This page indicates my teaching philosophy.
My teaching takes to heart what I learned from Greg Wilson, that teaching is much more about motivating students than it is knowledge transfer. Students have access to any information they want through the internet, but a classroom environment has the powerful advantage of the presence of peers and a knowledgeable instructor. I take a semi-spontaneous approach to teaching, as inspired by improv comedy – I have a rough guideline that I abide to, but am not afraid to deviate from this where necessary, responding to how the class is feeling about a topic. I use various methods to keep constant awareness of how the class is feeling, through simple methods such as asking questions, to more modern active learning strategies such as think-pair-share, which leverages the knowledge of peers.
I believe in quality over quantity, and this is especially important for a program as time-sensitive as the Master of Data Science program at UBC that I currently teach in. Here, I focus on fundamental concepts, asking the question “what must the students absolutely know by the end of this course?” To help answer this, I look to fundamental concepts as they relate to applications, not necessarily how they are developed in academia. I stick to these core concepts, and show students just how far they can go by exploring deeper concepts and data science methods – again, going back to motivation over knowledge transfer.
I believe that teaching is far less effective when done “in a vacuum”, as opposed to collaboratively with the input and feedback from peers. I’m lucky to be involved with open and communicative colleagues who can share their input, to build world-class courses in data science. And I’m happy to be on the other end as well, providing input and feedback to my colleagues.