Week 11 Summary – Algorithms in Education

Click for article – How AI is taking over the classroom.

In the final extended week of the course I decided to further my social media interactions on algorithms by exploring how they increasingly intersect with secondary education. Recently, my school invested in Century Tech, a nascent technology start-up that offers a teaching and learning platform powered by artificial intelligence. Using vast quantities of student data gathered through diagnostic assessment, as well as student responses in learning activities, the AI is able to use algorithms to generate unique and individualised pathways for a child’s progress in core subject areas.

This made me ponder the role that algorithms may play in education in these coming years, and the impact that that an emerging algorithmic culture will have on classrooms and teachers. Adrien DuBois, in his TEDx speech is vociferous in his view that ‘teachers are on the path to becoming obsolete’. It is impossible to know how accurate this assertion will be.  That said, other evidence from the lifestream suggest whilst AI, and the algorithmic culture they create, are certainly a threat to corporeal teachers’ viability, there is a strong counter-argument offered that posits human creativity and insight, inherent qualities within the teaching profession, are irreplaceable and cannot be replicated by machines.

It is perhaps the middle ground being presented in the third YouTube clip,  that really shows what the future truly holds. The clips explores the growing symbiotic relationship between teacher and technology, with AI and their associated algorithms, working as supporting actors alongside the teacher in developing students’ education, particularly that of special educational needs and ensnaring student engagement and positive behaviour.

Week 10 Final Summary – Thinking About Algorithms

Thinking about algorithms

As I draw to the end of Education and Digital Cultures, there are a number of issues I would like to reflect upon to close the blog. As a nascent student of digital education, algorithms have been a key player in the development of my own knowledge and understanding, with them ‘sorting, filtering, searching, prioritising, recommending, deciding and so on’ as the course has progressed. As David Beer states, an algorithm provides us the opportunity to ‘to shape our knowledge and produce outcomes’ (Beer, 2017, 2) This has certainly been true throughout the EDC module, and there is there is no doubt they have played a vital role in sculpting my understanding of digital cultures within an educational setting, and cultivating my success in the completion of this lifestream.

Despite much of the evidence from the core reading that ‘algorithms produce worlds, rather than objectively account for them’ and that they are ‘manifestations of power’ (Knox, 2015), I would still hold the view that much of the algorithmic governance, in the context of my EDC learning, has been fairly innocuous in nature. Perhaps others would argue this position is naïve, but generally, I am confident that the algorithms throughout the lifestream have always steered my learning in positive directions, offering sensible and useful links to capture my interest, and further learning. This was mostly frequently noted use within my use of YouTube, whereby recommendations normally had congruence with the prior clip, that I had watched or searched for. Whilst my algorithmic play noted the problematic nature of this within other settings, and how this could entrench users in a negative cycle of confirmation bias, within an educational setting there are real benefits to this for the potential it has in furthering learning. In short, I have not felt undertones of subliminal messaging encoded into algorithmic suggestions throughout the duration of this course.  That said, I do not deny the existence of algorithmic power, and the manipulative qualities they possess. Indeed, ‘algorithms …are the new power brokers in society’ (Diakopoulos, 2013 cited in Kitchin, 2017). That cannot be denied.

Finally, I wonder where algorithmic governance leaves education, particularly high school children, many of whom are happy to mindlessly watch clip after clip on YouTube, or click on every link or suggestion within their social media?  I wonder how much this impacts on their ability to harness enquiry skills or ask valid questions, and steer the direction of their own learning. Do the algorithms exert more influence on their learning pathway than their own processes of enquiry and logical thinking? Are the algorithms encouraging students to think less, and follow more? Is this further contributing to a spoon-feeding, instant gratification culture, that appears to be growing in younger generations? Furthermore, if the algorithms lead students down an incongruous route, how much time would be wasted in watching superfluous clips or heading up ‘digital blind alleys’, before a student is able to realign with the task in hand? Perhaps, with this in mind, it is incumbent upon the educator to ensure that use of this media is mitigated or digital tasks are directed more so by the teacher, than that of an algorithm.

Beer, D. (2017) The social power of algorithms, Information, Communication & Society, 20:1, 1-13, DOI: 10.1080/1369118X.2016.1216147

Kithcin, 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

Week 10 – Algorithmic Play


Choosing my social media – For my algorithmic play activity, I decided to focus on my most frequently visited social media site – YouTube. I watch a variety of YouTube clips almost every evening as one of my primary sources of news, and I often follow my selected pathway of clips, as recommended by the YouTube algorithms.
That said, I was interested to monitor more closely how the algorithms ‘guide’ me in my recommended viewing and personal decision making, and whether or not, I was actually in the driving seat.

I decided to start with the type of video that I watch almost every day – The View. This is a daytime American talk show, co-hosted by four women, including Whoopi Goldberg. The show is highly political in nature, and discussion amongst the women mostly centres around American politics, and in particular the Trump Administration.

