Vaccine hesitancy is a logical consequence of the way we teach science.

Vaccine hesitancy could literally kill, yet it’s a logical consequence of the way we teach science.

We tend to think Science is about facts and right answers. This is absolutely the way we teach it, but it’s the opposite of what Science really is. We learn the periodic table, the arrangement of subatomic particles around a nucleus, the equations for force and motion, and how to name the components of a cell. We teach with experiments where known inputs are treated with a known process, producing a known outcome. Kids who don’t get the “right” answer either fake their results or copy from their neighbours. This is not an education in Science, it’s an education in confirmation bias – in seeing what we expect to see.

Science is actually a way of exploring and understanding the world, and of solving problems. By its very nature science deals with uncertainty, and constantly proves itself wrong as new information becomes available.

Scientific theories are based on the information we have right now. Sometimes we can’t see, measure, or understand enough to explain a phenomenon fully, but we have a model we think is right, and it’s right enough to help us understand some parts of the way the world behaves.

We can see this in the way our understanding of covid19 has evolved. At first we thought it was transmitted by droplets, so that unless you were in the direct path of someone’s sneeze or cough, the main risk was touching an infected surface. As we learned more, our understanding developed. We now know that it is very easily transmitted by aerosols – in other words, virus particles can hang in the air in such quantities that we easily breathe them in and become sick.
This explains why transmission rarely happens outdoors, and why ventilation is key when we’re indoors. It also explains why hotel quarantine is so problematic – because even if there is no air transfer between rooms, an infected person walking through a corridor can leave that corridor so contaminated that it’s infectious for some time afterwards. It also explains why masks (when properly worn) are so effective at preventing transmission.

We’ve also seen our understanding of vaccines and their side effects evolve. And the fact that the story keeps changing – from “Astrazeneca is safe for everyone” to “it’s safe for everyone over 50” and now “it’s safe for everyone over 60” – makes people nervous. But it’s this rapidly changing information that should give us comfort and confidence. This is Science doing its job – adapting our understanding according to new information.

I once interviewed Cameron Neil, who at the time was head of the Fair Trade Association of Australia and New Zealand. We were talking about the fact that it’s hard to buy ethically, because the information we have keeps changing. Neil’s response is an ethical approach to consumption, but it also encapsulates an intelligent approach to Science: “With the information available to me today I make the best choice I can, knowing full well that I may get information tomorrow that means the choice I made was the wrong one, and I’ll have to do better next time.”

When it comes to vaccines, of course, we crave certainty. No-one wants to take something that might harm them. We want to know with absolute clarity what the best thing is that we can do for our health. The fear and uncertainty in the community around the Astrazeneca vaccine is palpable. Yet according to Hassan Vally, an epidemiologist at La Trobe University, the risk of dying from a blood clot due to the vaccine in Australia is 0.5 per million, while the risk of dying in a car accident in Australia in any given year is 28 per million. Compare that with the risk associated with taking aspirin or other non steroidal anti-inflammatories (NSAIDS), which is 24.8 deaths per million people, or a staggering 153 per million users of those drugs. This is where a different understanding of Science could help us.

If we truly understood how Science worked, the rapidly changing information would give us confidence that our understanding was getting better and better. If we taught Science as an exploration of the unknown, and a constantly developing set of theories, rather than a fixed set of hard facts, we would be far better prepared to understand the constantly evolving picture of covid19 and its vaccines.

It’s really hard to teach kids critical thinking skills when your toolkit is questions that all have right answers, curricula full of facts and straightforward procedures, and textbooks that leave kids floating on an uneasy sea of factoids, memorisation, and perfectly neat examples tied up with a bow.

Imagine if we taught Science by exploring the world. By trying to solve problems that have no textbook answers, where students have to rigorously test and evaluate their own work (and the work of others) in order to be confident of their results, because they can’t just look up the answer, and have the teacher mark it right or wrong.

If we grew up with this basis, knowing Science as an evolving, developing discipline, rather than a bunch of facts pinned to the unchanging pages of a textbook, we would know that our changing understanding of covid19 and its vaccines is not a threat. It’s what’s keeping us safe.

Read more about the way we teach science, and about teaching our kids to be critical thinkers, in Raising Heretics: Teaching Kids to Change the World.

