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Girls in STEM

If I hear one more person say “Girls just aren’t interested in tech” or “girls naturally go into the life sciences, it’s biological” I swear I will explode in a way that puts thermonuclear weapons in the shade.

At the same time, I get very frustrated with programmes that aim to attract girls to technology using 3D printed jewellery and sparkly shiny things.

I applaud people making efforts to get girls into tech. I really do. And having a diverse range of such programmes probably gives us a better shot at attracting a diverse range of people to the field. Which is great.

But I have two problems with the sparkly pink approach. First of all, I think it grossly underestimates and trivialises girls. Are we, as a gender, so shallow that it takes sparkly pink things to attract us? I reject that premise utterly.

And the second problem is that lack of girls is merely the obvious, measurable diversity issue in tech. We have a severe diversity problem that is not measurable with chromosomes.

The issue we have is that we are attracting the same types of people to STEM fields, especially technology, that we already have in those fields. That’s natural, to some extent – like attracts like. But if we are to design new technologies to be truly inclusive – like making our payment devices accessible for the blind , or creating wireless microphones for female speakers*[footnote] –  then we need a truly diverse range of designers who will question, challenge, and innovate with everyone in mind, not just people like them.

If we only have people in technological roles who have been immersed in technology their whole lives, then we will only have products designed for those people. And that can render those products inaccessible, and indeed inexplicable, to the rest of us.

So we need to attract a broader range of people into tech than we are at present. And I don’t believe that sparkly pink things are going to cut it.

We are grossly underestimating not just girls, but all of our kids, if we think that they are only attracted to fun and frivolous things. Attract girls with sparkly pink and boys with video games – you’ll just get more monoculture. What we need to do, more than anything, is to show our kids the relevance of technology. What can you use this stuff for? How can you make a difference? What does it mean?

When we used to teach our year 10s programming by having them write code to draw pretty pictures, we had low numbers choosing to study computing in year 11, and very few girls (around 5 at best). The single most common piece of feedback we got was “Why are you making us do this? It’s just not relevant or interesting.”

When we started to teach Data Science using authentic datasets with real problems to solve, we doubled the number of girls going into Computing in year 11 (although as a data nerd I do have to point out that one data point does not make a trend! What it does make is an excellent start.), and the most common piece of feedback we got is now “This is SO useful, and so relevant to what I want to do.”

That’s why I’m so passionate about the Australian Data Science Education Institute. Because if we can support teachers to put Data Science into the way they teach everything – from history and geography through to science and maths – using real datasets, then we are showing the kids how technology is relevant to everything they do.

 

[footnote] The microphone issue may sound trivial, but I was presented with a wireless microphone last week that had a receiver designed to clip onto a belt. I was wearing  a dress. With no belt. Fortunately I had a scarf around my neck that I could tie around my waist for clipping the receiver onto. But I should not have to rearrange my clothing in order to accommodate the technology. And what would we have done in the absence of that scarf? Seriously, how hard can it be to design devices that work for everybody??

Data Science for Primary Schools

People tend to assume that Data Science is a high level skill, only applicable to high school – and the senior years of high school at that. But engaging with data is something we can do from very early on.

Got a kinder class you want to do some data with? How about getting the class to keep track of who does what activity each day, using tally marks on a white board or flip chart, and then work out which activity is the most popular? Then do it again only this time tally which activities girls do and which activities boys do? (The results may surprise them.) This is data science.

In primary school, kids can collect and analyse data from their own environments. They can do a rubbish audit and work out which types of rubbish are the biggest problem in the yard.

The younger kids can do that simply by piling the chip packets in one pile, the ice cream wrappers in another, and the cling wrap in a third, and then looking at which one is bigger.

The older kids can be making graphs. They could look at which types of rubbish are more common on canteen days, versus when the canteen is not open. Then they could work out a solution to their worst rubbish types – for example, if it’s chip packets, maybe the canteen could use large chip packets and distribute them in smaller lots in reusable containers.

Or they could do a biodiversity audit of a section of garden in the playground, perhaps comparing a garden which has only one type of plant with a garden which has a variety. They could plant a veggie garden and measure plant grown in a bed with compost versus a bed without compost.

Anything that allows them to collect data about their own environment and then uses that data to enact positive change – reducing rubbish, increasing biodiversity, attractive native birds to the playground, etc.

It’s really important that we start engaging kids with data science and computation early, because by the time they reach High School they’ve often already lost interest. And that’s a problem for them, and a much bigger problem for our society! But more on that in another blog post.

PS If you’re a primary teacher and need some help with the Australian Digital Technologies Curriculum, you might like to check out my “Demystifying the Digital Technologies Curriculum”  posts on my old blog.