Methodology – Before starting the algorithmic play I decided to delete the watched history, to avoid algorithmic influence from previous YouTube sessions and other videos I had watched.
When the sidebar of recommendations was shown, I opted for clips that piqued my own personal interests and I always chose one from the top six recommended videos. I continued to click through the recommendations for over 30 videos, and I recorded where it took me. The results of this pathway are shown on the Timetoast attached to this blog.

Reflections – It is clear that starting the algorithmic play with a television programme such as the View, resulted a preordained pathway being lain by the algorithms. The View, as mentioned, is highly political with three-quarters of the panel coming from liberal, left-wing backgrounds. The programme has a high degree of ‘Trump-bashing’ and although there is one Republican panellist, Megan McCain, unlike the majority of the Republican Party, she is an ardent outspoken critic of President Trump. The initial clip I watched focused on the Trump Administration’s lacklustre response to the Coronavirus pandemic.
It seems that the tenor and tone of this particular clip was highly influential on the subsequent recommended pathway suggested by the algorithms. Each of the clips that followed were imbued with the following themes:-

• Left-wing liberal news organisation e.g. Vox Media/ CNN/ MSNBC (20 clips)
• Anti-Trump (9 clips)
• Coronavirus Pandemic (7 clips)
• Race relations (4 clips)
• Brexit (2 clips)

At several points the algorithms had restricted my options, limiting what I could see and what I could choose for a period of time. In particular this happened when I selected the first of the Vox media clips. This resulted in being ‘stuck’ with only Vox choices to choose from for another 12 selections.
In order to change the options of the algorithm, I purposely selected a video clip that would create a new direction. This worked, and I was able to ‘escape’ the Vox loop and move onto content created by other organisations, although still within left wing, liberal media.

On reflection, it seems that there was certainly a loop of information, with the algorithms directing me to clips with very similar themes and information. At no time was I directed towards media such as Fox News or other right-wing media groups.

It is clear that with this type of ‘algorithmic power’ or ‘algorithmic governance’, there is threat of algorithms giving rise to confirmation bias in users. This is highly problematic, particularly in a society that is extremely polarised in political opinion.  How can society ably solve problems if it is unable to objectively see the other side of an argument? If algorithms do not show me alternative political opinion, how will I ever be able to understand opposing perspectives? This type of algorithmic echo chamber, is therefore very dangerous. This has congruence with the view put forward in by Rob Kitchin who states that “Far from being neutral in nature, algorithms construct and implement regimes of power and knowledge…. Algorithms are used to seduce, coerce… regulate and control: to guide and reshape how people… and objects interact with and pass through various systems… ” (Kitchin, 2017, 19).

Ethical Issues – There are a certainly some ethical issues to consider. For instance, there would be very little doubt, having seen my list of viewed videos, as to which end of the political spectrum I belonged. Could this data be misused or manipulated? Do my political affiliations no longer hold the same degree of privacy as they had done in the past, now that such data is widely available to large companies and organisations?

Another ethical consideration is how to disentangle between private and professional. As a user of YouTube both at home and at work, it is important to the ensure that the algorithms do not unnecessarily reveal private data and personal preferences in a professional setting, and so ensuring appropriate log ins are used in each of the settings.

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

Week 9 Summary – Algorithmic Bias

David Beer raises the issue of the decision-making power of algorithms and identifies that there is need to understand how algorithms shape organisation, institutional, commercial and governmental decision making (Beer, 2017). There are criticisms of those holding the view that of algorithms as ‘guarantors of objectivity, authority and efficiency’ and with others arguing that due to the fact algorithms are created by humans, they embed layers of social and political bias into their code, that result in decisions that are neither benign or neutral. Furthermore, these “decisions hold all types of values, many of which openly promote racism, sexism and false notions of meritocracy” (Noble, 2018). As such ‘algorithms produce worlds rather than objectively account for them, and are considered manifestations of power’ (Knox, 2015).

It was this notion of algorithms bias that drove my inquiry in this week’s section of the lifestream blog and there was no shortage of social media commentary on the issue. Cathy O’ Neil identifies this in her YouTube clip and supports the view by claiming that algorithms are not objective and that they are merely ‘opinions embedded into math’. Perhaps most interesting, was the work of Joy Buolamwini, whose investigation  artificial intelligence face recognition software, has unearthed inherent racist and sexist elements from its developers.

To what extent are racism values embedded into algorithms?
Joy Buolamwini’s has carried out extensive research on how algorithmic code determining facial recognition, fails to recognise black women – Click the image for more detail

However, where does this notion of algorithmic bias intersect with education and what type of educational landscape will the algorithms produce? With the rise of anti-plagiarism software, and the growth of intelligent teaching and learning platforms such as Century Tech, many educators fear that there is incremental dependency on algorithms within schools and colleges, particularly for assessment. This is certainly not without difficulties or tension. Ben Williamson claims that many studies have highlighted inaccuracies in the Turnitin software, which many institutions use to cross-check student work, incorrectly branding some students as cheats, whilst missing other, and very clear instances of plagiarism. This ultimately leads to a growing level of distrust between youngsters and their educators, and is responsible for breaking down relationships as the use of technology, and algorithmic dependency increases. How else will students and teachers be negatively impacted by algorithmic biases (or errors) and, as dependency on these tools continues to grow, will educators be able to even identify when this happens, let alone how to mitigate it?


