#mri13,  learning analytics,  MOOC,  Research

The iceland of Dallas



<Dallas deathstar in the snow – this may, or may not, be a metaphor>

I was at the MOOC research initiative conference in Dallas, Texas last week. As Jim and others have reported, we got caught in icemageddon, but that's a whole other (war) story. I'll be doing a few posts about the conference. It was a fantastic meeting, well done George Siemens, Amy Collier and Tanya Joosten for putting it together. I got to have some great conversations, and meet people I've know online for years. Which is by way of apology for my first post being a bit negative.

This one concerns one aspect of the conference that I am having difficulty articulating, so I'm going to try and work it through in this post. There was a data strand to the presentations, and I went to a few of these. There is some fascinating stuff being done, particularly when you have analytics on so many learners. But I also had a vague sense of unease about some of these.

They were often presented by super-smart, young computer-science researchers from privileged universities: Carnegie-Mellon, Stanford, Harvard. I don't have anything against super-smart, young or computer science people, or anyone from those universities (some of my best friends are young people :). But there was something a bit, well, cold, about it all (and not just because of the wind chill factor of -10). People were nodes, and they could be manipulated to move from peripheries to the center by tweaking certain elements. It was easy to forget you were talking about learners, and not sales of baked beans.

But I think we need this research, it's useful and can tell us a lot about what's going on. Candace Thrall was stuck at the airport so couldn't give her presentation, but a colleague gave it on her behalf. She mentioned that one of the transitions we were going through was from a theory-led one to an evidence based one. Prior to this Jim Groom was telling me about Mike Caulfield suggesting we were in post-theory now, where only the big-data mattered (this was from a book I think, if anyone knows which one, let me know). 

UPDATE: Mike has done a great post elaborating on the post-theory debate here. It explains what I was trying to get at in this post.

I felt I had a glimpse of that post-theory world, and I wasn't sure I liked it. We may have been too theory-heavy before, where the evidence was inconsequential, because hey, we have a nice theory. But the pendulum swing to lack of theory where we only care about the evidence seems to lose sight of the people in the system. So I guess my plea to the super-smart, young computer science researchers at ivy league institutions who are now getting into to education is – don't ignore the bearded old guy with a bunch of theories in his back pocket, we need those too. 

Like Dallas, education should be warm and welcoming, and the danger is that data fetishisation will make it like the post-apocalyptic Dallas I experienced.


  • Erikduval.wordpress.com

    http://www.wired.com/science/discoveries/magazine/16-07/pb_theory is a short introduction to the idea that theory is no longer all that relevant… Triggered quite a bit of controversy…
    Actually, I worry less that a focus on data is ‘cold’. I like data 😉
    I worry more that we focus on the things we have data for (time spent, who interacts with whom, clicks on links, etc.) and not on the things that are more tricky to measure, even if they are more relevant, especially in more open ended, deeper forms of learning…

  • Dougclow

    I’m with Erik – I like data too. 🙂 And I also share his concern that we are focusing on the data we have, rather than the data that might be most illuminative.
    It’s the classic drunkard’s lost-key search:
    “What are you doing?”
    “Looking for my keys.”
    “They don’t seem to be here under this streetlight. As you sure you lost them here?”
    “No, I lost them over there, in the dark.”
    “Then why look here?”
    “Because this is where the light is.”
    I do share your (Martin’s) concern with completely atheoretical approaches, though – typically they’re not actually atheoretical, they have a very robust theory, it’s just unstated and unexamined.
    We need both theory and data. What do you base a theory on, if not evidence? How “people centric” or “humane” your theory is in theory can be a very misleading predictor of what it’s like in practice. Think Stalinism or Maoism for extreme examples.

  • mweller

    Hi Erik and Doug, thanks for commenting, like I said I’m not really clear on my thoughts here, so I’m trying to work through the niggling doubts I had in all those presentations by those super-smart kids. Doug, you are quite right about humane theories being very inhuman – I think we lived in a theory-centric world where there wasn’t much data and you could find enough to support your argument. Erik – I hadn’t thought about that, maybe that is my concern. I like data too, I really like data when it confounds your theory and expectations (one prof showed that understanding of physics concepts got _worse_ midway through a campus course). But I just felt that MOOCs and big data were bringing people into education who maybe hadn’t been in that field before, which is a good thing, but they need to be in the same labs as the educators as well.

  • Sbskmi

    Hi Martin
    I know I’m forever in your debt for sharing your room with me on a cold, icy Texan night… but I’m going to disagree with you now. I’m quite surprised at your nervousness around the data heads! When you get jumpy about: “People were nodes, and they could be manipulated to move from peripheries to the center by tweaking certain elements. It was easy to forget you were talking about learners, and not sales of baked beans.” — well this is all very Orwellian — I can feel a new Apple 1984 Big Brother movie coming on. You know as well as I do that the fact that living human beings can be rendered as nodes in a network is a way to map the world, for some purpose. Maps always distort, but the thing is not to pay attention to the blob and links and ask “How can this be a reasonable proxy for a person?”, but to ask is this making visible something that was otherwise invisible, and who does this help?
    Where I’m sure we do agree is that if theory stays in academics’ back pockets to be gently stroked but never make a real impact, then that’s a poor show — and it’s here that I think analytics could change the game: now you are increasingly challenged to put your analytics money where your theoretical mouth is, and evidence that your theory is sound from the data.
    My thoughts sparked by the blog debate: http://people.kmi.open.ac.uk/sbs/2013/12/learning-analytics-theory-free-zone

  • mweller

    Glad you made it back to the UK Simon. You’re undoubtedly correct – as I said, I couldn’t quite put my finger on my unease. Maybe they were just younger, smarter and more privileged than me so I got all angsty. I think Mike’s post gets at it better than mine. I guess having seen data people ‘discover’ lots of stuff this year (see my The Year of No Shit Sherlock post), I get a bit nervous that new people wash in with no regard for existing knowledge. But that sounds overly defensive and protectionist. I welcome our new data overlords 🙂 No, I think it is exciting to have this kind of evidence finally in place, I just want to make sure we walk together hand in hand.

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