Posts Tagged ‘lak11’

In which I take a quick glance at another open course.

In spite of our best efforts at incorporating public transportation and, in the warmer months, serious commuter bike mileage, I spend an alarming amount of time ferrying offspring hither and yon. This precludes a lot of other activities, including any consistent writing for such things as open courses. But, depending on the traffic level and the weather, the drive time does foster opportunities to muse upon the events and information of the day.

So in musing about my brief, once-over-lightly, tip-of-the-iceberg foray into learning analytics descriptions and commentary, I found myself reflecting on my agreement with Viplav Baxi‘s response to George Siemens’ question about learning analytics critiques: the potential and actualization of learning and knowledge analytics will make our current systems of assessment and other learning processes look like a horse and buggy before the invention of the wheel.

OK, so I’ve rephrased this a bit, caught up as I am in the role of transportation jockey. But I’ve just spent the past week driving a most modern horseless carriage.  And it struck me as I inched along in a fresh three inches of chemically-converted slush that the vehicle comes equipped with a rudimentary form of “learning analytics,” especially in terms of feedback mechanisms. And here I’ve become acutely aware of the persistent discrepancies between the ideal and the reality from the learner/driver end of the equation as the technologies – and maybe even our psychological processes – undergo developmental and adaptive changes.

True, my car and I agree on the big picture. The point is to get from location A to location B in a safe manner while consuming as few fossil fuels as possible. It was ever thus. (Well, at least in this house, since we have never understood why one would drive vehicles insouciantly named after the landscapes they’re destroying… tundra, sequoia, etc.).

But now my car has taken on the role of data provider and driving analyst in ways that the venerable Small Outdoorsy Wagon has never done. It “responds” to my driving through various signals and signs. It’s a bit trying, at least in this initial phase.

For example, in the name of safety, the car is equipped with numerous bells and whistles, and yes, I mean this literally.  The one most perplexing to me is the Mack-truck-in-reverse beeping that occurs when I put this considerably less intimidating vehicle into reverse. BEEP BEEP BEEP BEEP. Every. Damn. Time…. I leave the garage. It’s not even outside the car. This I could see as a reasonable warning to others that the vehicle and a driver of indeterminate skill are on the move. But it’s inside the car. There’s no override. There’s no volume control. And I’m wondering what research has shown that such vast numbers of drivers are so confused about whether they are coming or going while piloting this car that all drivers need to be warned that they’re going backward.

Or how about the orange “slipping tires” warning light that blinks if there are…well, slipping tires? Which is about, oh, every quarter mile with our current road conditions. My peripheral vision is constantly caught by the flash of Threat Level Orange just off to the left, behind the steering wheel. What’s going to happen over time? I’m going to learn to ignore it, I suspect, which probably wasn’t the intent. And as an experienced driver, believe me, I know if I’m spinning my wheels and need to change tactics. Flashing lights at me just increases the number of things vying for my attention under already problematic circumstances.

I’m also feeling a bit ambiguous about the sheer volume of data that is suddenly available to me as a driver. True, dashboards (and I’ll point out my laptop has one, too) through the ages have provided drivers with all sorts of information. Speed being of most interest, I suppose, both when it was hard to come by and now, when it’s hard to keep down. Fuel gauge. Engine temperature. Oil level. Add some trip mileage. A clock. The radio controls.

What strikes me now, however, is that the degree of precision in this information has increased tremendously. True, it’s my choice (or is it?) to react to the data, but I’m finding a digital readout of 54, 55, 56, 57 (oops) to be a more exhaustive and rigid taskmaster than a needle quivering around the 55 mph mark on a dial. This is also true for the second set of feedback mechanisms that have suddenly appeared: the Hybrid System Indicator. All of a sudden, I know not only my exact trip mileage, but also have second-by-second information on battery power. And on how far I can travel on the remaining fuel at the current rate of speed (as if I could maintain that speed in rush hour traffic). And on exactly how many gallons are left in the tank. And on whether I’m pulling from the battery, from the gas, from both, or whether and how much I’m charging the battery (available as a scaled readout or as an animated illustration that reminds me of those movies of blood flowing through the heart chambers). And even more addictive: I can know the average number of miles I’m getting per gallon every single moment, to one place behind the decimal point. I’m not much of a gamer, but we’ve already developed a friendly household competition to see who comes back to the garage with the highest score.

