Using a runners motivation to help solve Tin Can Big Data

Early April 2014 saw the eLearning Network run an event on Tin Can (xAPI).  There were some great speakers who created a real buzz around the new technology.

One of the main things that was discussed was Big Data.  Now that Tin Can will give us more flexibility of tracking various learning experiences there will be a lot of ‘data’ coming our way – for example, each learning experience, when and how the learner experiences it, what score they achieved etc.  Andy Wooler of Hitachi Data Systems mentioned that there will even be a requirement for someone in an organisation to be able to make sense of all this data and decide how best to use it.

I came away thinking about this and wondering how learning professionals would be best able to analyse the data coming in to ensure they can make sense of it to better support their staff.

Recently, I bought a Garmin watch to keep track of my running.  For those of you who don’t know, Garmin watches are GPS enabled devices that will track how far you have run, the elevation, your heart rate, your time. the speed at which you are running and even the weather!  All of this data is synced to your personal account which you can play back, compare and track yourself against your goals.

Tracking yourself against your goals….  that sounds familiar doesn’t it?

Thinking more on the subject, there is a lot of data available to me as a runner from such devices, apps and websites but there isn’t anyone ‘actively looking’ at my data and telling me what I need to do to achieve my goals – no, i’m motivated enough to do this myself and i’m sharing this data with others. It’s easy, it works and most of the sites i’ve used look really slick and inviting! Many of the sites provide badges, social sharing and are multi-device.

So what can we do to learn from this?  I think that before we start developing websites, apps or plugins that use Tin Can to track our learners experiences and before we hire expensive data analysis gurus, let’s think about ways to motivate learners to want to analyse their data themselves. They can see how well they are towards their goal, where they need to improve and share their data with peers and potential future employers.  If we can do this in a way that motivates them to use the data themselves then this will go a long way towards making sense of the ‘Big Data’ which is coming our way.