Teaching STEM is more important than robots

This is an excerpt from Raising Heretics: Teaching Kids to Change the World, which is due out on August 1st.

I founded the Australian Data Science Education Institute in 2018 because I wanted to show kids that they are capable of working with technology, that it is relevant to them, and that they don’t have to look like Sheldon from the Big Bang Theory in order to learn to program.

It’s well known that the technology industry has a diversity problem when it comes to women, but lack of diversity goes way beyond gender. By trying to increase the number of women and girls in STEM, we are only tackling the easy part – though it’s actually not that easy, judging by the sheer volume of women in STEM programmes and the persistently stubborn failure of the numbers to actually shift.

The problem is that we consistently attract the kinds of people to tech that are already there. We are missing big chunks of the population – boys included. Boys who don’t see themselves as nerdy, or who don’t see the point of tech. Girls who don’t see it as relevant to them. Non binary and gender queer kids who don’t see themselves as represented or welcome in any of the tech programmes available to them.

If we had true diversity in technology and Data Science, we’d have a range of ethnic and cultural backgrounds, as well as people with a wide range of physical abilities. We’d have people on our design teams that are mobility compromised, vision impaired, with allergies, with varied gender identities and sexualities, with every possible skin tone and body shape. We’d have people who act differently, dress differently, think differently, and have different needs. I have headphones that don’t work well with long hair, for goodness’ sake! Guess who was on that design team?

This lack of diversity is bad for the technology industry, but it’s even worse for the rest of us, because technology is changing the shape of our world at an alarming rate, and we currently have very little say in our own future. Companies like Uber and Doordash are radically changing our working conditions and eliminating hard won entitlements and protections, while Facebook and Youtube spread misinformation and encourage radicalisation, all in the name of keeping people on their platforms and maximising their profits. Our world is being directly shaped by technology companies that are working in ways we don’t understand and have no control over.

Meanwhile we see human resources companies using AI to filter job applicants, claiming that their system eliminates “human bias”, without admitting the possibility that it introduces new forms of machine bias. We see “predictive policing” algorithms being used to predict crime and target particular communities in disturbing ways. We see a rush towards machine learning and artificial intelligence systems for their own sake, rather than for problems they can legitimately solve, and we have a wholly unwarranted confidence in the accuracy, reliability, and objectivity of their output.

It turns out that diversity in the technology industry is only a small part of the reason why teaching all kids Data Science and STEM skills matters. The big part is that we need a technology and data literate population who are trained to think critically and creatively, and, in particular, trained to believe that they can solve problems. That’s the world we need to build. And the foundation stone of world building has to be education.

We have a choice. We can train kids to be obedient process followers who don’t rock the boat, or we can train them to be challenging, critical and creative thinkers who ask difficult questions and come up with innovative solutions to our worst problems.

Above all, we need people who are prepared to be heretical.
Who ask “why?”
Who ask “how can we be sure?”
Who ask “what have we missed?”
Who ask “how can we do better?”
Who ask “who are we hurting?”
Who ask “how can we fix this for everyone?”
Who ask “how will we know how well it works?”

These questions are often heretical. By asking them, I’ve sometimes made my bosses very unhappy. They make people uncomfortable. But they are crucial to building an ethical, sustainable, positive future for all of us.


Pre-order Raising Heretics now:

Raising Heretics – why should we?

This is an excerpt from the Introduction to Raising Heretics: Teaching Kids to Change the World, which is due for publication on August 1st. We’ll publish more excerpts here from time to time, so check back for more!

In this book, I want to show you how Data Science Education is key to nurturing a rationally sceptical, creative, ethical, problem solving population who can save the world.
I’m going to do that by looking at the problems we have in the Data Science and Technology communities today, and how those communities are shaping our world – problems and all – in Chapter 1: “Who’s in Charge?”

Given that Data Science is in the driver’s seat, taking us towards a future we are not yet equipped to understand, Chapter 2: “The Shape of the Future”, talks about what the future could look like if everyone had enough data literacy to form evidence based policy, support high quality science, and have a say in the shape of our future.

Of course, if we want an evidence based society that treats science with respect, we need to understand how science actually works. Too often a change in our understanding of something – whether it’s climate change, a virus, or our diet – leads us to think that science got it wrong. Scientists, however, know that this is how science progresses; by improving our understanding of complex systems. That means that sometimes what we think we know about science today turns out to be wrong tomorrow. This is science at its best. Unfortunately there is a perception in the wider community that science is solved. And science education reinforces that idea quite firmly. Chapter 3: “Science is Solved”, looks at the way we (mis)understand science, and how we can fix it.

