I see you, I hear you, I acknowledge you.
I’ve struggled to come up with a unique analysis of our Tweetorial in #mscedc. I am unsure of how to take the data presented in the archive and transform it into some sort of academic critique. Besides providing us with quantitative data (top users, top words, top URLs, source of tweets, language, volume over time, user mentions, hashtags, images and influencer index), what meaning do these numbers give us and what is the significance? Did we, as a class, create any broader connections to each other or to relevant academic work from our participation in the Tweetorial? If we did create connections and relationships through our intensive two days of tweeting, then what can we glean from these connections and relationships and what is the meaning and value of them (Eynon 2013)?
In our Hangouts tutorial on March 21, I mentioned my love for ‘liking’ tweets and how this miniscule effort of seemingly passive participation, albeit small and arguably insignificant, is important to me because it is my way of letting my colleagues know that I see them, I hear them and I acknowledge their efforts and contributions to the Tweetorial event. It is so easy to simply click the heart and ‘like’ a tweet, but I really feel that by doing so, others will (perhaps) feel validated and – dare I say – more confident to keep contributing. I also believe that ‘liking’ provides a sense of belonging for both me and for those whose tweets I like.
Through our ‘data trails’, we did seem to connect through strings of tweets – of 140 character digital conversations that created relationships between classmates, professors and outsiders, and that encouraged and produced learning (Siemens 2013). Our conversations directed us to articles, images and to our own EDC lifestream blogs. I took some time to review my classmates’ Tweetorial analysis posts, and have collected their posts here:
Eynon, R. (2013) The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology. 237-240.
In preparation for our collaborative video, I’ve spent some time digitally editing our musical selection (Zoe Keating’s Optimist) down to approximately one minute. Listen to the original unedited track here.
I was so grateful to be able to have the time to participate in today’s EDC Hangouts tutorial. It was great to engage in discussion with Jeremy, James and my classmates – and despite having the flu, I’m thankful to have the time off of work to join the video call!
from http://twitter.com/ notwithabrush
via IFTTT http://ift.tt/2mK2H8r http://twitter.com/notwithabrush/status/844015724595216385
— Anne M A Powers (@notwithabrush) March 21, 2017
More algorithmic exploration
While fooling around on Netflix, I thought I would see what would happen if I chose to watch a film from the ‘horror’ genre – of which I would never usually choose as I detest this genre!
The first movie in this genre on the list was: The Last Days On Mars, which I watched a portion of and was relieved to discover it was not that scary. Because I chose a film that was out of my normal realm of preferred viewing (crime drama, comedies, foreign films and documentaries), how will this affect my future Netflix recommendations, if at all?
The next Netflix algorithm at work became apparent from the notifications section where Netflix recommended ‘top picks’ for me: Friends, Chef’s Table and Transformers.
Netflix also provided me with recommendations based on my past viewing habits, as seen in the following photo:
And finally, Netflix also recommended “Movies & TV from the 80s” (as I posted HERE). It seems to know when I was born or (at least) which era I grew up in. This is a collection of Netflix’s recommendations for me: Movies & TV from the 1980s. Admittedly, I was excited to watch Jem and the Holograms again!
After reading Siemens (2013) article, I was enlightened about Google’s ‘knowledge graph’ as an example of “articulating and tracing the connectedness of knowledge” (p.1389). Since I was eating a bowl of raspberries, I tried searching for “amount of calories in 10 raspberries” and came up with the following results on Google’s knowledge graph:
Google’s knowledge graph provided me with a wealth of information on raspberries and cited sources from Wikipedia and the USDA. Although the information is useful and can be obtained without going beyond the initial Google search, why does Google obtain its source information from only Wikipedia and the USDA? Are there other sources that are not listed? Why does the algorithm work this way – what is happening behind the scenes that I’m not seeing?
Since Google is so pervasive and, I dare say, educators and students alike use it to perform a magnitude of daily internet searches, should we question how the information being presented to us is gathered or blindly trust Google as an institution in the search engine field?
Moreover, I checked my topics on my Google+ profile and found it interesting to see the results (as seen in the photo below):
As it says, “these topics are derived from your activity on Google sites…” It is interesting to note that I had to manually add in the topics of ‘Anthropology” and “Archaeology” because I have a causal interest in these areas and I felt like Google might cheat me out of potential fascinating web content if I didn’t add them to my list. I also (embarrassingly) felt a little hurt that Google didn’t automatically recognize that anthropology and archaeology are interests of mine. Am I disappointed in the algorithm’s performance? It seems ridiculous for me to have feelings towards this since I’m talking about a machine who is simply “running complex mathematical formulae“, but despite this, I was affected. Is this what Knox (2015) is talking about in reference to the “co-constitutive relations between humans and nonhumans?”
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 3 Summary: Jan. 30 – Feb. 5
This week was dedicated to the following three things:
- Recording video footage for my visual artefact: I decided to record clips of ‘happenings’ from around my own house – the coffee grinder, piano, laptop computer, chandelier, etc. I wanted to depict everyday things that mix the ‘technology’ with the ‘human’. I posted a rather ‘unedited’ planning document HERE, before taking the footage to begin the long process of creating and editing it on Final Cut Pro into a video for my visual artefact.
- Postings: I engaged in conversations on Twitter commenting on others’ visual artefacts and tweeted my own artefact HERE. I also blogged about figure skating prosthetics and cyborgs HERE, and reflected on transhumanist views in relation to an exciting initiative called ‘New Dimensions of Testimony’ using Bayne (2014) and Miller (2011) HERE. Finally, I posted a picture of Holocaust survivor, Pinchas Gutter HERE, and more on ‘New Dimensions of Testimony’ HERE and HERE.
- Visual Artefact: This week, I posted my visual artefact – a few times from a few different sources – but the final artefact can be found HERE, with comments from my classmates. I was proud of how my artefact turned out; I wanted to portray a feeling of anxiousness and of monotony – like a drone machine trying to struggle through life as a human or machine (or a cyborg, perhaps?). I enjoyed mixing the tech sounds and images with the human (breathing, heartbeat sounds) and think it was an effective way to combine human and tech.
As I usually do, I realise I probably spent entirely too much time creating my visual artefact, but I did find it to be a worthwhile project – a great way to end week 3!