Assessment is all down to this simple number. Objectivity guaranteed. But if there is a correlation between socioeconomic status… if girls are driven out of particular subjects by the perception that they are not suited to them… if rural kids don’t have access to the same range of subjects… if some schools don’t have great teachers or support structures… then what we have is the pretence of objectivity and fairness, rather than actual objectivity and fairness.
It doesn’t matter what technology you teach, when you’re teaching Data Science. I don’t care whether you use Python, R, spreadsheets, or stacking blocks to make graphs and analyse your data. What matters, above all else, is that you teach your students to ask critical questions about the data. How was it collected? What are the definitions you used? How do we know the definitions are valid? What other definitions could we use, and how would that change the data?
This is a great chat about critical thinking and the importance of scepticism in Data Science, and the importance of data and scientific literacy around the world. Check it out!
Whether hardware or software, smartphone or car, laptop or microwave? Have you ever looked at a piece of technology and thought "This is a gloriously beautiful thing that does exactly what I need it to do, and brings me great joy?"
An amazing conversation with Neuroscientist Associate Professor Nic Price from Monash University, who has a lot to say about the way we teach science, how we can understand the brain, and how we need to get comfortable with uncertainty. Check it out!