I started off by ordering the tracks from 1-27 in the center so that I could see the connections between the different respondents. However, I soon realized that there were too many nodes, so I began playing around with the groupings.
(Palladio, 2024)
The first group had five people, myself included. I then noticed that I was the only one who picked Track 21, so I assumed this grouping was made based on the number of similarities we had. I still found this too complicated to analyze so I jumped to group 4 where there were only 2 respondents.
For group 4, it looks like Brie and Shannon picked 5 of the same tracks.
Working backwards, I then looked at group 5, which had 3 respondents. The three of them picked 4 of the same tracks. Katy and Robyn picked 2 of the same, Katy and Duncan picked 2 others of the same, while Duncan and Robyn picked one similar track.
For group 0, the 4 respondents picked 2 of the same tracks. (I lost the screenshot for this.) I was starting to see a pattern so I decided to tackle group 1 again.
This time, I was able to see that I picked 4 of the same tracks as Stephen, 6 of the same tracks as Warren, 4 of the same tracks as Jennie, and 2 of the same tracks as Lachelle, but all 5 of us did not share any similar tracks.
What does this mean? Well, this may suggest that Warren and I had similar music tastes for task 8. However, it does not take into account WHY we chose 6 of the same tracks. It could be that we ended up picking these tracks for completely different reasons. I picked a few of my tracks based on familiarity and nostalgia because I used to play the violin and had heard these orchestral tracks before. Perhaps Warren had a similar musical background or exposure that influenced his choices. Maybe we both ended up picking these tracks through a process of elimination because we weren’t familiar with the other tracks so we picked these ones by default. None of these dots and lines display the thought process behind these decisions.
On the other hand, for some reason, Duncan and I had 4 similar tracks but we were put in different groups. Why were my connections with Lachelle stronger even though we only had 2 similar tracks? What was the reasoning behind these groupings? I couldn’t quite figure this out. What if Duncan and I had similar reasons for our choices? How did these visualizations take this into account?
This is what is frustrating when you reduce individuals and their thought processes to mere nodes and visualize relationships through node connectivity. For example, algorithms predict your preferences and interests based on your searches and clicks. However, they can sometimes misinterpret your interests. When I scroll through different posts on Instagram, sometimes I stop to watch a video because curiosity got the better of me and I wanted to see what would happen in the video. But this does not mean that I have a keen interest in that particular topic. Sometimes, it is on a topic that I dislike but I would still like to see what happens. After stopping to watch that one video, the algorithm then bombards me with similar videos because it has decided that I enjoy the content.
Similarly with this activity, we were asked to choose 10 tracks from a list of 27. The results are inconclusive because it was based on various reasons. I cannot conclude that Warren and I are the most similar out of the entire class just based on the data. This is really important in education and really important to teach to students when we look at digital literacy because it is essential for students to look at data critically and evaluate information online. We should not look at data at a surface level and accept these relationships without considering the implications. Content on the web can be at times a form of confirmation bias, prioritizing certain information over others and visualizing certain information in a specific way, oftentimes ignoring minority groups and their opinions. We need to ensure that students are aware of the political implications and consider the information that has been left out of mainstream media.
References
Code.org. (2017, June 13). The Internet: How search works. [Video]. YouTube.
Systems Innovation. (2015, April 19). Network connections. [Video]. YouTube.
Palladio. (2024). GR_S_2024.json [Screenshots]. https://hdlab.stanford.edu/palladio-app/#/visualization