Week 10 Summary

This is a very belated week 10 summary as week 10 itself (16th-20th March) was a blur of writing and running workshops, training academic staff on how to deliver their subjects online, trying to un-buy a house and generally keep safe and prepare for the Coronavirus’ descent on Sydney.

So apart from the Google Hangout meeting we had, I barely engaged with the course in a direct way and didn’t record much on the lifestream apart from a comment on Sean’s comment on my artefact.

I also managed to upload the 2nd half of the podcast on Algorithmic Cultures. And then finally ot around to writing my summary for week 9.

In all honesty, it has been hard to maintain motivation with everything else going on and a great deal of visceral uncertainty and tension in the atmosphere.

This has been significantly ameliorated over the last couple of days by us being able to find a place of our own to stay in. We had been housesitting for the last 16 months, but that all ground to a halt very suddenly with the pandemic preventing travel.  Now, we’re settled in a what would usually be a holiday flat in South West Rocks NSW.

As we had only a matter of days to find accommodation, and Sydney’s rental market is absurdly difficult to get into, we contacted an agent here in SWR and they said they’d be happy to rent to us for a much reduced price. Renee’s family are from this area so, it’s familiar to us both and a kind of second home already.  With that decided, we jumped in a borrowed and rusty Subaru and drove the 6 hours to get here.

Working from home means that the key consideration was having a wifi connection. Getting this going was a costly exercise but the easiest choice I could think of which was a portable Wifi Modem.

Now, what does any of this have to do with Digital Cultures and more specifically this lifestream blog?

Well it’s clear that I’m in a very privileged position of being able to simply pack up and leave and still have a job while hundreds of thousands of others in my city are out of work indefinitely. Part of this is simply due to the digital nature of my work. I can consult with academics digitally, I can produce work and share it digitally, communications, digital, and the sector my job is devoted to is almost entirely based-upon digital technology.

But this digitality of my work in education belies the fact that for the receivers of this work, the ability to access the digital is not a given. The great assumption of all edtech really is the idea of equity of access.

This is particularly sharp now when the task assigned to many educators is to make their teaching fully digitally available. For many, this simply means turning lectures and tutorials into video-conferences for students to access from home. Home.

That word, that place can be taken for granted so easily. When we say ‘work from home’ or ‘study from home’ , it’s getting harder to ignore the fact that some students may not be able to afford to do that much longer. I suspect this will be the sharpest point for many international students as well, who rely on their casual employment to support themselves and can’t yet access government welfare services. The response of educational institutions will be central. We may come out the other side of this praising the work of digital education forgetting how many students were either completely excluded from studying or who had to give up some basic right to privacy to an EdTech company serving institutions.

I sure hope not.

Week 9 Summary

Started the week replying to a comment left by Matt on my micro-ethnographic artefact about how he finds thinking about the implications of Learning Analytics (LA) often quite depressing.

I replied half-heartedly that there are some beneficial uses of analytics that aren’t reductionist and gave some examples but given that these examples are few and far between in the landscape of LA, Matt’s overall impression is valid.

Following this I posted an example of Algorithmic play that involved looking at my personalisation of my Google ads.  Given that I’ve largely stepped away from Google as a search engine & browser instead moving towards services like Duckduckgo  (a privacy focused browser) . The personalisation was 50/50 accurate and may seem fairly innocuous. However, when that is scaled up for open data commerce, we suddenly see scores of copied profiles in systems that we have no oversight nor power over. They’re profiles that seem like you to some extent: DOB, location, race, hobbies, and even a lot more private information.

On March 9, I first started to notice more stories about the use of mobile data to track the whereabouts of people with COVID-19. It reminded me of the very recent protests in the US from students who didn’t want their location tracked using the university WIFI. Now that seems banal compared to the sudden explosion in extremely invasive Tech in Education from video-conferencing software to automated grading tools exploiting the emergency situation.

Seems like this is the catalyst for far-reaching surveillance-model education.

The article “Is Learning Analytics Synonymous with Learning Surveillance, or Something Completely Different?” looks at LA from several angles and asks some challenging questions. Of course, LA relies upon the collection of big data and the use of algorithms to manipulate and present that data. The big question for me coming out of it is, if we as educators see a problem with this and want to stop it, what happens when students turn around and say “no, I’m paying all this money for my education, I expect you to use every possible means to ensure I pass. That includes using the data you collect to provide feedback on my progress and what I need to do to improve”

If this kind of mentality is prevalent then what choice to institutions have but to respond in order to market themselves?

2 posts on March 10 referencing Kin Lane, husband of Audrey Watters, and his experiences of the changing online algorithmic landscape. One is a comment on the actual behaviour of Google: it’s not about high quality content anymore, it’s all about who you know. The other is a speculative fiction which seems too eerily close to reality: auto-correct AI changing the meaning of your text messages and emails in order to bring them inline with company policy.

The rest of the week was spent getting my Artefact up and running, commenting on my classmates’ artefacts and a few random tweets about doing an image search for MSCEDC and reflecting upon the computer modelling that predicted the COVID-19 pandemic months ago.

 

 

Algorithmic play artefact : teaching@digital podcast:

My algorithmic artefact is hosted on Podbean. There is the podcast itself but also more details of my algorithmic play experience framing this episode as text and screenshots. Would love you comment here or on the Podbean site, cheers:

https://teachingatdigital.podbean.com/e/teachingdigital12-part_1-algorithmic-play-soundcloud-education-and-gaming-elevators/

One thing that has stood out for me in the efforts of platforms to personalise our experience by means of recommender algorithms is how this cultural turn influences what is expected of the services of educational institutions:

“the predefined ‘needs’ of the learner begin to provide the core justifications for education, the role of the educational institution and its teachers becomes merely responsive, one in which the institution exists to supply educational ‘services’ in response to learner demand.” (Knox, Williamson, Bayne, 2020)

In the same way that these ubiquitous platforms become more successful, the more responsive they are to our whims and desires, there is a feeling that for education to “survive” or merely become better, it must be adaptive to what ‘learners’ want.