I’m then going to explore the issues with our current education system in more depth. There is no such thing as perfect data, yet we treat data with more reverence than it deserves. Our entire education system is built on the idea of being measurable, yet all too often “measurable” winds up being the opposite of “meaningful”. Chapter 4: “Measurable or Meaningful, pick one,” considers how we got here, and how we can create an education system that focuses on meaningful outcomes, and develops our students into rational, ethical heretics.
All of these goals require us to get comfortable with the idea of uncertainty. To be prepared to challenge the status quo, query accepted wisdom, and even to question our own findings. Chapter 5: “Accepting the Unexpected,” focuses on why uncertainty is important, and how we can get comfortable with it, especially in education.

Why should you take my word for it? Chapter 6: “Projects with Impact,” goes into detail about how Data Science projects work, with case studies from my own teaching, and Chapter 7 outlines templates for Data Science projects involving community projects and more global issues, with examples of units ADSEI has created right across the curriculum, from Humanities to STEM.
Finally, how do we get there from here? Chapter 8: “What now?” maps out what we need to do to overhaul our education system and raise all of our children to be rational heretics, so that they can understand the world, and then save it.

Win for your school!

Raising Heretics book cover - a person looking sceptical surrounded by objects

We are thrilled to announce that every copy of Raising Heretics pre-ordered here goes into the draw to win a $5000 school consultation package for your school! If you want your kids’ school, the school you teach at, or a local school, to have the opportunity to develop projects that build kids’ STEM and Data Science skills while solving real problems in their own communities, buy a copy of Raising Heretics: Teaching Kids to Change the World and enter the draw by filling out the form!

If you’ve already bought your copy, you can still fill out the form.

Terms and Conditions

  • Every copy of the book purchased through this page earns one entry into the draw
  • Any donation over $30 through the same page qualifies for one extra entry into the draw
  • The winner will be drawn by Dr Linda McIver at the online launch on August 1st and announced on this blog the same week.
  • The winning school has three weeks to accept the package in writing to, otherwise another winner will be drawn
  • All projects developed as part of the winning package will be shared on the ADSEI website under a creative commons attribution non commercial license
  • The package is valid until December 2021. If the school cannot make use of the package in this time and notifies ADSEI, another winner will be drawn if enough time remains.
  • The winning school agrees to publicity around the resulting programs over the course of 2021 and 2022.

2021 Seagrass Data Visualization Competition

Seagrass in Moreton Bay

The Australian Data Science Education Institute (ADSEI) and Science Under Sail Australia (SUSA), with support from the GRBF and Reef Trust Partnership, are thrilled to announce our 2021 Data Visualization Competition, open to all Australian Secondary School students.

Using Data Collected by SUSA’s citizen science programme for schools in 2019 and 2020, your challenge is to create effective and compelling visualisations of the different benthic habitats – including seagrass, coral and bare substrate – found on the Great Barrier Reef.
Choose either the Main Project or a Data Analysis Project.

Main Project
Present the locations of the survey sites on a map with the ability to zoom in and out to see data at the whole of GBR scale as well as zoom in and look at a single bay or island
Provide options to show people what the benthic habitats look like at different locations – using our photos or videos (2000 videos available)
Provide a visual link (colours, shading or lines around multiple sites) that link survey sites with similar benthic habitats.

Data Analysis Projects
What depths are seagrasses observed at in different regions of the GBR
Does the max depth seagrass are observed at correlate with the GBR water quality (clarity) data – available through GBRMPA (If you need helped getting this data contact ADSEI)
Look for other variables that predict or correlate with observation of seagrass – or living hard coral?

The results will be judged by SUSA and ADSEI. The student with the best project will receive a $100 gift voucher. Second and Third place will receive $50 gift vouchers. All projects that meet the criteria will be displayed on the ADSEI website and publicized on social media

While students retain the copyright, submitting the project to this competition grants SUSA, ADSEI, and the GBRF the right to display the work publicly in any form in perpetuity.

Entries are due June 30th 2021.

Register now for free!

For queries, please email