Task 9:
What a fascinating exploration of data and the possible implications of these networks of connection between text technologies. Dr Leah McFadyen and her work have forever influenced my perspective on data analytics and graphs. In several of her highly cited works, she discusses how much of the data gathered by learning management systems (LMS) is very difficult to extract value from, but easy to make erroneous inferences from (Macfadyen & Dawson, 2012). Examples include log data and dwell time. How many times a student logs in and how long a module page is open tells you very little about what the student is actually doing at their computer (Macfadyen & Dawson, 2012). One might infer they are better students, or perhaps suffer from a little ADHD. Such thoughts percolated through my mind as I viewed the graph generated from the music curation data.
I would have expected a much more even spread across the tracks in terms of popularity and nodal links. The three most linked tracks seem to have minimal connection to one another, and, surprisingly, several tracks, such as track 16 and track 7, have so few links. It carries implications about these tracks, perhaps suggesting they are unsavoury or generic compared to the others, but without knowing the curators’ rationales, we can only make guesses.
I think demographic (Age, Education, Cultural, Religious, Ethnicity) data would be beneficial and add a layer of potential rationale to the choices, providing more ground on which to stand when trying to identify meaningful connections. However, ultimately, I think greater questions are worth being asked about this data and the decisions that were made. The music selection on the golden record would not be out of place in a shopping mall or an elevator; my point being that I doubt many of the curators had any familiarity with the music before the assignment and would not choose to listen to it in their private lives. The list itself is from a time before many of the curators were adults, pre-internet, and pre-apps such as Spotify and Apple Music. Here, for example, are my most listened to artists of 2025 in the last 6 months:
They bear no similarity to any of the music presented in the curation process. At least, if they put a metalcore screamo track on the Voyager satellite, they hide that information. This brings me back to my musings on Leah Macfadyen’s analytics lens. Does information about the curation process matter so much if the subject matter was irrelevant to the curators themselves? Demographic data may then be frivolous. What might be a better angle to take? Perhaps ask how media-related algorithms have shaped the curator’s perspective on the tracks, and whether there are any relations or connections along that line of thinking? As algorithms bear such a presence on how many consume technology-centric media (Code.org, 2017) in our internet-mediated society, it is hard to separate these variables from one another. What an excellent exercise! I hope these ramblings present a semi-coherent narrative of my exploration of the data.
References
Code.org. (2017, June 13). The Internet: How search worksLinks to an external site. [Video]. YouTube.
Leah P. Macfadyen, & Shane Dawson. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Journal of Educational Technology & Society, 15(3), 149–163. http://www.jstor.org/stable/jeductechsoci.15.3.149
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