Course summary: algorithms and the lifestream

Starting EDC was daunting – reading the instructions on how to set up our lifestreams, I wondered how I would manage to deal with all the technical aspects of the course. Ten weeks and 105 posts later, this memory seems very distant now. And I have to say that, in the end, it was much easier than expected and posting on my blog on a daily basis almost became second nature.

Block 1: cyberculture

I found the cyber culture block difficult to get into because the themes were quite alien to me. While it was relatively easy to find information to explore this topic, I initially struggled to link it to education.

Aside from learning more about the various themes, these first few weeks were mainly about setting up the algorithms in IFTTT such as:

  • Setting up a rule to post YouTube and Vimeo videos when I hit ‘like’;
  • Adding articles to Pocket;
  • Showing tweets and retweets that are tagged #mscedc

The algorithms seem very transparent, however, what is unclear is how other algorithms shaped my lifestream. I realised that social, geographical, cultural etc. aspects were feeding into the selection of my content without me being able to influence it.

Block 2 : community cultures

Reading the literature on online communities and working in education has given me a sense of how beneficial virtual communities can be. While I found reading and commenting on other blogs very inspiring, I mainly focussed on what is going to be assessed, i.e. posting weekly summaries and reading the required articles. Being more of a lurker myself, I often wondered how I would have behaved if participation hadn’t been encouraged. I found Kozinets’ article particularly useful to understand how people behave differently online and how they can each benefit from online communities. Not only can they ‘have real social benefits, but also they have powerful effects on people’s sense of identity’ (Kozinets 2010, p.29). Online communities are no longer just a place for people with specialist interests; they can also play a big part in our everyday lives as well as in education.

Towards the end of EDC, everyone’s world changed completely due to the effect of the coronavirus. With schools and exam centres being closed in many parts of the worlds, many institutions are looking to provide education through online learning. It is too early to say how successful virtual communities will be in terms of replacing physical places of learning but we have already seen how people are finding creative ways to socialise online such as participating in virtual book clubs or an Instagram series to teach cooking. This will be vital for many people in order to overcome loneliness due to social distancing/isolation.

Block 3: algorithmic cultures

Overall, I enjoyed the last block most, possibly because our lives and algorithms are so intertwined. As mentioned above, I began to realise that my selection of content for my lifestream was in no way all due to my decisions. How were the videos and articles I shared through IFTTT affected by algorithms? Would I have found different sources if I lived in another country or was of a different gender? Technology had a big impact on my decisions and, at the end, it has become impossible to say which actions were performed purely by me. As Knox (2015) points out: ,[r]ather than technology being framed as simply the passive instrument of predefined educational aims, here the algorithm represents a much more complex relationship between humans and non-humans in education, pointing towards an increased entanglement of agencies in the production of knowledge and culture.’

Algorithms are everywhere and, no matter how much or how little we use technology, they are part of our lives. What this block and in particular the various algorithmic plays reinforced was that algorithms are in no way neutral. ‘Far from being objective, impartial, reliable and legitimate, critical scholars argue that algorithms possess none of these qualities except as carefully crafted fictions’ (Gillespie 2014a in Kitchin 2017, p.17). When relying on automated decisions in education, we need to be mindful of this in order not to discriminate students. Behind every automated decision lies a form of human judgement.

During the course of EDC, I have also become aware of the issues surrounding plagiarism detection software such as Turnitin. The majority of my lifestream consists of material that has been created by someone else. Yet, somehow, my lifestream is unique, reflects my learning journey and includes reflections based on what I have read and watched. How would an algorithm rate this assignment? Undoubtedly, it would detect a lot of ‘plagiarism’. This goes to show that Turnitin should be used with caution and perhaps tutors need to go back to reading students’ assignments more closely without making assumptions based on machine-generated results.


Kitchin, R. (2017). Thinking Critically about Researching Algorithms. Information, Communication & Society, 20:1, 14-29, DOI: 10.1080/1369118X.2016.1154087.

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.

Kozinets, R. V. (2010). Netnography: doing ethnographic research online. London: Sage.

Comment on My short video documenting how I played around with Facebook ads fell foul to YouTube’s algorithm managing spam and scams😆. You can see the video by clicking on the link. I HAVE APPEALED THIS TRAVESTY OF JUSTICE👊😋 #mscedc by smacleod

Very funny! And scary at the same time! Nothing is private on the internet and we need to be mindful that companies profit from our behaviour all the time.

Comments for Sean Flower’s EDC lifestream

The Machines Are Learning, and So Are the Students

Artificial intelligence is starting to take over repetitive tasks in classrooms, like grading, and is optimizing coursework and revolutionizing the preparation for college entrance exams.

from Pocket

Comment on block 3 artefact – my Eurafrican Youtube algorithmic play by smacleod

Fascinating experiment! The assumptions algorithms make simply based on race, gender etc. are so profound! While many of us are aware of this happening, it’s so difficult to tell how these assumptions really affect people. How would your worldview, your knowledge, your relationships change if we were provided with different information? Very thought provoking!

Comments for JB’s EDC lifestream

Weekly summary: week 9

youtube on a laptop
Image from pixabay

This week has been all about the algorithmic play. I spent a lot of time playing with the Youtube recommends algorithm and trying various ways to understand and manipulate it. It would have been naïve to think that I could figure out the algorithm within two weeks. I was baffled by a lot of the recommendations but I believe that there is a reason for suggesting these videos to me. What this exercise has taught me is how complex algorithms are and that no matter how aware we are of them, they can be very powerful in influencing our behaviour. This thought was reinforced by Kitchin’s (2017) article who stresses that algorithms aren’t purely technical and objective: ‘Other knowledge about algorithms – such as their applications, effects, and circulation – is strictly out of frame’ (Seaver, 2013, pp. 1–2). As are the complex set of decision-making processes and practices, and the wider assemblage of systems of thought, finance, politics, legal codes and regulations, materialities and infrastructures, institutions, inter-personal relations, which shape their production (Kitchin, 2014).

Another topic I explored on my lifestream this week was the rise of personalised learning. I can see why people get excited about it but, as with everything to do with Big Data, people often seem to forget the ethical side.


Kitchin, R. (2017). Thinking critically about and researching algorithms, Information, Communication & Society, 20:1, 14-29, DOI: 10.1080/1369118X.2016.1154087.