The assumption being that autonomous ‘learners’ already exist complete as they are, rather than having their learning aspirations shaped by their teachers.

Week 8 Summary

Attempting to play with algorithms

Last week was spent attempting to affect a change in my Soundcloud weekly playlist by liking and playing every track I could find with the title ‘algorithm’.

That’s why there are so many Soundcloud posts currently in the lifestream. I switched off the IFTTT link after a while to avoid flooding the stream as I ended up with more than 100 tracks.

However, perhaps fortunately, it didn’t have great deal of effect. In fact the one effect it did have was that I received a message from Soundcloud telling me to slow down the liking of so many tracks. Weird.

I guess that shows how the algorithm has been played  by users previously.

Besides playing with the algorithm itself, I found it interesting to see just how diverse the material of the songs was. So many different takes on the idea of an algorithm. Of course, there was a preponderance of futurist EDM, but also country, folk, and spoken word. Love songs, songs embracing algorithmic culture, songs protesting it, thrash metal songs predicting a gory future of perpetual war with robots more in line with the fear of cyber culture.

Algorithm Analysis for Big Data in Education Based on Depth Learning

This article highlighted for me how the belief in the organisation and analysis of big data from educational institutions has become systemically accepted. Or at least a primary goal of IT.

Democracy and the Algorithmic Turn

Makes a case for the extent to which algorithm design has become a global force. And shines a light on how these can shape flashpoint events like elections but also become inscribed in the digital mediation of democracy. It reflects what Williamson (2018) proposes regarding the capacity for ‘big data’ to shape policy and for policy to determine what data is collected.

Introna’s  major work on Turnitin

This groundbreaking piece is a sharp analysis of not only Turnitin’s algorithmic design but the impact it has upon the written word itself and the way students write.

Excavating AI

“in which the Internet’s distorted picture of us becomes who we really are.” – but who are we anyway? Do we expect there to be a true self which is freed from the algorithms perception of us. How is that any more authentic and real than the ‘distorted’ picture?

Anatomy of an AI System

A revealing of the materiality of Alogirthms. From the extraction of rare earth materials to the indentured / prison labourers who built Amazon echo devices.

The Prevalence of Algortihms in Education

Pretty banal article detailing all the ways that algorithms are already in schools shaping how teachers, students and institutions behave. But don’t worry, it says, the heart of school is still its human faculty.

So the algorithmic turn is in fact another part of the humanist project.

Simon Denny’s Mine

The exhibition from MONA also brings the brutal reality of data mining and the growth of AI powered products into focus.

Reflectacles

A company selling glasses that befuddle facial recognition technology. We’re opting to subvert the surveillance rather than legislate against it.

Turnitin’s podcast on the written word

Interesting to see the company producing this. Getting on the podcast wagon and occassionaly interviewing some interesting people, though never challenging the assumption that the algorithm is right.

Limitations of algorithmic recommender systems in Soundcloud

  • Based upon tags
  • Based upon musical style
  • Based upon track image
  • Not thematic content or name

This results in more o the same style of music being pushed -to the extent that one would assume the platform only houses one type of music.

Simplistic recommenders that worked just by title might render more obscure and bizarre recommendations – less suited to what the algorithm producers assume to be the way people listen to music.

This produces a way of listening to and thinking about music which reflects commercial interests rather than artistic ones.

Internalises a belief that one’s musical tastes are of a particular flavour fixed and dictated by the algorithms in our digital platforms. Does the algorithm shape our tastes or is it merely an accurate reflection of how our musical tastes take shape. The challenge is not necessarily what the algorithm includes in its recommendations  but rather how it excludes those who don’t adapt to its patterns – If you don’t tag your uploads, the algorithm is unable to process and recommend them as effectively, thus making your work invisible to all except those who seek it out directly.

The artificially social nature of these platforms (many accounts are bots) also heightens the presentation of self in everyday life – One presents the music one likes in order to collect more followers. Resulting in a system where users like and share music that will attract the most followers rather than what they may actually enjoy listening to.

The platforms and entrepreneurs pushing ‘personalisation’ of learning are based upon similar fundamental principles of commerce and advertising. They miss the key problem that education is not advertising, no matter how much they may want it to be. Students are not just another demographic to be catered to. The educational experience cannot be turned into a recommender system.

 

 

Added to Pocket

Is Learning Analytics Synonymous with Learning Surveillance, or Something Completely Different?
https://ift.tt/eA8V8J
It all started off simply enough. Someone saw a usage of analytics that they didn’t like, and thought they should speak up and make sure that this didn’t cross over into Learning Analytics: One correction – #LearningAnalytics IS NOT used for student surveillance.
Is Learning Analytics Synonymous with Learning Surveillance, or Something Completely Different?

Algorithmic play

How my Google ads are personalised

Google Ads - see outline for details

Given that I try to avoid Google searches as much as possible by using Duckduckgo , this is still fairly accurate. I imagine my years of Google use prior to switching the DDG have contirbuted dramatically.

SO yes, I am 34-44, male. Not quite sure what elegant themes are according to google but maybe its simply www.elegantthemes.com/.

The Guardian, yes, I’ve sought out news from The Guardian for years.

Squarespace is a mystery to me.

Menulog makes sense as I have ordered food online before.

American football? Perhaps I’ve followed Colin Kaepernick’s career and others ‘taking a knee’ at some point during the US anthem.

Adventure games: WoW during IDGBL perhaps.

etc. etc.