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?
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?"
When kids see STEM skills as tools they can use to change the world, we're both empowering them to create positive change in their communities, and putting them in the drivers seat of the future. This is how we shift the numbers. This is how we get real diversity into STEM. And, incidentally, how we create critical, creative thinkers who can solve real problems.
"The first thing I think [about graphs] is: what story am I supposed to believe when I see this? What are they trying to make me think? And then immediately, once you understand 'oh they want me to think that it's really big, or it's going down really quick, this pattern is abnormal…' then I immediately think 'what other stories also fit that data?'"