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There’s a fair bit of hype around wearables – smart watches, bands, rings, belts (!) and the list could go on. As a trend, it’s still a few years from actual impact (according to the Gartner Hype Cycle). I am curious about the possibilities for application to training or learning situations. So far, we have naturally gravitated towards examples like Google Glass or Oculus Rift, which hold potential for delivering instant information or training.
How would wearables apply to training/learning?
I’m equally curious about wearables for monitoring and feedback on your learning and in gadgets to deliver training or support. In a recent #chat2lrn, there was some brief conversation around this as a trend it got me thinking about some potential blog posts. I can see several scenarios worth exploring:
In the November issue of HBR, the feature article (“How Smart, Connected Products are Transforming Competition“), identifies there are three aspects to consider:
Physical components comprise the product’s mechanical and electrical parts. In a car, for example, these include the engine block, tires, and batteries.
Smart components comprise the sensors, microprocessors, data storage, controls, software, and typically, an embedded operating system and enhanced user interface. In a car, for example, smart components include the engine control unit, anti-lock braking system, rain-sensing windshields with automated wipers, and touch screen displays. In many products software replaces some hardware components or enables a single physical device to perform at a variety of levels.
Connectivity components comprise the ports, antaennae, and protocols enabling wired or wireless connections with the product. Connectivity takes three forms, which can be present together:
- One-to-one: An individual product connects to the user, the manufacturer, or another product through a port or other interface –for example, when a car is hooked up to a diagnostic machine.
- One-t0-many: A central system is continuously or intermittently connected to many products simultaneously. For example, many Tesla automobiles are connected to a single manufacturer system that monitors performance and accomplishes remote service and upgrades.
- Many-to-many: Multiple products connect to many other types of products and often also to external data sources. An array of types of farm equipment are connected to one another, and to geolocation data, to coordinate and optimize the farm system. For example. automated tillers inject nitrogen fertilizer at precise depths and intervals, and seeders follow, placing corn seeds directly in the fertilized soil.
So in a series of posts, I’ll explore wearables and training/learning for these situations, and also look at the role of the xAPI (here’s an awesome post on “Getting Started” by Aaron Silvers). If you have suggestions or examples, please pass them on!