Basic Data Literacy

It’s easy to get caught up in highly technical aspects of Data Science. To focus on complex numeric analysis using programming languages like Python or R, and think of outputs like fantabulous heatmaps and stunning geospatial visualisations.

But an article I saw in The Age today highlighted some of the deceptive data practices we see every day. Some of them are wholeheartedly deliberate, designed to mislead us and persuade us of untruths. Some, like this one I suspect, are purely accidental. But the journalist who wrote this article should never have let it stand, and the readers need to be able to think critically about what these numbers mean. Read the paragraph below for a moment.

Screen Shot 2018-02-23 at 3.38.47 pm

Can you see why it got my hackles up?

“From a 2-million-ton butter stockpile… dwindled to less than 12 days’ supply.”

So hands up if you know how many tons of butter constitutes a days’ supply?

Are you actually able to compare those two figures without further research? I certainly couldn’t. As it happens, I did further research and found a web page that puts global butter consumption at 8,000,000 tons annually.  I have no idea how valid that webpage is, but let’s roll with it for a moment. To compare the figures we divide 8 million by 365 to get a daily figure, and then multiply by 12 to get back to tons per 12 days. I get 263013 tons (and some assorted decimal places which I am going to wickedly ignore for now, but that will be a whole other blog post).

By dividing 263,000 by 2,000,000 we find that it’s roughly 13% of the stockpile we had before. Which is, it’s true, a significant decline. But now we can see how much of a decline it is, which trying to compare 2 million tons with 12 days’ supply made impossible. Even better, let’s represent it visually:

Screen Shot 2018-02-23 at 4.07.07 pm
Butter stocks then and now

 

This is just the default graph from Google sheets, but it conveys the size difference quite effectively.  (There are, of course, plenty of ways we could improve the graph, but one snark at a time, ok?)

It wasn’t hard maths. Or a challenging graphing exercise. Or even a tricky research problem – although, as noted, I have no idea how valid the figure of 8 Millions tons per year is, or indeed what year the measurements were taken (although the graph on that page implies either 2004 or 2009, but from the information available on that page I can’t be sure about the aggregate figures). But then I don’t know how valid the figures are in the original article (and, to be honest, I’m just not that excited about butter consumption, unless it’s on my own toast, and if we’re measuring that in tons then I probably have a problem).

The point is that if you’re going to use figures to support your argument, and you’re going to compare them by saying they “dwindled” from one value to another, it’s not rocket science to make those figures easily comparable.

This is one of the reasons Data Science needs to be core in schools. To make sure that when we present our own data, we present it in a way that’s both valid, and easy to interpret. And to ensure that when others show us data, we can analyse it critically, and call it out when it doesn’t make sense. Whether it’s by mistake, or by design.

 

The Power of Data Science

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.

— Sean

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.     

— Reena

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 could look at census data, climate data, or live parking data for the Melbourne CBD.

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 contact@adsei.org today.

 

Authentic projects, Authentic Motivation

This Monday 8 John Monash Science School year 12 students will present their Computational Science projects from last year as a poster at the Lorne Genome Conference.

These students worked with Dr Sonika Tyagi, the Monash Bioinformatics Platform Manager, to develop software that identifies micro RNA sequences, and works out their likely structure.

They worked in two teams, so that one group worked on the identification, and the other on the structure. They took experimentally verified results and used them to train their software, using machine learning techniques.

Their project was submitted in October, for credit in their year 11 Computing class, but both groups are continuing to work on the software to improve it. They are keen to make the software faster and more accurate. And the key reason that they are still working on it is because it’s not a toy project purely for classroom credit. It’s a real project that has the potential to have an impact in science.

Most of the students in these groups had no background in Biology, but they were keen to learn more about machine learning, so out of the range of projects on offer, they chose this one as an opportunity to learn new skills and produce something useful. In fact one of the students in the project wasn’t even studying Computing. He was just really excited by the opportunity to work on a real and challenging project.

When I was teaching I ran projects like this every year. Not every student produces something that goes on to be used, but every student has the chance to work on real projects, with real data, and real outcomes.

Every year I had students who kept working on their projects long after the subject finishes. Every year the students consistently rated the projects as both the most challenging and the best parts of the course. It was HARD. And they loved it.

This is the beauty of Computing, and of Data Science. Kids can do something real, and have an impact, while they’re still at school. Even Primary Schools can run Data Science projects that have an impact on their local community. I’ll write more about that soon.

Imagine what a difference we could make in the world if all students had opportunities to do real projects and learn Data Science and Computational Skills from the start.