First of all, let us start on the same page. The percentage of women in computer science should be higher and can get higher. How am I sure of this? Look at the graph below.
The percentage of computer science degrees awarded to women used to be 37.1% but now is a measly 17.6%.
How can we change this? Now Know has created 7 key strategies in order to encourage more females into computer science. Some of these strategies are unique and some are common but fundamental:
Strategy 1: Girls care more when social impact is involved
Research shows that girls are more interested in something if they see the impact it will have on the community and others. For example, a class at MIT focused on using computer science for social good had more women enrol in it than men. We can use this observation to our advantage by framing the impact of computer science towards social good. A lot of introductory computer science courses focus around video games. Instead we allow the student to select what they would like to build – a video game, a website, a community tool?
Strategy 2: Knowing the bigger picture gets you through the details
Computer science courses can go straight into the syntax of a particular programming language. But what good is that? Google research shows that females who are unfamiliar with computer science associate it as ‘boring’. By focusing first on the bigger picture, we can show that the syntax of a particular programming language will be a step in order to arrive at the final goal. This improves motivation and excitement.
Strategy 3: Subtle stereotypes
Not every woman will experience an explicit situation where they are judged because of their gender, however, every woman will experience subtle biases. By implementing small things such as more pictures of women coding, we can shift the subconscious of both men and women to believing that women can do computer science.
Strategy 4: Building confidence
Research shows that men are more confident in their abilities than women. The Google research showed that women unfamiliar with computer science also associated it with ‘difficult’. We can build confidence in women by telling them that computer science is something you learn, not something you are born with.
Strategy 5: You don’t have to be passionate at the beginning
One myth is that you have to find your passion and pursue that. But this is nonsense, because how do you find your passion in the first place? Instead, you have to try different things, find the one you like best and are good at and then pursue that which eventually becomes your passion as you practice it more. If a boy is exposed to computer science at a much earlier age and then becomes passionate about it, a girl may think ‘I don’t have as much passion as him for computer science, therefore I don’t think it is for me’. This is wrong. The boy gained passion after being exposed to computer science and learning more about it. We have to teach women that they should try things first and passion will come later.
Strategy 6: The natural effect
There will be some critical point at which a natural effect will take over. When you have more women in computer science, more women will be encouraged to go into computer science for various reasons. At this point, the percentage of women will grow faster because of this feedback effect.
Strategy 7: Mindset
We should teach the growth mindset to minorities. In the book ‘Mindset’, Dr. Dweck talks about minorities and their mindsets. Society portrays minorities with less abilities – in particular in technical fields: ‘Women are not as good as math as men’, you might hear. Minorities with a fixed mindset (i.e. they believe that their abilities are fixed) will be more likely to believe the stereotypes and will likely not pursue technical fields because they believe they are inherently not good at it. People with a growth mindset, however, believe that their abilities are not fixed and that they can continuously improve their abilities. Therefore, they will be more likely to pursue a career in tech and stick with it despite the unfair biases and setbacks they may have.