Learning to Begin: Part I
My PhD advisor, Dr. Bob Duke, is one of the smartest humans I’ve ever met. He understands human learning with a depth that is humbling and inspiring. Of the hundreds of hours we’ve spent together during my PhD, a few stand out as the clearest portrayal of his capacity for expertise:
There are a few of us in the Music and Human Learning department at UT that use eye-tracking data to explore the cognitive processes that drive music learning and music teaching. Having millisecond to millisecond information about how musicians attend to their musical environment gives some of the most reliable insight into how they understand musical problems and how they gather information from the world to solve them. But, to get reliable data, we’ve needed to overcome a few technical hiccups while learning to navigate some finicky hardware and software.
Team Eyeballs: Dr. Lorelei Batislaong, Dr. Marjorie Yankeelov, Dr. Laura Hicken, and Robin Heinsen (I’m behind the camera)
Learning how to fit and calibrate the glasses to the gaze of participants is hard work. Software crashes and failed attempts at getting reliable data were the norm. Being a beginner with this gear was difficult for me.
Bob handles this differently. Every time I get frustrated, he gets even more curious. When I bury my face in my hand, he scrunches his eyebrows in an affable sense of wonder with a thoughtful, “huh”. This is usually followed by a very brief pause, and then “I wonder what happens if we…”, or “have you tried…”. He views failure as a curiosity. Having something go wrong means that he’s closer to understanding how to make it go right. Expert learners learn like Bob. They do so, not because they’re better at succeeding, but because they are better at failing.
Being good at failing is characterized by the quantity and quality of data gathered, and the value of the any resulting changes. Curiosity is better understood as an open-mindedness to feedback. As I once heard the Dalai Llama refer to it: a state of “wow”. This state helps to optimize the flow of data coming in. Emotion can help to weight feedback, but curiosity can help to ensure that the weighing of various data is fair and, if not objective, at least consistent. An open minded gate keeper will let all sorts of data in and let the bookkeeping processes in the perceptual-motor system do all the number crunching about which data is good, which is bad, and which is useless.
Expert beginners act with fearlessness, tenacity, and most importantly, curiosity. An inquisitive learner will be more successful exploring the landscape for solutions and adaptations. This openness to new experiences affords learners at every level more data about what they’re learning. More new experiences offer more feedback, which affords better learning.
Not all students are curious all the time. But if we accept that our classrooms and our culture need more expert learners, these are characteristics we should be prioritizing in our students. For the young students that come to us with these, it is precious cargo to be protected and propagated. Older students that are out of practice being curious, tenacious, and fearless may require the harder job of inspiring.
What can you do today to help your students learn expertly? How can you model the best characteristics of good learners? How can you help them be more fearless, tenacious, and curious?
