Five ways to challenge data bias and support representation in Maths - Professor Hannah Fry on #DiversityInData
Hot on the heels of her #DiversityinData webinar, run in partnership with Pearson, Professor Hannah Fry reflects on her top five takeaways to support teachers and educators in challenging data bias and supporting diversity and inclusion in Maths.
Professor Fry is a leading mathematician, best-selling author, award-winning presenter, and Professor in the Mathematics of Cities at the UCL Centre for Advanced Spatial Analysis. The webinar, attended by hundreds of teachers and educators on 30 November, explored the power of numbers and ethics within research and data collection. In addition, it explored gender inequality in STEM and ways to remove bias to ensure fair results.
1. Speak up and challenge assumptions
A lot of harm can be done by someone who is not malicious but simply isn’t thinking about the impact of their words or the data that is presented. And a lot of good is done by someone simply speaking up to point it out.
Data bias makes assumptions about the population and can omit a group of people by design. This can lead to racial, gender and exclusion of other groups of individuals such as learners with special educational needs and disabilities (SEND). It is essential that children and young people are not given the message that ‘you don’t belong’ and we must not be afraid to challenge where intentional biases appear.
Instead of reinforcing assumptions, we can spread positivity and representation through data and promote diversity through numbers and Maths education.
2. Pay careful attention to stereotypes in the classroom
Flip the stereotypes and ensure there is gender, racial and SEND representation in the portrayal of careers, home life and achievements. Use visual and verbal representation to reinforce the message. Our classrooms must represent a version of the world that we want.
Stereotypes are particularly problematic within STEM subjects. During an experiment in the 1960-70s, less than one percent of students drew a female when they were asked to draw a scientist. However, this is changing, and girls are driving the shift. By 2016, around one third of drawings were female.
Change is happening more prominently in younger students and so it is important that we pay special attention to stereotypes and provide students of all ages with diverse role models. These don’t have to be the leading mathematicians and scientists. Evidence suggests that role models that make the biggest difference to students are just one step ahead, so employ the help of diverse students one or two school years ahead, to inspire in the classroom.
3. Debunk the myth of innate ability
Boys are NOT better at Maths than girls and yet girls struggle with maths anxiety and less choose maths as a subject. Scientifically, there are very little differences in the male and female brain and very little difference in maths performance. Gender differences in confidence, imposter syndrome, maths anxiety and choices of math-intensive career choices do exist, but evidence suggests that socialisation and social influencers create these differences in the brain.
A growth mindset is essential in STEM. The false idea that some people just inherently ‘get it’ is so prevalent and will invariably tip towards a gender imbalance.
Effort must be rewarded and resilience praised. We must remind our learners that any difficulty and struggle only reflect on the complexity of the subject, NOT on the ability of the person. Constant reassurance and a reward system are needed to ensure that everyone is equally equipped to succeed in Maths.
4. Accept that you have biases so that you can act against them
The fact we are working to stop data biases being ingrained in the young people of today means we must recognise it is inevitably ingrained within ourselves as teachers and educators.
Take time to pause and recognise what you personally struggle to overcome within yourself, so you’re better prepared to question your own decisions and actions.
5. Recognise the scope of the problem
Try as much as you can to mitigate against data bias and point out issues when they arise but recognise that this is not something that can be easily solved.
Data bias is global, nuanced, pervasive, and persistent. Minor changes can easily feel meaningless and hollow when held up against the world at large but they might mean the world to the students in your classroom.
It may be chipping away at an iceberg, but when every chip is a child who feels like they belong where they previously didn’t, it’s worth it.