I have become obsessed with MOOCs. That’s “massive online open courses.” Not because they are, in the words of Thomas Friedman, a “college education revolution.” (Though there is some truth to that as well in my perspective.) Rather, I think they make vivid many of the fault lines of how we think about and enact teaching and learning in higher education. And while the vast majority of attention has focused on the standard (and understandable) issues of postsecondary access, quality, and cost, MOOCs also reveal deeper assumptions around issues of socialization, stratification, and success in the academy.
I have written a couple of op-eds for general audiences about this in the last few months: “Disrupt This” at the Huffington Post; “I Am Not a Machine” at the New England Journal of Higher Education; and “MOOCs R Us” and “What MIT Should have Done” at eLearn Magazine. Here, I want to lay out a few of issues that don’t get mentioned as much in general audience discussions. I hope in the coming weeks and months to delve into these issues and look forward to any feedback and pushback.
For now, I want to lay out just one implication of MOOCs that draws from recent work in Security Studies, and specifically around the notion of “data doubles.” The idea comes from Haggerty and Ericson’s (2000) “The Surveillant Assemblage.” This assemblage, they argue, “operates by abstracting human bodies from their territorial settings and separating them into a series of discrete flows. These flows are then reassembled into distinct ‘data doubles’ which can be scrutinized and targeted for intervention.” The implication of all this, they suggest, is a “leveling of the hierarchy of surveillance, such that groups which were previously exempt from routine surveillance are now increasingly being monitored.”
Their work, as much of the work in the Security Studies field, draws from and extends many of the ideas of the panopticon from Foucault, with strands of Deleuze and Guattari, Giddens, and Haraway. Torin Monahan’s recent (2011) “Surveillance as Cultural Practice” really nicely extends this discussion by suggesting that surveillance be seen as “embedded within, brought about by, and generative of social practices in specific cultural contexts.” This means that there is no Big Brother out there, per se; no conspiracy theory; no police state that is all knowing and future-predicting. Which does not mean, of course, that it is benign.
The connection for me to MOOCs can be seen in this interview with the leaders of Knewton, which provides an “adaptive learning platform” that provides a “personalized online learning content” for each user. I have cut a long section, but it is fascinating:
You do a search for Google; Google gets about 10 data points. They get, by our standards, a very small amount of data compared to what we get per user per day. If they can produce that kind of personalization and that kind of business, based off the small amount of data they get, imagine what we can do in education.
Here's why education is different from search or social media. For one thing, the average student studies for more time than they spend on Google or Facebook. People spend way more time in Knewton than they spend on Google—they spend hours a day as opposed to minutes per day. So that's one big reason why we produce a few orders of magnitude more data per user than Google, just based on usage.
But then there's the more important reason even than that, which is that education is not like Web pages or social media. It's a different product. And it lends itself infinitely more to data-mining than does any other industry right now. The reason is that nobody has tagged all the world's Web pages for Google down to the sentence level, the way that we ask publishers to tag every sentence, every answer choice of every question. They say, Here's what this sentence is about, or this video clip. They're basically telling us every single thing about every single piece of their content. That's how we can slice and dice it so finely.
So what Knewton and many other computer-based learning platforms allow is the construction of a highly personalized learning profile through such data aggregation and analysis. The idea, of course, is that such a profile, such a “data double,” better supports the adaptation critical to quality real-time feedback. To be clear, such adaptive learning systems have been shown to be as effective for learning as human tutors and instruction.
Yet the implications of such data assemblages are far from clear. Above and beyond the instrumental aspects of better learning of certain content knowledge, there are troubling aspects of data privacy, of the normalization of competence and intelligence, of the asymmetries of visibility, of the embedded nature of self-surveillance. Similarly, such “big data” fosters an entwinement between our notions of education and the capacities of technology: those “data doubles” are the foundation from which we define, determine, compile, analyze and ultimately deploy the data of what counts as teaching, learning, and knowledge. These are socioculturally, strategically, and politically complex and fraught processes that become reified and stabilized in particular procedural and institutional structures. This raises a host of questions about what counts as teaching and who benefits from such structures and practices.
I truly believe that MOOCs are going to disrupt large segments of higher education in the coming decade. But they may disrupt our notions of teaching and learning even more.
