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Lifestream, Pocket, Imaginaries and materialities of education data science

Excerpt:

Ben Williamson

This is a talk I presented at the Nordic Educational Research Association conference at Aalborg University, Copenhagen, on 23 March 2017.

Education is currently being reimagined for the future. In 2016, the online educational technology  magazine Bright featured a series of artistic visions of the future of education. One of them, by the artist Tim Beckhardt, imagined a vast new ‘Ocunet’ system.

via Pocket http://ift.tt/2n8CT5W

I found this post after reading Knox’s (2014) post on interpreting analytics in the same blog space. What would we call that? Searching laterally? Something which was, at the time, really frustrating in DEGC was that we were always given links to Journal home pages rather than to the specific article we were reading. While I seem to recall this being connected to copyright and appropriate practice, it was frustrating because none of the links were set to open in a new window/tab by default, so unless one right clicked and opened a new window/tab, one then had to go back to the original page to find out which issue one was looking for.. but I’ve subsequently reflected (repeatedly!) on how it made me much more aware of the types of ‘publications’ and their respective content, and perhaps resultantly, I think my ‘lateral searching’ has increased. It’s not a new practice, of course, but an addictive one nonetheless, and it’s always good to find a ‘treasure trove’ of good reads.

I’m getting tangential, though – what caught my eye about this post, in particular, was the focus on ‘imaginaries’, and the ways in which such ‘imaginaries’, or fictions, play a role in the creation of future reality. Williamsons writes,

..what I’m trying to suggest here is that new ways of imagining education through big data appear to mean that such practices of algorithmic governance could emerge, with various actions of schools, teachers and students all subjected to data-based forms of surveillance acted upon via computer systems.

…

Importantly too, imaginaries don’t always remain imaginary. Sheila Jasanoff has described ‘sociotechnical imaginaries’ as models of the social and technical future that might be realized and materialized through technical invention.Imaginaries can originate in the visions of single individuals or small groups, she argues, but gather momentum through exercises of power to enter into the material conditions and practices of social life. So in this sense, sociotechnical imaginaries can be understood as catalysts for the material conditions in which we may live and learn.

The post has a lot more in it, focusing on how the imaginaries of ‘education data science’ combined with affective computing and cognitive computing are leading to a new kind of ‘agorithmic governance’ within education. Frightening stuff, to be frank.

What I’m really interested in is the role of these ‘imaginaries’ though: how do fictions, and, frequently, corporate fictions, work their influence? Which previous imaginaries, captured in science fiction, can we trace – along with their reception over time – to present day materialities?

And, why are ‘the people’ so passive? Why isn’t there shouting about imaginaries being presented as inevitable? Why isn’t their protest?  A rant: “Uh – you want to put a camera on my kid’s head, to tell me how she’s feeling? Have you thought about asking her? You want to produce data for parents? How about as a society ‘just’ recognising the value of non-working lives and giving people enough time to spend with their kids while they’re trying to pay rent or a mortgage?”

It would make an interesting study – perhaps too large for our EDC final assignment, but I’m wondering about it could be scaled back.

 

 

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Lifestream, Comment on @james858499 Footnotes still allow for single-author assignment. Ditto library catalogues. But, single-author rare in some dsciplns? #mscedc by msleeman

Renee, thanks for this – and for the alert to the very-well-hidden hyperlink. I wouldn’t have found it without your second comment!
The graphs risk masking something acknowledged in the accompanying text, namely that ” the annual number of single-author, non-review papers themselves, as tracked since 1981, has remained largely consistent in the course of the three decades”. The declining percentage share reflect the increase in multi-author pieces, not so much the decline in the single-authored pieces per se. Clearly a complex picture is in view.
Also, I’m curious that there is no category for ‘humanities’: presumably it’s incorporated within ‘social sciences’. I’d imagine, within that category, there are lots of sub-sectors, each with their own practices, circulations and markets. Different assemblages, reacting to and with digital cultures in differing ways. Great to have some data-led insight on it, and inviting of more. Many thanks!

from Comments for Matthew’s EDC blog http://ift.tt/2obZwrK
via IFTTT

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Lifestream, Tweets

Stephen Downes’ summary:

When I spoke at the London School of Economics a couple years ago, part of my talk was an extended criticism of the use of models in learning design and analysis. “The real issue isn’t algorithms, it’s models. Models are what you get when you feed data to an algorithm and ask it to make predictions. As (Cathy) O’Neil puts it, ‘Models are opinions embedded in mathematics.'” This article is an extended discussion of the problem stated much more cogently than my presentation. “It’s E Pluribus Unum reversed: models make many out of one, pigeonholing each of us as members of groups about whom generalizations — often punitive ones (such as variable pricing) can be made.

My additions (i.e. from  my reading of the article):

What are ‘weapons of math destruction’?

Statistical models that:

  1. are not opaque to their subjects
  2. are harmful to subjects’ interests
  3. grow exponentially to run at large scale

What’s wrong with these models that leads to them being so destructive?

1. lack of feedback and tuning

2. the training data is biased. For example,

The picture of a future successful Ivy League student or loan repayer is painted using data-points from the admittedly biased history of the institutions

3. “the bias gets the credibility of seeming objectivity”

Why does it matter?

It’s a grim picture of the future: WMD makers and SEO experts locked in an endless arms-race to tweak their models to game one another, and all the rest of us being subjected to automated caprice or paying ransom to escape it (for now). In that future, we’re all the product, not the customer (much less the citizen).

Inside this picture, the cost of ‘cleaning up’ the negative externalities that result from sloppy statistical models is more expensive than the savings that companies make through maintaining the models. Yet, we pay for the cleaning up (individually, collectively), while those pushing the weak statistical models save.

The other loss is, of course, the potential: algorithms could, with good statistical modelling, serve societal needs, and those in need within society.

The line of argument is hard to argue with – but one does have to ask, is ‘sloppy’ the right term? Is it just sloppiness? At what point does such ‘sloppiness’ become culpable? Or, malicious disregard?