Blog

Why robots are a disaster for tech education

It’s very tempting to see robots and other shiny tech toys as fantastic motivators for STEM education. After all, who doesn’t love playing with cool toys? Unfortunately this kind of hardware has huge drawbacks in the classroom. To show you why, let me tell you a story.

On the weekend I took my kids to Oz Comic Con. My 11 year old, Jen, is a HUGE tech nerd and loves all things hardware, software, mathematical, and, of course, STAR WARS. Dressed as a Jedi and wielding a lightsaber, Jen was magnetically drawn to the stall selling star wars drones. Jen had been saving for Comic Con for months, so the $50 cost, while more than they have ever spent on anything before, was well within their reach.

I did a quick bit of online research and it seemed like a good buy.

Behold Jen’s X-Wing in all its glory.

IMG_0958.JPG

You can imagine the excitement when we got it home, but we were out to dinner that night and didn’t have time to unbox and charge it. The next day Jen bounced out of bed and went straight to the box. Eating, drinking, and other necessities of life were not on the agenda, so it was lucky it was a public holiday and I didn’t have to try to get them to school.

Once charged (the drone), batteries installed (the controller), and with the beginner-pilot’s safety cage installed, we fired it up. The controller even buzzed when we inserted batteries and had Yoda saying “feel the force!”. The excitement was INTENSE. The instructions said to power up the controller and the drone, flip the left hand lever up and down, whereupon it would beep, and the flashing lights would then stop flashing to show that the devices were synced.

But there was a catch. Beeping occurred as expected, but the lights on both devices continued to flash. We powered both devices off and on again. We tried different batteries. We even went shopping for new batteries. We spent all day trying to get the damned thing to work, to no avail. 3 days later it still didn’t work and we were waiting for tech support from the drone company to reply to our emails.

Now you may think we were doing something wrong – and perhaps we were – but I have a PhD in Computer Science, and my husband is an Electrical Engineer. If we can’t make it work, what hope does your average teacher have?

Unlike with programming, a student, a teacher, and even an electrical engineer have very little hope of debugging a device such as this one, because there is no feedback. There’s no way of knowing its internal state. Short of taking the device apart and resoldering each of the connections and testing each component (not skills taught in your typical primary education course last I checked), there’s no way to troubleshoot these things.

Whether Robot, Raspberry Pi, or Arduino, hardware all suffers from these issues. There’s a significant chance that they won’t work out of the box. Even if they do, connections come loose and they might stop working mid-lesson, or not work next time they come out of the cupboard. And what we teach kids with these kinds of intensely frustrating experiences – when they are trying to do the same things as everyone else, but for them it doesn’t work – is that these problems are insurmountable. That they have no control over technology, no power to fix it when it breaks, and no way of understanding how it does what it does.

These are not the lessons we want to be teaching our kids.

*Update: The company got back to us the day after I wrote this, and very quickly replaced the drone. 10 days after the initial purchase we have a drone that works – but Jen’s enthusiasm – and confidence – has taken a severe battering.

ADSEI in the news

ADSEI has been in the news lately. Check out our Executive Director, Dr Linda McIver, on ABC Radio Sydney’s Focus Program, talking about Big Data and data literacy.

There was a profile piece on Linda in the Australian Financial Review, in BOSS magazine.

And an Op Ed in The Age, the Sydney Morning Herald, and other Fairfax publications on why kids need to be data literate: https://www.smh.com.au/education/we-need-to-arm-our-kids-against-the-interests-of-big-data-20180430-p4zcej.html

Linda also gave a recent YOW night talk on how kids can solve our data problems with Citizen Data Science: https://youtu.be/BteN0Qtt0kA

Data Science Education is an idea whose time has clearly come!

 

Conversations about renewable energy

Our first featured Dataset is renewable energy installations around Australia by postcode.

I downloaded the csv file “Postcode data for small-scale installations – SGU-solar” which is a beautifully rich dataset that offers a range of options for exploration.

When you open it in a spreadsheet package it looks like this:

Screen Shot 2018-05-06 at 3.22.53 pm

Can you work out what has happened to the postcodes? The first is 0! The second is 200. Australian postcodes are four digits, so what the heck is that about? This is an example of your spreadsheet hiding things it thinks you don’t need to know about – in this case, leading 0s. Mathematically speaking, there’s no difference between 0, 00, 000, and 0000. They all just mean 0. So spreadsheets (and other software) tend to remove the leading 0s, which means postcode 0 is actually 0000, 200 is actually 0200, etc.

Now let’s look at the first two solar columns. The first is historical installations from 2001 to 2016. We don’t seem to have any data from before 2001, but that’s not because nobody was installing solar before that. It turns out that it’s because 2001 is when the government introduced the mandatory renewable energy target and began tracking renewable energy.

Next question: how much solar is actually operating now? Answer? We don’t know. This data tracks installations. It doesn’t track people getting rid of their solar panels, or the panels ceasing to work. Installations are a reasonable measure of how much solar we have, but not perfect.

This opens the way for a great conversation about the data we want, versus the data we have, and how many data studies work with flawed or missing data, simply because it’s all we have available.

Ok, so let’s look at the first column. Having it sorted by postcode is logical, but not terribly interesting. Let’s look at the top 20 postcodes – to do that, we can sort the entire table by the second column (how many installations happened between 2001 and 2016), in descending order. In other words, put the largest values up the top.

