Following the first live session in the latest MOOC on Learning and Knowledge Analytics (LAK12), I did some reflection on the direction that Learning Analytics has taken over the past year or two. As far as I can see, Learning Analytics follows to a large extent the line of web analytics, but with the intention to improve learning by gaining insights into hitherto invisible connections between user characteristics and actions.
However, web analytics has, I believe, a very different objective when analysing people’s navigation patterns and tracking their activities online. This objective is to better influence user behaviour in order to direct them (unknowingly and personalised) to the pages and activities that matter – to the company not the user. In almost parallel, the expressed attitude and examples brought forward in favour of Learning Analytics, puts the main focus on understanding and influencing learner behaviour, and only to an extremely limited extent if at all, their cognitive development.
An often mentioned example is that of a jogger who trains up for a marathon run, and through collection of performance data becomes more motivated, is able to see progress, compares this to other runners, etc. Similarly, tools that track the usage of software applications on your computer, provide feedback that is useful if you think you should change the amount of time you spend on e-mails. Equally, tracking your own smoking or eating habits, will hopefully lead to achieving a personal goal. These are all valid examples where and how feedback loops can improve a person’s acustomed performance.
It is vitally important, though, that if Learning Analytics is supposed to make a beneficial impact on (self-directed) learning, it does not stop at manipulating learners in a way that these are merely conditioned into different behaviours! It is not enough to check behaviour patterns of learners even though some such feedback might be helpful at times. We need more LA applications that support metacognition and cognitive development. Even memory joggers are quite useful at this. One of the oldest I am familiar with and which I haved used to great benefit are vocabulary trainers. In using those, I could see that in the first run, I was able to answer maybe 46% of a given wordlist, increasing to 65% in the next run. Over only a few runs I was able to answer 96% of all questions. Not only was this summative feedback in % a motivator and excellent for my own benchmarking; I also was able to detect decline in memorised vocabulary and identify which words I was most likely to forget, once I stopped actively revising (say three weeks later).
Since I am most interested in cognitive development and less in learning behaviour patterns, I would like to see more Learning Analytics tools that allow this to happen.