Go to Source
I was a music major as an undergraduate. BFA, Music (vocal performance), Marshall University, 1997. One of my great loves is teaching music theory. I taught a little even as an undergrad, substituting when professors were away or guest teaching about indigenous Japanese music when we studied ethnomusicology. (When the time came to apply to graduate school, I still hadn’t decided what I wanted to be when I grew up. In addition to applying to the Instructional Psychology and Technology program at BYU, I also applied to the ethnomusicology programs at University of Tokyo and University of Hawaii.) One of these days, after I’ve retired, I’m going to write a radically restructured, OER-based freshman music theory course, based on research I did early in my career and what I’ve learned about good teaching since then. (Theory is generally taught as a weed out course to scare non-musicians out of the program, rather than as the solid grounding in the technical aspects of music that every musician needs to have.)
All of that to say – music is a metaphor for many things in life to me.
Something has always stuck out to me about the freshmen music theory course. Each year in the US, we turn out thousands of students who have learned the rules Bach used to write four part chorales. The rules are relatively straightforward. But even though we train thousands of students, year after year, we haven’t seen another Bach. There was apparently something more going on in his composing than just slavish devotion to the part writing rules he discovered. Those rules were mixed with something deeply human.
Rules can be applied by machines. Consequently, machines can compose music in the style of Bach. If you didn’t play with the recent Bach-inspired Google Doodle, you should – it’s a lot of fun, and an amazing piece of data-driven technology:
Amazing as it is, the Google Doodle music isn’t much better than the music you find in the homework assignments of freshmen each year. Yes, it’s technically solid in its application of the rules, but it’s generally soulless. It doesn’t move you. It’s all science and no art.
And of course the opposite exists – music that commits to the art but eschews the science. A night at karaoke will reveal many people who pour their whole souls into singing, but who never spent time practicing and developing their technique – that is, they never learned the science of singing. This kind of music is generally not that pleasant to listen to, though it is admittedly a lot of fun to make.
I will pause here to admit that yes, the art-science divide is a bit dodgy. There is an art to doing great science, and yes, there is a science to doing great art. It’s a bit of a false dichotomy. But stick with me – that’s kind of my point.
There’s a debate going on in education that seems to be growing almost by the week. It’s a partisan divide over the role of data in education. To caricature both sides of the debate (which they seem happy to do to each other), on the one side you have those who are represented as believing that if we just provide the right algorithms with enough data, we can solve all of education’s problems. Importantly, we can apply the things we learn from data to eliminate the faculty role and fully automate education, thus allowing technology-mediated, data-driven educational opportunities to scale cheaply and quickly to everyone around the world. On the other side of the divide are those who are represented as believing that educational data is a plague, and that contact with it should be avoided at all costs. We should never collect it, we should certainly never analyze it, and above all we should never make teaching decisions based on it. Rather than building education around what we “learn from data” (air quotes), we need to center education on humans and the relationships of care and support that can exist between them.
I don’t know of any person whose beliefs are so extreme as to actually match up with either of these caricatures, but it does feel to me like there’s a migration toward one or the other of these extreme views by a growing number of people.
The point I hoped to make in this post (yes, there was a point) is that education is like music – in the same way that inspiring music is a combination of art and science, inspiring teaching is a combination of art and science as well. A course that implements the relevant learning science but is completely missing a relationship with a caring faculty member (looking at you, MOOCs) is going to fall short of its potential. Likewise, course taught by a teacher who loves the students and loves the discipline but has no technical training in pedagogy and isn’t capable of doing their own research with their own data is going to fall short of its potential. A truly great course will be based on relevant research, deeply rooted in relationships of trust, care, and support, and continuously improved through analysis of local data. The choice between a research-informed, data-driven classroom and a classroom centered on relationships of care and support is a choice we don’t have to make. We can choose to have both as we work to find new ways to synergistically combine the art and the science of teaching.