Screen Shot 2018-05-06 at 3.34.48 pm

A quick glance shows us that the majority of the top 20 postcodes start with a 4, meaning they’re in Queensland. (If you’re not sure which postcode is where, as I’m not, you can check at a postcode site.) The top postcode, 4670 covers 53 regions, including Bundaberg. There’s a surprisingly large gap between the top postcodes and the bottom of the top twenty, which is interesting. Most of the postcodes in this list that aren’t in Queensland are in Western Australia. except for 3029, which is West of Melbourne, around Hoppers Crossing, and 3977, which is South East of Melbourne, in the Cranbourne area.

There’s a rich conversation to be had around why these suburbs have so much more solar than other places in Victoria. Toorak, for example, a notoriously wealthy suburb, comes in at 1701 on the list. My suspicion is that areas with a lot of new housing are more likely to have solar, as it gets put in when the house is built as a way to increase the energy rating of the house. But this is a topic worth exploring! You don’t have to know all the answers, as it’s an opportunity for the kids to research and explore, and come up with their own theories for why it might be the case.

Let’s look at column 4: solar installations in January 2017.  How different are the top 20 if you sort the whole table by this column?

Screen Shot 2018-05-06 at 3.44.32 pm

Now WA scores better, and the rest is still largely over to Queensland, except for one Victorian postcode (Cranbourne area again), and this time one NSW representative.

Why do WA and Queensland do so well on both historic and recent measures? This is an opportunity to explore the politics and have your students find out what incentives there are to install solar in those states. Could it be due to solar feed in tariffs, government incentives, or home energy rating requirements?

You can keep going and explore the different columns, or you could step it up a notch and start to look at how the columns are related. For example, are postcodes with a lot of historical solar installations also likely to have a lot of recent ones? You can do that roughly by eye, simply by looking at whether the top twenty when sorted by those two columns is similar or very different, or you can go heavy on the statistics and try to work out whether both values are equally predictive of a postcode’s place in the ranking. (I won’t go into that here, lest I scare away the non-statto’s among us!)

You can use this dataset to explore different attitudes to solar power around the country, and the possible reasons for them. You can use it to question which incentives work and which ones fall flat, or whether solar incentives actually make a difference.

Now, what if you wanted to visualise this data? Well, you could find out the names of the top 10 postcode areas and graph them. (You could just graph the postcodes but it’s not terrible meaningful to anyone who hasn’t memorised the postcodes of Australia!) Top 10 is a fairly arbitrary selection, aimed at not putting too many places into the one graph. It would make more sense to choose a place in the data where there’s a big drop from one value to the next. In this case I might go top 5, since there’s a big drop from 5 to 6. It shows you the top performers well, but doesn’t show you much else.

Another technique would be to colour a map by number of solar installations. Say, bright red for >9000, and becoming paler for each drop of 1000. This would be rather time consuming given that there are 2795 postcodes listed, so this is an opportunity to consider aggregating your data. What happens if you use average stats for each state?

You can do that in Excel or any other spreadsheeting package by sorting the data by postcode, and then just copying and pasting each state into a separate sheet, but it’s lso nice and easy in Python. (I’ve been lazy and lumped the ACT in with NSW.)

Screen Shot 2018-05-06 at 5.04.20 pm

Interestingly this shows that the state that dominates the top 20 doesn’t perform as well when you average over all of its postcodes, so there is another rich conversation to be had about different ways of ranking data outcomes, and how you can characterise data in accurate but misleading ways.

It’s a great example of not needing complex technical skills to explore a dataset. Being able to program unlocks more ways of looking at the data, but to get started all you need here is the ability to short a spreadsheet by different columns, and a wealth of information is at your fingertips!

We will publish more datasets and more explorations as we go along, but in the meantime why not find your own datasets, and explore the things it can tell you? There are no right or wrong answers in this game, just different ways to play with the data. The more you play, the greater your data literacy.

 

 

Bringing Data Science TO YOU

ADSEI is super excited to be partnering with CSIRO and AeRO (Australian eResearch Organisations) to run some public events at the C3DIS Collaborative Conference on Computational and Data Intensive Science at the Melbourne Convention and Exhibition Centre (MCEC).

FOR the general public we have a science panel event at 7:30 on Wednesday May 30th:  Data Intensive Science: from Astronomy to Zoology  Come and hear Scientists talk about the uses and abuses of data, and ask your questions about science, data, and everything! You can book tickets for only $5 per person.

For Year 10-12 Students there is a student day on May 30th from 10am until 2pm. 

This is an outstanding opportunity for students to learn about cutting edge STEM research, hear talks from world class scientists, and to meet researchers using Computation to solve problems in areas as diverse as Biology, Climate Science, Astronomy, Marine Science, Bushfire Prevention and Management, and much, much more.

Teachers can bring students to this day for FREE, and student groups are welcome to enter the Visualisation Competition. 

Students can work in teams to choose a real world dataset (such as the workforce equality dataset on data.gov.au), analyse it to answer an interesting question and visualise the results.

More information on the student day and optional visualisation competition can be found here.

FOR Teachers there is a workshop on integrating STEM into your classes using Data Science. For $250 you can see keynotes at the conference, attend a workshop where you will build classroom projects and lesson plans around CSIRO and other datasets, attend the Poster Session drinks and the Gala reception in the evening.

 

 

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??