I was recently on a panel discussing how to get more women interested and involved in STEM (Science, Technology, Mathematics and Engineering) fields. The audience were primarily young people considering what they might study at University, and schoolteachers.
The questions posed by audience members highlighted a couple of key points; that gender is too often seen as binary, and that we need to think not just about injecting time, money and resources into promoting STEM education for women, but about how we can most effectively utilise the numerous resources that are already out there.
This second point is particularly important, because it is relevant not just to aspiring female scientists and technologists, but for anyone who has an interest in the field and a desire to learn more.
It is striking that despite the significant variance in mathematical ability between within gender, generalised binary rules are often applied when discussing these issues. This phenomenon is not exclusive to gender and skills, but seen consistently in situations where there are different groups, leading to stereotype formation.
Humans like to classify things, we often do this subconsciously, to save cognitive effort. The trouble is we are not always aware of why we hold the beliefs that we do, or whether they are at all valid. You can test your own unconscious bias by using an online test devised by Harvard university here.
In the case of gender and STEM, what we often see are beliefs that men or boys are better at maths and computer science and women are better at the arts. The reality is that some men are good at maths and computer science and so are some women, and some men are talented in the arts as are some women. Taking a binary view is unhelpful and frustrating. Over time it becomes problematic because it reinforces false beliefs, and that is something that was noticeable speaking to some of the young girls at the STEM debate.
A group at Harvard tested these assumptions and discovered that women actually performed worse on maths tests when their gender was made salient, and Asian American women performed better when ethnicity was primed, highlighting the implications that stereotypes can have on performance.
It is important to try and normalise gender from a young age, and to target interventions at educating both men and women. We need to avoid taking a binary view, and actively question the stereotypes we may hold.
The second thing we can do is think about how best to raise aspirations of and open doors up to young people of both sexes. There is a lot of effort and funding attempting to do this, but it is often very difficult to know how best to allocate resources.
The face of education is changing, currently there are far more resources available to young people, and anyone with an internet connection than ever before. A teenager in Barnsley can now take an advanced Statistics course run by a top US university for free using Coursera. The days of textbooks are over. This can be incredibly useful, but is limited by the extent to which it is accessible. While many parents and teachers are highly motivated to encourage students to further their STEM education, plenty remain unaware of the options that are already out there.
Influence and proximity are inextricably linked. Suggestions from external sources are important, but a repeated message from someone who interacts with a student on a daily, or even weekly basis is far more likely to be adopted in that student's behaviour. If we want students to be aware of the free resources they can use themselves, enabling parents and teachers to spread the message is a worthwhile endeavour.
Likewise, if we can steer away from binary views of gender in the classroom and at the dinner table, then the next generation will be well set for following the careers of their choosing.
For those interested in initiatives promoting STEM education, I have been impressed by Mathigon.org; a fantastic free resource for students and teachers alike, and Code First: Girls; a programme designed to help young women learn to code.Suggest a correction