Skip to main content

What Data Can’t Do

I came across this fascinating article from the The New Yorker that really speaks volumes about how careful we have to be when it comes to using and visualizing data. Whenever you try to force the real world to do something that can be counted, unintended consequences abound. The COVID pandemic demonstrated just how vulnerable the world can be when you don’t have good statistics, and the US Presidential election filled our newspapers with polls and projections, all meant to slake our thirst for insight. The same applies to education where knowing what to measure, but also why you want to measure it, is the primary hurdle to tackle. We all have a tendency to naturally trust data as it aims to represent something we are observing. However there are times when simply even solid data is not enough for decision making. That’s why the context, the aim, and the balance between quantitative and qualitative data is so important. As the article states: “The great psychologist Daniel Kahneman, who, in his book “Thinking Fast and Slow,” explained that, when faced with a difficult question, we have a habit of swapping it for an easy one, often without noticing that we’ve done so. There are echoes of this in the questions that society aims to answer using data, with a well-known example concerning schools. We might be interested in whether our children are getting a good education, but it’s very hard to pin down exactly what we mean by “good.” Instead, we tend to ask a related and easier question: How well do students perform when examined on some corpus of fact? And so we get the much lamented “teach to the test” syndrome.” You can read this fascinating article in full here. 


Comments

Popular posts from this blog

From Mission to Metrics

Last week a new LAC school presented me with an interesting question: “ How do we know we’re meeting our mission? ”. It’s the kind of question that we often ask ourselves at accreditation season, but how many schools can truly answer with confidence and evidence? The more I thought about it, the more I realised that unpacking this question is no different to any other data dive we might do. It requires us to understand what we’re measuring, to find a range of data to analyse, and then to use all that evidence to gain a deeper and more holistic understanding of our current situation and future goals.    Step 1: Translating values into visible behaviours What does our mission look like in action? When we are living our mission, our values align with our actions. Let’s take “lifelong learning” as an example phrase we often see in mission statements. Schools that value lifelong learning will likely have administrators that promote and encourage staff professional development...

The End of Year Data Handover

“And then she went to the porridge of the Little Wee Bear, and tasted it, and that was neither too hot nor too cold, but just right, and she liked it so well that she ate it all up, every bit!” — Goldilocks and the Three Bears    For many of us, the sun is shining and we are in the mad dash of wrapping things up before escaping for a well deserved summer. I suspect it would be an easy task if wrapping up was all we had to do, but of course it’s never that simple in a school. We don’t just pack up our class, we need to hand them over to next year’s division and teachers, and get ready to receive our next batch. The data handover is enormous, and figuring out the what and how requires a “Goldilocks” attitude - we’ve got to get it just right. Quantity: Sharing enough to inform, but not so much that it will overwhelm next year’s teachers.  Ingredients: Not sharing just numbers, but also anecdotal records and holistic data that will help teachers know students better and so...

Growing Data Champions

In my work with schools, I’m always on the look out for a school’s data champions: the early adopters of a culture where data is valued and is used to improve schools and student outcomes. Data champions help colleagues understand how to find, interpret, and use data effectively. They are also translators, able to turn complex findings into clear and actionable insights.  Image by  Mohamed Hassan  form  PxHere  - CC0 Public Domain We often go looking for data champions in the IT office, or failing that, in the math department, but the truth is that data champions are hiding in plain sight everywhere; anyone who believes in using data to inform choices, and who can convince others of the value of data, has the potential to become a champion.  So how do we find and grow these “sleeping champions”? Jim Collins share strategies for building “enduring greatness by cultivating a talent pipeline”. In a data context, this could include: Modeling data-driven d...