When it comes to learning, there’s nothing as important as motivation. I want to share with you some of my students’ comments on the impact of authentic projects using real data.
If you’d asked me what I thought I was capable of 6 months ago, the answer would be very different to now. From just one single project that really pushed the limits of what I could achieve, I have done so much more than what I thought I’d ever do. When provided with potential gigabytes of raw data, and the task to interpret it all, one’s abilities are truly pushed, and a level of understanding of how to algorithmically dissect data and create intelligent tools to make discoveries presents itself as an opportunity to learn and grow.
What blows my mind, and I think most others, is that in the presence of a huge magnitude of information – that could not possibly be conceived by the human brain alone – a high school student with a few lines of python can interpret such immense data and create something innovative and frankly, wondrous.
I found this course was very interesting as we were able to analyse the data from real data sets. This is in stark contrast to what a friend of mine is doing at his school, which is textbook coding. I found that being able to apply things we learned during class to real data immediately helped me understand how the code worked.
— anonymous survey feedback
Authentic projects, with real, measurable impact, are accessible from primary school onwards. We tell kids to be the change they want to see, but then add “But not yet. You’re not old enough to effect change yet.”
But kids can measure, analyse, and change their environment, and their community, easily. How about collecting rubbish from the school playground and examining where the worst of the rubbish comes from? Is it clingwrap from school lunches, or the wrapper off a particular item from the canteen? How could we develop strategies to change that? Nude food days, or changing the food available at the canteen? Give kids the challenge to impact their own environment and they invest in the outcome – as well as the data science techniques they needed to get there: collecting the data (picking up and sorting the rubbish, recording the type of each piece), analysing the data (working out what the most common items are), visualising the data (presenting a graph, or more sophisticated visualisation), and then solving the problem.
Biology and Enviroscience? What about doing a bird or bug survey and look at their preferred habitats and food sources.
Social Studies & Politics? Let’s look at voting patterns in different areas – you can actually download vote data from the Australian Electoral Commission Website, or you can use existing analyses from organisations like the ABC. (Or this one, which is really cool and interactive.)
You want to explore it in class? There’s a dataset to make it possible. ADSEI’s mission is to support the teaching and learning of Data Science in schools, so if you want help making this happen in your school, contact us at firstname.lastname@example.org today.