So one question from a learning perspective is: has this information and analysis (provided partly by the car, and partly through my interpretations) somehow changed my behavior or knowledge as a driver? In this “getting to know you period,” I’d say yes. It’s easy, for example, to use the power monitoring to make minor adjustments to the acceleration rate when pulling away from a stop sign, especially if you’ve developed an aversion to seeing the little indicator zoom into the brown (cleverly equaling “yucky”) fossil fuels zone.

But the other thing that concerns me is how much time I spend looking at these gauges, drawn in by the hobgoblins of consistency and accuracy and constant feedback, and the mixed-motive enticements of low fuel consumption/less pollution/economic savings. The speedometer checks are actually more essential, as the quietness of the high tech engine makes it hard to recognize the speed at which I’m travelling; in other words, I have more potentially “useful” information and thus greater potential control over my “results,” but I am receiving fewer environmental cues. (And how much precision does one really need? Do I really need to know that the car’s interior is 67 degrees, and will my driving experience be all that much cozier if I set it to 69? And finding out that my life behind the wheel averages 23 miles per hour? I think I’d have rather not known.)

I’ve also become acutely aware that consistent monitoring and making use of all this information means… less time looking at the road. Paying less attention to the other cars. Pretty much ignoring the scenery. I might have more safety warnings, but my new, information-rich processes aren’t necessarily contributing to more safe or enjoyable procedures. And all of this information and the constant adjustments I make in response create, I can attest, a more mentally fatiguing driving experience. (Something I, three months into a snowy, x-hundred-rush-hour-miles-a-week winter, wouldn’t have thought possible).

So how much of this new wealth of information and responsive feedback will I simply begin to incorporate without this extra refelection over time? To what degree will I assert my autonomy as a driver and simply ignore what I see as bothersome analysis, fuel consumption results be damned?  How soon will all of this be old hat, whereby the constant exposure to the technology will gradually wear me down into unreflective compliance with those digital measuring sticks, and I’ll likely forget the initial dissonance of these changes? And what about the household’s driver-in-training, whose arcane, state-required  “driver’s education” tells new drivers to honk at bicyclists ahead of them as a warning (wtf?) and preaches about the dangers of cell phones behind the wheel, but doesn’t begin to recognize the new cognitive demands of driving such a technically advanced vehicle, with four screens worth of data accessible via a steering wheel control?

On the other side: does this discussion really capture the full potential of– or any reasonable hesitation about– the sophisticated complexity of learning analytics?  It’s more about an interim or introductory stage in driving analytics, to be sure. Already, there are cars that do far more than mine. Some remember, for example, the preferred interior settings of each individual driver. Some, like the Google car, even drive themselves. Ultimately, it’s clear that I’ll be adjusting to the vehicle, not the other way around, which seems indeed to be the most rudimentary of “responsive” systems.

I also recognize that this conversation is still all about driving, and that’s a paradigm problem. I can get pretty excited about 47.7 miles per gallon when I’d gotten used to a (mentally calculated) 28 mpg. But these new numbers, no matter how improved, aren’t a seriously effective response to the larger implications of fossil fuel consumption in a shifting climate. Better mileage is insufficient for the leap we need to make. So I’m hoping this, too, reflects an interim, rather than ultimate, solution. I’d say the very act of driving needs to be scrutinized as well, along with a whole host of other forms of consumption. (Teleportation, anyone?)

And finally, I’d note that the seductive power of the oversimplified analogy can create a misleading but unfortunately persistent picture. So I suspect I’d best spend more time surveying the route maps and take these musings for another drive… and thoughtfully prepare to cross some fancy new bridges as I come to them. 

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