  • Beer, D. (2015) The social power of algorithms, Information, Communication & Society, 20:1, 1 – 13, DOI: 10.1080/1369118X.2016.121614
  • 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
  • Noble, S. (2018) Algorithms of Oppression, NYU Press, New York.
  • Williamson, B. (2019). Automating mistrust. Code Acts in Education

“Algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions… They’re just automated assumptions. And if you don’t fix the bias, then you are just automating the bias.” https://t.co/dWrDdN1FyL

Liked on YouTube: The Truth About Algorithms | Cathy O’Neil

Some key takeaways from this short clip, that have congruence with the themes of the Algorithmic Cultures block.

Cathy O’ Neil argues that algorithms being presented as objective fact is a lie. She says ‘a much more accurate description of an algorithm is that it’s an opinion embedded in math“.  “There’s always a power element here” she adds, and that “every time we build an algortihms, we curate our data, we define success, we embed our values into algorithms.”


Algorithms of Oppression

“Part of the challenge of understanding algorithmic oppression is to understand the mathematical formulations to drive automated decisions are made by humans being. While we often think in terms such as ‘big data’ and ‘algorithms’ as being benign, neutral, or objective, they are anything but. The people who make these decisions hold all types of values, many of which openly promote racism, sexism and false notions of meritocracy, which is well documented in the studies of Silicon Valley and other tech corridors”

– Noble, S, (2018) Algorithms of Oppression

Article showing congruence with Rob Kithcin’s view that ‘we are entering widespread era of algorithmic governance, where algorithms will play an increasing role in the exercise of power’ (Kitchin, 2017) https://t.co/A2hbTjrAsl via @Technology_NS

Week 8 Summary – How Algorithms Shape Our Lives

The origins of the word alogrithm – click for a great BBC clip

The TEDx presentation by Kevin Slavin in this week’s lifestream, argues that we “need to rethink a little bit about the role of contemporary math… its transition from being something we extract and derive from the world to something that actually starts to shape it – the world around us and the world inside us.” This has congruence with the themes being explored in the core reading that posits in recent years algorithms have become “increasingly involved in the arranging, cataloguing and ranking of people, places and knowledge… They are becoming increasingly ubiquitous actors in the global economy, as well as our social and material worlds.” (Knox, 2015).  In essence, algorithms are now major actors in contemporary human society and culture.

On personal reflection it is evident that algorithms are highly influential in my own life, and are certainly shaping my every day thoughts and actions. I need only consider my Netflix recommendations to see tangible evidence of how an algorithm can shape day-to-day decision making. This was surmised in both news articles in the lifestream, each which explored the incredible power of major organisations such as Amazon and the impact they have had, and continue to have, on contemporary culture. As the Observer article recognises, this provides these companies with tremendous power, and raises the question of algorithmic objectivity. Are automated processes completely free of biases, or are they, as many would suggest enmeshed with corporate or political biases?

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

Liked on YouTube: How algorithms shape our world – Kevin Slavin

Some strong links with the core reading in this YouTube clip that identifies the ‘pervasive’ nature of alogorithms and how they shape and mould every day life.  Similar to what David Beer argues that “algorithmic systems feed into people’s lives, shaping what they know, who they know, what they discover and what they experience.  The power of algorithms here is in their ability  to make choices, to classify, to sort, to order and to rank.” (Beer, 2017, 6)


Beer, D. (2017) The social power of algorithms, Information, Communication & Society, 20:1, 1 – 13.

Week 7 Summary – Results of the Digital Ethnography

On completion of my ethnographic study, I have uncovered, in one particular discussion thread of my chosen MOOC, a certain degree of ‘shared value’ –  a key component of online community, stipulated by Mark Wills’ in his TEDx speech. This was evident in the commonality and repetitive nature of the language being used throughout the thread, from post to post. The visual results of this are shown in the word cloud in the attached ThingLink, along with commentary of the methodology and results.

Some key reflections:- In carrying out this study was frustrated by the fact that I encountered far more limitation in the digital ethnography than anticipated. Firstly, finding a MOOC discussion forum that engendered enough dialogue to allow for a study to take place was perhaps the biggest challenge. And once this was finally done, being able to immerse myself fully into the MOOC discussion was not always possible due to time constraints – as such I found myself acting as a passive observer, rather than an active participant. Furthermore, the qualitative nature of the results made it difficult to analyse and draw precise conclusions. This is likely due to the fact that the study was small in nature. Had this been scaled up and carried out over a longer period of time, a clearer perspective of community and shared values could have been extrapolated from the results. Consequently, I do not feel that the results of my ethnography give a true representation of the community culture that exists within the MOOC forum, but merely a tiny fragment of what it may be.