It is disheartening to see that poorer students have been more likely to be downgraded for their Scottish Qualifications Authority (SQA) results this year, despite the early warnings from teachers and trade unions. In April, I wrote about the risks of further entrenching multiple privileges as a result of teacher bias in the estimation process and the SQA’s reliance on a school’s historical performance to adjust grades.

On the one hand, it is important to value a teacher’s professional judgement - we all worked hard to gather evidence and do our best to remain consistent in our assessment of our students. On the other hand, no matter how well-intended we are, it is necessary to recognise that none of us is immune to implicit bias, especially in times of stress caused by an unprecedented pandemic.

Background: Poorest far more likely to have Higher pass downgraded

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As an anti-racist researcher and activist, I was keen to see what the SQA did to assess and mitigate the impact of racial bias in teachers’ estimates. Its Equality Impact Assessment (EqIA) acknowledges the risk of bias influencing estimates and it cites some research, which hints that racial bias can have an impact. The EqIA uses data from the 2019 teacher estimates and exam results as an equality impact exercise to assess how likely it is that gender, socioeconomic background and additional support needs would lead to under- or over-estimation of results. What concerned me the most was the SQA’s excuse for not analysing ethnicity data in the same way as the other protected characteristics:

“Race: around 90 per cent of candidate entries were either ‘white - British’ or ‘white - other’, with the largest other ethnicity (Asian - Pakistani) being 2.5 per cent. Thus, each non-white ethnicity is a small dataset - and small datasets are difficult to analyse and draw firm conclusions from, as the data tends to be variable, meaning it is often not possible to distinguish the natural variation found in small datasets from meaningful signals.” (p.20)

SQA results: The impact of racial bias

Whether the SQA intended it or not, the assertion that ethnicity datasets were “unreliable” obscures the significance of racial bias in estimating exam results. From the 2011 census, ethnic minorities make up 4 per cent of the total Scottish population, so ethnic minority datasets are naturally going to be small. In my recent article, I draw on Critical Race Theory to prove that data is often manipulated (and ignored) to veil racial inequalities and fuel racist discourses. Equally small datasets were used to produce the Race Equality Framework for Scotland and, in 2010, to claim that white pupils had higher attainment gaps than Asian pupils. Clearly, the data is only good enough when it supports the policymaker’s agenda. Likewise, the Scottish government’s failure to produce reliable ethnicity data on the disproportionate impacts of Covid-19 let ethnic minorities down when we had no official evidence to defend the additional precautions needed in our workplaces.

If they intend to commit to race equality, institutions need to stop shying away from consistently gathering and analysing ethnicity data. Encouragingly, the EqIA does conclude the need to explore better ways of gathering data, since the SQA does not hold ethnicity data for the 2020 results. More importantly, refusal to properly analyse existing ethnicity data, and uncover uncomfortable truths about racial inequalities, allows racism and intersectional inequalities to persist. Implicit bias training alone (especially the quick-fix online training rolled out on SQA Academy) will not be enough to reduce racial bias.

We need to take a closer look at the ways our education system - in its assessment procedures, policies and curriculum - continues to uphold intersectional inequalities and privileges.

Mélina Valdelièvre is a secondary school teacher in Glasgow, a member of the STUC Black Workers’ Committee and the co-founder of The Anti-Racist Educator