyday knowledge from such divergent and relatively narrow perspectives. As Williamson concludes: “Developing forms of digital media literacy that attend to the role and power of algorithms in political and cultural life now appears to be real priority that will require dedicated attention in 2017.” Cue EDC…

 

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Lifestream summary, week nine

This week’s Lifestream feels like a multi-stepped journey, without a panoptic view of beginning and end. In part, this reflects the Tweetorial’s velocity and scale, in part it reflects the topics in view.

https://upload.wikimedia.org/wikipedia/commons/9/98/Algorithms-NetFlow1.png

To pick out some themes:

(a) Education and learning analytics have to both inform each other: there is a two-way relationship between them – neither is a ‘thing in itself’;

(b) Learning analytics do not escape the influence of context. Thus, learning analytics need to be understood relationally and in a context-critical way – this requires both localised particularity and also an eye towards globalisation;

(c) statistics don’t bleed, but learners still do – there are ethical entanglements with learning analytics which are inescapable;

(d) learning analytics are emblematic of wider algorithmic cultures, and need to be – and will be – understood within that wider cultural matrix;

(e) instant answers such as ‘learning to code’ are perhaps more instant than answers – and will generate new issues, new questions. At very least, coding needs to go hand in hand with social theorising, even if the money follows the former not the latter. Particularly if the money follows the former, not the latter.

(f) as the final post for the week suggests, I’m surprised to think that learning analytics might even humanise learning, in some situations – but then i think this unexpected ‘turn’ reflects the breadth of possible contexts and entanglements. A particular assemblage can surprise, and be unexpected.

(g) this block of the course is by far the least stable in my thinking and experience – befitting its emergent qualities, but also its ‘big’ scale.

https://upload.wikimedia.org/wikipedia/commons/9/98/Algorithms-NetFlow1.png