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.
Another week has flown past before I feel I have truly got to grips with it. I am a bit stuck in a ‘catch up one week at the start of the next‘ loop. I really enjoyed looking at and commenting on quite a few ethnographies, but made myself move on mid-week. However, I did add Pocket to IFTTT!
Trying to make sense of algorithms was worrying as I am definitely out of my comfort zone with numbers never mind big numbers. I began by watching some instructive talks and videos.
Despite my focus on my YouTube algorithm exercise the main element that has come through my week is yet again online community. On Twitter I spotted a good article about Google and education and this started a conversation about community and sharing and it turned out to be very circular indeed.
The Tweet from Amanda Taylor re article in the Conversation, author, Ibrar Bhatt brought algorithms and/vs serendipity to life: Amanda in Lancaster University, worked in Queen’s previously, Ibrar wrote article whilst at Lancaster University, now works at Queen’s in a different department from me. When I first retweeted the article I had no idea where Amanda was located or anything about the author so discovering such close network nodes showed me how algorithms are at play without me even realising, as I have no recollection of how I came to follow Amanda in the first place.
Lastly, as the week closes I am again thinking on the paradox of Higher Education’s continual resistance to change whilst simultaneously lauding technological innovations as potentially disruptive. Each time change is slow and minimal with a focus on administrative benefits rather than the learning experience. The virtual learning environment, VLE, is an ever present piece of evidence of this.
There you go Jeremy, proof I act on your feedback – word count under 300!
I have tried to pull all of the different elements of my week into a single video artefact: readings, audio produced in part by algorithms, visuals generated by algorithm alongside the algorithmic generated YouTube recommendations for me. The final curated video displays the human aspects behind the numbers.