A work project moving from the planning phase into full on delivery, together with commitments over the weekend, left precious little time for blogging last week.  However, I did manage to find several periods of time over the week to write the required analysis of the Tweetorial.  I’ve since had a few more thoughts on the use of Twitter for education and I will either add these to the analysis or create a separate blog post.

The fact that I have what amounts to some self-imposed analytics on my Lifesteam, in the form of the calendar of blog posts, hasn’t escaped me.

I included the calendar for two reasons, firstly because I thought it might be helpful to future students of this course who visit my blog and secondly because it’s a reminder to me of the course requirement to ‘add to the Lifestream almost every day’.   The irony of this is that the Tweetorial analysis I worked on over several days only shows as a single post – another example of analytics not necessarily ‘making visible the invisible’.

As part of my current work project I’m using Articulate Storyline to create a tool that will enable our practice managers to review their current knowledge and use their input to point them to resources that will help them.  This has involved creating a means of filtering their input, which has required a multi-stage approach and several hundred conditional triggers.  In effect I’m writing my own algorithm and it will be interesting to apply some of the thinking I’ve done around Algorithmic Cultures to how the tool might be viewed by those it’s intended for, and by others in the business.

The Tutorial on Friday was lively and useful.  It was interesting to hear everyone’s views on the Tweetorial and the Algorithmic Cultures block.  In common with my fellow students my thoughts are now turning to tidying up my blog, continuing to add metadata and starting preparation for the multi-modal essay.

It’s been an interesting week experimenting with algorithms. I’ve enjoyed trying to ‘reverse engineer’ the Amazon recommendation algorithm and, ultimately, going some way toward disproving my own hypotheses.

Reflecting on the cultural aspects of algorithms, I see similar dichotomies in the views people hold about them to those we saw documented in the literature relating to cyberculture and community culture.  To me this is clearly a linking theme and I see possibilities in exploring this in my final assignment for this course.

As with many other topics, the views people hold are likely to be heavily influenced by the media and, just as all things ‘cyber’ are often painted as worthy of suspicion, this does seem to be a ‘go to’ stance for copy writers and producers when taking a position on algorithms.  The banking crisis is probably the biggest world-wide event that has contributed to this and other stories, such as reliability issues with self-driving cars, or inaccuracy of ‘precision munitions’, add to the general feeling of unease around the use and purpose of algorithms.  I chose the latter two examples deliberately as there is a moral as well as technical aspect to both.

So the stories about algorithms that help people control prosthetic limbs more effectively, or to ‘see’ with retinal implants, or even driver-less cars  travelling tens of thousands of miles without incident, can be lost amongst more sensationalist stories of those same cars deciding ‘whose life is worth more’ when an accident is unavoidable.

As a result I wonder how much knowledge the general public has about the algorithms that make their day to day life a little easier, by better predicting the weather, ensuring traffic junctions cope better with rush hour traffic, or even just helping people select a movie they’re likely to enjoy.

One could argue that this underlying distrust of algorithms is no bad thing, particularly if this can lead to unbiased critical appraisal of their use in a particular field, as highlighted by Knox, J. (2015) with regard to their use in education:

“Critical research, sometimes associated with the burgeoning field of Software Studies, has sought to examine and question such algorithms as guarantors of objectivity, authority and efficiency. This work is focused on highlighting the assumptions and rules already encoded into algorithmic operation, such that they are considered always political and always biased.”

This week has made me a little more uneasy about the way “algorithms not only censor educational content, but also work to construct learning subjects, academic practices, and institutional strategies” Knox, J.  (2015).  In my professional practice we do not have the sophistication of systems that would make this a concern, but our learners are exposed to and learn from other systems and their apprehensions about how we might use their data will no doubt be coloured by their view of ‘big data’.  With that in mind this is clearly a subject I should have on my radar.

Apologies for writing double the word limit for this summary and including new content rather than summarising, it’s one of those subjects that, when once you start writing, it’s difficult to stop!

References:

Knox, J. 2015. Algorithmic Cultures. Excerpt from Critical Education and Digital Cultures. In Encyclopedia of Educational Philosophy and Theory. M. A. Peters (ed.). DOI 10.1007/978-981-287-532-7_124-1