Centre assessment grades in 2020: a natural experiment for investigating bias in teacher judgements

Louis Magowan ()
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Louis Magowan: London School of Economics and Political Science (LSE)

Journal of Computational Social Science, 2023, vol. 6, issue 2, No 8, 609-653

Abstract: Abstract The COVID-19 pandemic meant that, in 2020, students in England were unable to sit their examinations and instead received predicted grades, or “centre assessment grades” (CAGs), from their teachers to allow them to progress. Using the Grading and Admissions Data for England (GRADE) dataset for students from 2018 to 2020, this study treats the use of CAGs as a natural experiment for causally understanding how teacher judgements of academic ability may be biased according to the demographic and socio-economic characteristics of their students. A variety of machine learning models were trained on the 2018–19 data and then used to generate predictions for what the 2020 students were likely to have received had their examinations taken place as usual. The differences between these predictions and the CAGs that students received were calculated and then averaged across students’ different characteristics, revealing what the treatment effects of the use of CAGs were likely to have been for different types of students. No evidence of absolute negative bias against students of any demographic or socio-economic characteristic was found, with all groups of students having received higher CAGs than the grades they were likely to have received had they sat their examinations. Some evidence for relative bias was found, with consistent, but insubstantial differences being observed in the treatment effects of certain groups. However, when higher-order interactions of student characteristics were considered, these differences became more substantial. Intersectional perspectives which emphasise interactions and sub-group differences should be used more widely within quantitative educational equalities research.

Keywords: Quantitative education research; Bias in teacher judgements; Educational inequality during COVID-19; Machine learning for causal inference; GRADE data; Intersectionality (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42001-023-00206-x

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