We are always on the lookout for more content to help us build authentic Data Science into the curriculum. Read on for guidelines and advice on how to create content appropriate for education.
If you have interesting content you would like to share with us, ideas for activities or blogs, or want to volunteer your time visit our Volunteering page or email us: contact@adsei.org
Datasets
There are huge numbers of open source datasets available. Finding them is easy. But you can’t expect your average teacher to (a) find a relevant dataset (b) make sense of it and (c) build lessons around it. They don’t have the time, or often the skills. The more help we can give them, and the easier we can make it, the more likely they are to adopt it in the classroom.
All content must go through an approval process before we publish it on our website to make sure it aligns with our mission and standards.
If you have a cool dataset, we need it to have:
- Written explanations (or videos)
- What is the data?
- How was it collected?
- What does each field mean?
- What are the limitations of the data?
- What can the data tell us? (and what CAN’T it tell us?)
- Easy format (usually csv), clearly labelled, simplified if necessary. Where csv isn’t possible, software to visualise/manipulate the data (or instructions on using eg nationalMap or MyMap or other systems)
Dataset Requirements:
- Data must be open source and shareable on the ADSEI website.
- CSV format for preference
- Small enough to open in Excel (ideally varying in size, smaller datasets of a few hundred lines for beginners through to several thousands for more advanced students)
- Consider sampling or top and tail depending on the dataset, or breaking up by relevant categories (for example voting data could be broken up by electorate, year, demographic, etc)
- Limit the number of columns to just what is needed for project & understanding if there are squillions (ideally columns shouldn’t scroll too far off the screen, unless they’re really important to the project).
- Provide raw dataset & simplified dataset if trimming (so kids who can code can use the full one)
- Don’t clean unless it’s insanely messy, because cleaning is a great lesson – So dates in different formats etc, leave them be!
- And please provide clear plain English explanations of what the data is, how it was collected, what the units are, what each field means, etc. (in a separate google doc)
- If they are datasets that change over time, or provide timely snapshots of information, please provide links to the originals, so that we can grab up to date versions for projects.
Classroom Resources
Teachers can be really daunted by Data Science, many feel that it needs skills and knowlwdge they don’t possess. So it’s really important we make easy to use resources that bring them in gently, building on skills they already have.
Below are some suggestions of the sorts of resources that could be used in a classroom.
- Exploring what the dataset means – what it can tell you, what it can’t, what its advantages and limitations are
- Analysing the dataset in different ways – especially exploring the difference between different ways of looking at the data and how they might mislead you. For example, the renewables dataset – simply sorting it tells you the top postcodes, but it’s easy to infer that since the top postcodes are nearly all in WA and QLD, they are the top states. When you calculate the average, though, they turn out to be really low in the rankings. It’s great to write up (or video) discussions of this kind of thing so that teachers know how to run the discussion.
- Exploring the best kind of visualisation for a particular dataset – or more accurately, for a particular question for a particular dataset. Examples of this, and even video explanations, would be awesome.
- Some Sample project plans (note that I can polish and add curriculum links etc if needed, just the annotated datasets and some analysis/exploration ideas would be super useful):
Biodiversity for Primary Schools (collecting own data)
Year 5/6 Renewables project (collecting own data)
Year 7/8 Renewables Project (using online dataset)
We have a page of data repositories if you need help finding interesting data (let us know if you know others we should add!)
Some datasets will need to be simplified for school use. For example, the Happiness report dataset has been broken up into by-year and by-country data, from the original which contains hundreds of countries and ~10 years’ of data.
The aim for now is that most projects should be doable in a spreadsheet, as we don’t have enough teachers with Python/coding skills to teach kids to do that. But don’t be afraid to include extension activities that need code!