One thing many of us working in statistics and data literacy can agree on is the broken pedagogy and misalignment between maths and the existing teaching curriculum.
Now, because of COVID-19 we are taking that broken foundational model and moving it to remote learning--what could go wrong?
When I teach underlying mathematical principles in statistical or data science course I am leap-frogging over the memorization and boring bits and moving right to the application. Perhaps not ideal, but if the goal is to teach a team how to reach the part of the workflow where they can begin to curate insights from their data--a few corners are going to need to be cut.
Here is the rub though. They often learn more in the over-simplification because they never knew what they were doing down in the weeds anyway. For example, when you are data modeling--what is the shape of your data? We talk about linear, sinusoidal, or quadratic relationships. I write about it briefly in this blog--Maths in the real world.
We all have heard the lamenting about why take calculus. “When am I ever going to use it?” Did you know derivatives can tell you a lot of information in the real world? How about whenever you think about rate of change of a function? Most recently while calculating the COVID-19 rate of positive tests for example. Also when we think of population growth in biology or marginal functions in economics.
I like to introduce the brilliance of maths that we can stand back and marvel or appreciate. Recently, a post On apple trees and man described Benford’s Law. Discovering the not so random nature of big data provides a glimpse of the complexity but also mystery of math. A look beyond the rote memorization introduction that led many of us to avoid math simply out of principle.
The quote below is from an informative discussion about online-instruction and how we need to Teach Better.
Anyway. the key thing there is that the relevance has to be there for people to engage, and we also have to think about how do you kind of shape knowledge in the discipline? You know, how does a novice look at things? And chemistry is a great example because when you're a chemist, you get good at dealing symbols.
I think the problem with symbols and not knowing the storytelling of their shorthand stops so many of us in our tracks. If you are integrating classroom response systems or “clickers” where you can respond to student gaps and questions in real time you can avoid the tendency to gloss over esoteric terms and abbreviations and mistakingly assume that all students are joining you on the journey.
Online workshops and webinars have taught me that we can’t do any of it in a meaningful way without engagement. Here is an article, The Classroom Observation Protocol for Undergraduate STEM (COPUS): A New Instrument to Characterize University STEM Classroom Practices. I use it as a model for teaching technical topics remotely. I hope you will steal these ideas to make your work more engaging.
Here is the podcast episode where they provide a bit more context to the work being done in STEM specifically in Chemistry but you can easily connect the ideas to how we our teaching statistics for example.