Initially, I found the Palladio app difficult to navigate and challenging to understand. However, the video tutorial on how to use the tool was very helpful. The networks felt a bit overwhelming at first, but after experimenting with various filters, I was able to make sense of the data and identify patterns.
I was assigned to group 3, which included 6 other peers, making it the largest of the 4 communities the app created. After reviewing the posts of my peers in this group. I would assume that we were grouped together because most of us included diverse geographical and cultural representation in our selection criteria.
It’s important to note that my assumption was made after reviewing my peers’ posts. The graphs shown in the app indicated that we made similar musical selections. However, I had to dig further and review the posts to understand the criteria others used. I believe a potential misunderstanding of the community grouping is assuming that everyone in the group uses the exact same criteria. We had similar criteria, but there were some differences and nuances to each of our criteria. This exemplifies the importance of reviewing multiple data sources, quantitative and qualitative when possible, before making assumptions.
Lastly, another interesting pattern I was able to identify was that even when looking at the entire class selection.The most popular tracks, when compiled, also create a geographically diverse curation. As shown in the image below, the 10 most popular tracks feature musical representation from Germany (2 songs), Java, Peru (2 songs), Mexico, Australia, New Guinea, India, and China.
The image above displays the most popular tracks. The larger the dot, the more popular the track was.