It’s not unknown for me to enter my name into a drawing for something, if I don’t mind what they’re doing with it. It’s almost unknown, however, for me to actually win, but that’s actually the case a month or so ago when I put a comment on a blog prior to the MacWorld show, and won a copy of Navigon turn-by-turn navigation software for my iPhone. I’d thought a dedicated one might be better, though I’d have to carry two devices, but if I moved from an iPhone to Droid or Pre I’d suffer. But for free…
When I used to travel more (and that’s starting again), I’ve usually managed to get by with Google Maps: put in my desired location (so glad they finally put copy/paste in, such a no-brainer rather than have to write it elsewhere and type it on, or remember, usually imperfectly). In general, maps are a great cognitive augment, a tool we’ve developed to be very useful. And I’m pretty good with directions (thankfully), so when a trip went awry it wasn’t too bad. (Though upper New Jersey…well, it can get scary.) Still, I’d been thinking seriously about getting a GPS, and then I won one!
And I’m happy to report that Navigon is pretty darn cool. At first the audio was too faint, but then I found out that upping the iPod volume (?) worked. (And then it didn’t the last time, at all, with no explanation I can find. Wish it used the darn volume buttons. We’ll see next time. ) However, it does a fabulous job of displaying where you are, what’s coming up, and recalculating if you’ve made a mistake. It’s a battery hog, keeping the device on all the time, but that’s why we have charging holders (which I’d already acquired for long trips and music). It also takes up memory, keeping the maps onboard the device (handy if you’re in an area with bad network coverage), but that’s not a problem for me.
However, my point here is not to extol the virtues of a GPS, but instead to use them as a model for some optimum performance support, as an EPSS (Electronic Performance Support System). There’s a problem with maps in a real-time performance situation. This goes back to my contention that the major role of mlearning is accessorizing our brain. Memorizing a map of a strange place is not something our brains do well. We can point to the right address, and in familiar places choose between good roads, but the cognitive overhead is too high for a path of many turns in unfamiliar territories. To augment the challenge, the task is ‘real time’, in that you’re driving and have to make decisions within a limited window of recognition. Also, your attention has to be largely outside the vehicle, directed towards the environment. And to cap it all of, the conditions can be dark, and visibility obscured by inclement weather. All told, navigation can be challenging.
While the optimal solution is a map-equipped partner sitting ‘shot-gun’, a GPS has been designed to be the next best thing (and in some ways superior). It has the maps, knows the goal, and often more about certain peculiarities of the environment than a map-equipped but similarly novice partner. A GPS also typically does not get it’s attention distracted when it should be navigating. It can provide voice assistance while you’re driving, so you don’t need to look at the device when your attention needs to be on the road, but at safe moments it can display useful guidance about lanes to be in (and avoid) visually, without requiring much screen real estate.
And that’s a powerful model to generalize from: what is the task, what are our strengths and limitations, and what is the right distribution of task between device and individual? What information can a device glean from the immediate and networked environment, from the user, and then provide the user, either onboard or networked? How can it adapt to a changing state, and continue to guide performance?
Many years ago, Don Norman talked about how you could sit in pretty much any car and know how to drive it, since the interface had time to evolve to a standard. The GPS has similarly evolved in capabilities to a useful standard. However, the more we know about how our brains work, the more we can predetermine what sort of support is likely to be useful. Which isn’t to say that we still won’t need to trial and refine, and use good principles of design across the board, interface, information architecture, minimalism, and more. We can, and should, be thinking about meeting organizational performance, not just learning needs. Memorizing maps isn’t necessarily going to be as useful as having a map, and knowing how to read it. What is the right breakdown between human and tool in your world, for the individuals you want to perform to their best? What’s their EPSS?
And on a personal note, it’s nice to have the mobile learning manuscript draft put to bed, and be able to get back into blogging and more. A touch of the flu has delayed my ability to think again, but now I’m ready to go. And off I go to the Learning Solutions conference in Orlando, to talk mobile, deeper learning, and more. The conference will both interfere with blogging and provide fodder as well. If you’re there, please do say hello.