“What happens to serendipity in a system where all educational choices are based on recommender systems” (Enyon, 2013, 238)

I think this really echoes the points you were making about Netflix above Jeremy, and, as much as I love structured studying, I think stumbling upon ideas and concepts through education is also really important.

Rebecca Eynon (2013) The rise of Big Data: what does it mean for education,
technology, and media research?, Learning, Media and Technology, 38:3, 237-240, DOI:
10.1080/17439884.2013.771783

‘My choices, right from the beginning, were limited to recent viewing folly’

Yes, really interesting, particularly as Netflix seem to claim a somewhat more sophisticated algorithm, and also that they highlight the differences between what people say they watch, and what they actually watch (http://www.wired.com/2013/08/qq_netflix-algorithm/). Where is the scope to be surprised by something new in your recommended films? This is the crux of the you loop issue.

‘Harmless here, but what if it was a political view being reinforced? What if it was a perspective on a current affair or report on a certain issue I may not have heard about.’

Great point here. While one might argue that Hollywood films are relatively simple to categorise into genre, there is a problem here between how the algorithm might classify something, and how any one individual might interpret that area, isn’t there? So, staying with the film example,can we assume that people actually like films according to genre in all cases? Don’t people have more nuanced interpretations of films, and what they might like about them? For example, I think The Shining (http://en.wikipedia.org/wiki/The_Shining_%28film%29) is probably one of my favourite films, but I wouldn’t say I’m a fan of a ‘horror’ genre. There are other things I like about the film that I’m not sure I can even articulate fully what those things are and *why* I like it – so can an algorithm really do that for me?

That relates to your point here, because there are perhaps more serious implications around the kind of knowledge we have access to through algorithms (Google Search being a prime example). So, my argument would be that when we try to model the ‘likes’ and ‘dislikes’ of people, it is not only done in a hugely simplistic way, but the results also feed back formative ideas about our selves that are quite powerful (Oh, so I must be a horror fan!). Rather than think about what it is we like about films, the algorithm gives us an answer. When one thinks about this in terms of politics, as you say, the implications may be pretty significant.

You also raise a really good point about the automated ‘personalisation’ of education and the loss of diversity. The assumed transparency and authenticity of the algorithm is central here again I think: if an algorithm tells you that you are a ‘visual learner’ and that you prefer mathematics-related subjects, that is a pretty powerful message to somebody who might be feeling pressured to find direction and focus in their choice of educational pathway?