Network Assignment

I found this assignment to be interesting primarily for two reasons. The implication of grouped or networked “hard data” is much more nuanced and complicated than meets the eye. Additionally it brings up conversations about assessment and the inherited value we place on systems that do not reflect 21st century learning outcomes.

I have a degree in Jazz Studies as well as a Bachelor of Education in Secondary Music. In both of these degrees, I took courses that explored the history of Western music from a compositional, pedagogical and critical lens and I feel like I have a reasonably grounded argument for my choices that have been informed by dominant forms of music education as well as courses on Indigenous representation. In truth, and without trying to be crass, the face-value function of assignment 8 is nothing more than a personal favourites list. The hard work was done in the initial Gold Record curation and we have, essentially, been tasked with picking our favourites – which is essentially arbitrary when it comes to exploring the meaning behind our choices. The graphs display what songs were most popular and who picked certain songs. What the graphs do not show are the reasoning behind the choices. If this assignment was given in a music or art history course where social, colonial, and pedagogical implications are explored, the data would be really interesting as there would be an established baseline of understanding. Now I understand that this assignment is trying to dive deeper into the language of data collection, however, it is difficult (for me anyway) to glean a truly meaningful network from this sample. One way I tried to find a broader scope of meaning was to analyze the textual data from each individual website and see if there was any connection. I tried this for one of the songs that I shared with three other people. When looking at their reasoning, I found that the justification was about as vague as the networked graph that connected us. I say this because in order to find purpose and justification we have to define the terms that were used in their reasoning: “cultural importance”, “harmonious”, “style”, and “philosophical elements”. As you can see, without having common definitions of these terms, the justification behind a choice is chalked up to personal preference. Which is fine in and of itself, however, for purposes of data collection or in the event of actually trying to preserve global representations of music, we would have to have a very robust and detailed rubric by which we make our choices. The epistemological and semiotic reasoning behind an individual’s choice goes far beyond the “hard data” that was collected.

This brings me to my second point which is the emphasis that education systems have on quantitative data. For whatever reason, blame the Enlightenment if you want, the education system is yolked to quantitative assessment. To the point that the most difficult part of my UBC Music Education degree was the assessment course. It was extremely detailed and prescriptive because of the notion that music and art is subjective. And while I appreciate the efforts to “have music keep up with the ‘important’ subjects” by creating detailed quantitative assessment rubrics, I also think that quantitative assessment in education overall has a very small part to play in a students learning journey. This is an example of a subject discipline striving to compete in a system that rewards certain types of knowledge based on how “easy” it is to mark. Math, writing, and reading from a primary and secondary perspective, are rather “easy” to assess given that there are unquestioned cultural frameworks of how to do it “right”. Now, I do not think that seven year olds need to be introduced to critical literacy lessons or alternative math – they do need to know how to spell wurds ryte! In this vein, I do think that graphs and percentages can come in handy when servicing deeper learning. For example, referencing a graph of how students did on certain assignments to improve future iterations of the assignment. However, many education and government systems have somehow come to think that a percentage is synonymous with feedback. Two texts that I have come to appreciate when describing learning goals and outcomes is the New London Group’s Pedagogy of Multiliteracies (1996) and Baviskar, Hartle, & Whitney’s Essential Criteria to Characterize Constructivist Teaching (2009). In both of these papers, the function of education is multi-faceted and centered around creating experiential learning opportunities for a variety of “lifeworlds” that include meaningful feedback (1996). 

Now, I do want to reiterate that I am not opposed to quantitative data. There are many fantastic and necessary applications that quantitative methods have in education, research, and feedback. In fact, there is seminal quantitative data that shows how learning how to read and play music in a large group context is directly correlated to greater success in math and language courses (2019). This was measured quantitatively! However, especially when it comes to policy debates or important conversations around government positions, I often see percentages and graphs thrown around as a definitive and authoritative representations of desire, need, aesthetic, and want. Not often is there a conversational exploration of how the data was gathered or how the questions were asked – the “facts” are simply presented.

Baviskar 1, S. N., Hartle, R. T., & Whitney, T. (2009). Essential criteria to characterize constructivist teaching: Derived from a review of the literature and applied to five constructivist‐teaching method articles. International Journal of Science Education, 31(4), 541-550.

Cazden, C., Cope, B., Fairclough, N., Gee, J., Kalantzis, M., Kress, G., … & Nakata, M. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard educational review, 66(1), 60-92.