A few thoughts from LAK12, the Learning Analytics and Knowledge conference. I’m at the event in Vancouver BC this week, and while many of the panels are addressing how institutions will prepare themselves for educational "big data," I was particularly interested in the presentation today by Verónica Rivera-Pelayo about the opportunities at the intersection of the quantified self movement and reflective learning -- learning analytics and the individual, not just the institution.
What is the Quantified Self Movement?
Almost every time we search or click or buy or like or link online, there’s data collected about our transactions. As such companies know a lot about us. But how can we collect and leverage this sort of data to know more about ourselves? That question drives the quantified self movement, which involves collecting and tracking one’s own data through hardware (e.g. sensors like Fitbit), software (e.g. time-tracking tools like Chrometa), and/or handwritten journals and log entries.
The quantified self movement is very much about learning about the self, although much of its emphasis (and adoption) has been focused on health or productivity, and less on what we traditionally associate with “education.” This process of self-discovery is typically in the service of something actionable – losing weight, training for a marathon, cutting carbs, tracking and hopefully avoiding migraine headaches, and so on.
How Does This Relate to Learning?
The question of how this relates to learning is actually wide open, dictated by what someone wants to know, to track, to learn.
You could track what you read, for example. Think of all that your Amazon Kindle knows about your reading habits: what you read, when you read, how much you read, where you stop, what you highlight, what you annotate. Or you could track what you write: how many words, how many pages, when you write, how much gets scrapped or tweaked, which verbs or adjectives you tend to overuse, how your phrasing falls into certain patterns (for better or worse)? You could also track your personal learning network: who you follow, what you say, whose links you tend to click on and actually read, who you ask for help and how quick they are to respond.
None of these necessarily mean you’re learning, of course. You could your viewing of a YouTube video on linear regression, for example, and still not understand linear regression (speaking for myself here). You could read everything that Daniel Pinkwater has ever written and still not be able to answer a couple of reading comprehension questions on a multiple choice test (ha ha ha).
Can the quantified self movement when applied to personal learning be as transformative as it has been when applied to personal health?
So We’re Quantified…Now What?
After you’ve collected your data and quantified your self, then what? Typically you visualize and/or analyze it. The idea is to actually be able to do something with what you discover – diagnose, moderate, modulate, improve, act. In the case of learning analytics, this could include identifying things like when are you most productive – morning, noon or night? Is there a relationship between mood, memory, interest, engagement, and retention?
Although I see lots of possibilities for the quantified self and learning, I do wonder if many folks will know what to track? It's easier when you apply this to something like dieting. There is, after all, a substantial “self-help” industry around health. There’s a ton of both “folk” and “expert” knowledge about what matters: our calorie intake, our calories burned, fats, carbs, vitamins, sleep habits, and so on. Is there the same sort of understanding – right or wrong – about what matters to our intellectual health? Do we know what to track in order to aid, to diagnose, to maximize our own learning?
Of course, you could just take a page from Stephen Wolfram and just track everything. That’s all in the service of all of us boosting our analytics skills too.
My concerns – and maybe “concern” is too strong a word – about the quantified self and learning involve a fixation about transactional data, about the easily quantifiable but in the end meaningless. There are a lot of obvious numbers in our day-to-day lives – what we read, where we click, what we like, how much time we spend studying, who we talk to and ask for help. It’s the administrivia of education. And frankly that seems to be the focus of a lot of “what counts” in learning analytics. But does this really help us uncover, let alone diagnose or augment learning? What needs to happen to spur collection and reflection over our data so we can do a better job of this – not for the sake of the institution, but for the sake of the individual?
Disclosure: My travel and accommodations to BC were paid for by LAK12
Photo credits: Matthew Harrigan