Professor Judy Robertson

Chair in Digital Learning, Moray House School of Education

Tell us about your journey to where you are now
I studied computer science and artificial intelligence at university and then had various research jobs before becoming a lecturer at the University of Edinburgh’s School of Informatics. I’ve also lectured in game design at Glasgow Caledonian University and in computer science at Heriot-Watt University.
About five years ago, I saw an advert for professor of digital learning in the School of Education at the University of Edinburgh. I thought that sounded like a great fit – and I got it. So I’ve been working in the School of Education and recently started a joint appointment in the School of Informatics at as well!

How many plates to you currently have spinning at work?
Ha – I’m spinning so many plates it’s hard to tell! I have flexible working arrangements, so can do school pick-ups. My day starts at about 7.30am and on some days I finish early enough to be at the school gates at 3.15pm. It’s good – early in the morning I get uninterrupted time to get things done. My work is varied because of my joint appointment – on Wednesdays and Thursdays, I may be at Informatics helping staff with their teachers and on other days I’m working on my data education project at Moray House. Sometimes I’m at meetings in schools in the city region – it’s very varied, which is great.

What are you most passionate about?
I’ve always been interested in critical thinking and how people can spot red flags in pseudoscience and marketing, helping the public to open their eyes about what is shared in the news. Critical thinking is a fun subject to teach and I’m seeing ways to work that into my data science education project. We have a chance to teach children how to spot red flags for fake news, to take an evidence-based way of thinking about information in daily life. So that’s really exciting because there’s no end of ways that people can get fooled and fool themselves! It’s interesting to give people a toolkit to study that.

And is it is there anything you wish you could do more of?
I’d like to spend more time writing. I enjoy thinking really hard for a long period of time and developing a view. If you’ve got lots of meetings, your thinking gets chopped up.

But you must get the opportunity to read other people’s writing through supervision?
Yes, definitely – but I’ve also got some non-fiction books on the go and I’m always reading at home. If I’m on the bus, I’ll have an audiobook. At the moment, I’m trying to teach myself about data science. So I’ll always have a goal that I want to be learning that or reading about it.

And what areas of learning or interesting projects do you have going on at the moment?
We just delivered an event for school pupils across the city region, which was called Data Town. It was trying to find out what kids would like to learn from data science, and explore their feelings about data privacy. That was a really cool event; we had about 150 kids! I’ve got a big stack of evaluation forms to go through, which I really like (laughs). You get insights into what’s going on in their minds.

The kids are deep, funny and fascinating. We had a robot called SimMan at the event, which medical students use to practice CPR. It’s creepy because it looks like a mannequin or a shop dummy but it also looks like a dead body. However, it moves robotically and one of the kids wrote, “I’m not sure if Sim Man is alive or dead”. It’s fascinating because there are theories of mind about how children understand robots. For young children, it’s a strange mixture and it is interesting to see how they make sense of it.

What do you think are the biggest challenges to women and girls in the field when it comes to data and data science?
The problem is lack of critical mass. I was reading some stats that about 15% of data science employees are women, so that boils down to you probably being the only woman in the room or maybe there are some other women in your company but not very many. If you don’t have enough people, you won’t reach critical mass, which is meant to be around 30%. You end up feeling isolated and facing stereotypes that threaten your performance until you get that critical mass of people, which makes it normal to be a woman in the environment. The other thing in the wake of ‘Me Too’ is more reports of harassment in the tech industry, sexual harassment and gender discrimination – all kinds of discrimination, actually. We know that other work spaces have harassment as well, but it seems like tech may be particularly bad. That’s worth investigating because if we’re encouraging women to study data and technology then we need to make sure they’re going to have a positive working experience.

Do you find your work supportive of women?
Yes. I think you also get used to walking into a room and thinking, “Oh, okay, so it’s mostly men here”. As I get older and as I’ve done more reading about gender discrimination, I notice little things more. I notice the way people interact or the way people talk over women or the way people potentially unintentionally patronise women.

What would you like to see change in the gender makeup of data science and the culture?
Culture is really important. It’s about respect and treating people the way that you would want to be treated yourself. If that was more the norm, the workplace would be better for everyone. If you take something like banter or sexist jokes, it can be really grating to be the person on the receiving end of that. We need greater awareness among men in tech workplaces on how to create an atmosphere of respect and how to be male allies effectively. There’s been lots of initiatives over the years about building women’s confidence and so on – it would be nice if we started to teach men about how not to destroy other people’s confidence and how to behave in a professional way.

What would you recommend to women and girls who would like to do what you’re doing?
Read as much as you can, which is advice for everyone. Particularly for early career women and maybe women who have just started a family: train yourself to not feel guilty over everything – whether the house is clean or taking on extra work. When I had a child, I realised that this is good advice. Women are socialised to apologise for situations that aren’t their fault.

Do you work with any interesting data sets, analysis techniques or AI machinery?
Research methods and statistical techniques give us the best insight into what the truth is so that we can avoid fooling ourselves. I like to use the realist evaluation methodology, which is a bit like detective work, asking “why are the results like this?” With complex social problems, there’s no simple answer.

And are there any social problems you’re particularly interested in?
I recently started learning about physical inactivity. There’s a global epidemic of being physically inactive, which is really bad for mental and physical health. In Scottish schools, the government has increased the amount of physical education. A few years ago, we designed a mobile phone game which encourages kids to take more exercise. It’s like Pokemon Go, actually before they invented it! It’s naïve to think that one game will solve the problem because inactivity is so complex; there’s lots going in people’s lives. We did an evaluation of the game and found that the kids liked the design and used it every week. But it also rains a lot in Scotland so it disrupted how much time they take exercising in the playground with it. It’s a good example to a technology person like me that you can come up with a great design but there needs to be space in people’s lives. It’s not a magic cure.

What do women bring to the design of these sorts of products?
I do a lot of user-centred design and the idea is that you get the people who are going to use technology to design it with you. I used to teach interaction design and question students about their perceptions on how to get into the mind-set of users. User personas in design is just sometimes blatant stereotyping – you know, fictional users like Margaret, aged 60, wants to use this app to get healthier. If you’re not careful, you end up with bias, stereotyped portrait of what a 20-something- male thinks Margaret wants. If you talk to a real user, you might be surprised.

I’ve always found it fascinating to work on the design of technology, trying to design something for a particular user group – one of my students is designing something for kids in hospital. Like research, I love analysis and getting to the heart of what the data tells me, finding surprises when things don’t go as I planned. It’s very intellectually challenging.

What is the biggest opportunity you’ve had in your career?
Probably the City Region Deal funding for the Data Education for Schools project. It’s a chance to make a real difference in terms of education about data, going back to the critical thinking of teaching computer science. It’s a good opportunity to package those things together and it will have a real impact on learners. The City Region Deal’s Data-Driven Innovation component is bringing an unprecedented amount of funding to the area I’m most interested in.

What do you look forward to with the Data Education for Schools project?
I look forward to when the programme is established and we’ve got really great projects for the kids to get involved in and then for me to be able to track what the kids are learning, where they’re getting excited and inspired.

To finish up, do you have a fun fact about yourself and or a hero/heroine?
I asked my son to help me with this one – he missed the ‘fun’ bit (laughs) and sent me a picture of a book with facts about death. Six hundred people in the US die from falling out of bed each year! I’ve just finished reading Caroline Criado Perez’s Invisible Women – it’s so meticulous, detailed and full of compelling facts about gender bias in society; an awesome piece of work.

Image of Professor Judy Robertson
Picture by Lesley Martin, interview Poppy Gerrard-Abbott

I love getting to the heart of data, finding surprises and being intellectually challenged

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