Finally, through the analysis of the Tweetorial and discussions during the Hangout I began to suspect that I look at the data perhaps somewhat differently. It was raised that the data privileged quantity over quality and it was clear that in the data sets those who shout loudest and most often win the day.
The data will never be simply numbers to me, I will always want to analyse thinking of the humans as the nodes, joining and connecting. Maybe lots but maybe less, like the organic nature of a tree with many branches and buds, as the ‘visible’, roots and seeds as the ‘invisible‘. I still believe our networks are too complex to be reduced to this binary referred to in Knox (2014).
At the beginning of the week I focused on looking around to get some basics about Learning Analytics (LA), adding links to the HEA and Jisc for example. Overall in general I got the sense that the advantages of LA for the learner was for the institution to be able to provide support and guidance. The proof of this seemed to be increased retention. However, I then explored many examples of algorithms gone wrong and the human impact of this.
I have been a LA sceptic without having an in-depth knowledge on the topic and I was quite surprised that during the tutorial many shared my cynicism and highlighted the need for more qualitative and contextualised analysis.
As education has higher and higher student numbers and fewer teachers LA is yet another way to solve this problem – I tried to show this on my image – but I don’t believe that it will. In any conversation I have ever had with others LA is always framed in terms of surveillance. Essentially, ‘we need to track our content to prove student didn’t engage’. Every time I ask why. ‘Proving’ the student has clicked on content is absolutely no proof of engagement. Only a human making contact, face to face or virtual, can deduce this and know if the student is having problems or is simply working at their own pace and timetable. I added my own annotated LARC report to help me frame this.
In addition, there is also the issue of ethics around the collection and analysis of all this ‘big data’ and I tried to highlight this by including the blog post by Lorna Campbell who tried to question a software company on its collection of data policies.
I ended the week by including a Storify and a TAGS Explorer map of the, very informative and enjoyable, Tweetorial as I tried to visualise the conversation as this helps me see past the numbers.
It seemed only fitting to include my LARC report for the week after discussions around learning analytics. I have annotated it briefly with some notes but it definitely serves the purpose of highlighting the need for context. Without any context it would baffle an outsider, showing a poor level of social interaction, engagement or attendance.
A quick overview is that it was week 9 of a ten week course and two assignments were looming in my mind. I was focused on working on my learning challenge for the first of these and prioritised this along with reading and planning my writing. None of this was captured in the ‘numbers’. It also didn’t compare the week with my fellow classmates but to the course average which meant there was no direct comparison. This might have served to show some of the context as many of the others would be generating similar reports for the same week.