Probably most people do it or have done it in the past sometime: using sensors or measuring tools to track yourself. Common examples are counting your calories, daily exercise, monitor your heart rate or blood pressure, track your running path and pace, electricity consumption, words per minute typing, etc. Lots of gadgets and apps are on the market that support various of these activities. With mobile and ambient sensors becoming ever cheaper to produce, and more easily available through phones and other pocket devices there are near endless possibilities for analysing your own behaviour during your daily activities. With this we see an upsurge in interest in personal analytics.

Sleep data

What does it have to do with learning? Well, I see at least three compelling connections: Firstly, it’s always good to learn something about yourself. Information that otherwise you would not pick up. Secondly, you can set yourself objectives, such as losing weight, quit smoking, use less electricity, be more fit, and so forth. Thirdly, you can put yourself into perspective and position yourself against a crowd-sourced data pool.

Most of these tracking activities are not cognitive learning processes, but behavioural ones. Learning new behaviours becomes more difficult with age and without external supervision, so tracking your own progress puts you in charge. Tracking yourself can be an immense motivator and driver for personal improvement.

Although such analytics activities have been around for ages, the increased amount of handy available tools gave rise to a new movement of putting your life into numbers: the Quantified Self. A first European Conference is going to be held in Amsterdam in November 2011. In its most extreme form it consists of people submitting themselves to life-tracking, i.e. quantifying their entire physical life. Unfortunately, this is still a rather esoteric approach and lacks scientific and pedagogic attitudes and thoroughness. However, it catches on rapidly in the world of data, learning, and knowledge analytics, so I expect it to enter scholarly research very soon.

Why has this not entered education research earlier? Mostly, I believe, it has to do with the fact that the understanding of ‘learning’ in the educational sciences is often restricted to academic learning and knowledge acquisition, not focused on behavioural learning which is a domain of Psychology. Even so, just like with Learning and Knowledge Analytics in general, it may simply be that ready and cheap access to data and information in everyday practice had not had the critical scale yet. Now, it seems, this threshold has been reached and it’s time to embark on a journey of self-discovery!

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