People often assume that Data Science in Schools has to be secondary school only, because how could primary kids do Data Science? The truth is that Data Literacy and Analysis skills can be built in to the curriculum from as young as 5 years old. And it’s really important that kids learn Data and Tech skills early, because by the time they get to secondary school we’ve already lost a lot of them, believing that these skills are too hard, not relevant to them, or just not interesting. We need to show them early on that Data Science is a useful tool that they are more than capable of mastering.
So how can primary kids do data science? Like any other data science project, it’s crucial to put it in context, so the kids can see the point.
So Step One is: Find a problem the kids care about
It might be litter in the playground, traffic at pickup time (or, to put it in a way kids will really relate to – how long they have to wait to be picked up, or how far they have to walk to the car!), or access to play equipment.
Step Two: Measure the problem
Count and identify the litter, time how long people have to wait to be picked up, measure how far people have to walk to the car, or count the number of people who get to use the monkey bars every lunchtime for a week.
Step Three: Analyse the measurements
For younger kids, that might simply mean sorting the rubbish into categories (eg chip packets, icy pole wrappers from the canteen, and sandwich bags or cling wrap from home), or organising the drop off or play equipment measurements by year level or by day. For older kids you might enter it into a spreadsheet and use a formula to calculate some averages over the week or by area or year level.
Step Four: Communicate your results
This is where you graph or visualise your results. For the littlies they can “graph” the results by stacking up blocks to represent the different categories. Green blocks for chip packets, blue ones for icy pole wrappers, etc. This is a great, tangible, exercise in data representation. Older kids can draw graphs or do them in a spreadsheet like Excel or Google Sheets. It helps to get them to draw pictures and labels on their graphs to make them more interesting and compelling.
Step Five: Propose a solution
Think of a way you might solve the problem. For litter the kids might come up with nude food day campaigns, or a change to the way food is available in the canteen – such as using larger chip packets and handing out small paper bags chips in them, instead of lots of small plastic packets. For traffic it might be that pickup times can be staggered by year levels, or older kids might be encouraged to walk further and be picked up a block or two away.
Step 6: Implement your solution
This can be a whole school initiative, and involves a lot of communication, using the graphs from Step Four to tell the community what’s happening and why.
Step 7: Measure again to see how well it worked
This is my favourite step, often sadly missing from political initiatives. Once you’ve tried to fix something, you need to measure it again to see if you actually made any difference.
You can even repeat steps 3 to 7 with several different solutions to compare which ones work better.
I love this template because it is the essence of STEM – It’s a science experiment, devised by the kids, with rigorous measurement and evaluation. Maths and Technology are used in handling the data, and you can use Engineering to design your solution, or even to measure the problem if you’re looking at environmental conditions like heat, noise, or water and want to use some sensors.
You can scale the technology use up or down depending on available resources and where your students are up to. There are no robots with parts to fail. And the best part is that the motivation is built in. The kids are learning that STEM and Data Science are tools you can use to solve real problems in your community. They’re not just a bit of fun that’s not relevant to their futures.
ADSEI is developing more projects like these over the next year, as well as building a network of teachers interested in sharing their ideas and supporting each other to introduce integrated STEM and Data Science in the classroom. Jump onto the mailing list to stay in touch, and feel free to share your own ideas in the comments on this post!
2 thoughts on “Primary School Data Science Template”
What about finding a problem by collecting data rather than starting with a problem?
This might work if you know what you’re looking for. There’s a risk, though, of falling into the mindset of “we’ll collect all the data and then magically understand it.” Any data analysis has to start with a question: What are you looking to understand about the data? What questions can the data answer and how can you analyse the data to find those answers? Collecting the data doesn’t give you answers, or even identify problems. You have to know what data to collect, which means an understanding of the problem space.