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Dr Gillian Wyness evidence cited by Commons committee

14 July 2020

Dr Gillian Wyness, Deputy Director of the Centre for Education Policy and Equalising Opportunities (CEPEO) at the UCL Institute of Education (IOE), has had evidence cited by the government's Education Select Committee about the challenges of predicted grades.

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Dr Wyness’ research has shown that only 16 percent of applicants' predicted grades are accurate, with 75 percent of applicants having over-predicted grades. However, as the report goes on, “high-attaining, disadvantaged students are significantly more likely to receive pessimistic grade predictions.”

“We show that under-predicted candidates are more likely to enrol in courses for which they are over qualified than their peers. We conclude that the use of predicted rather than actual grades has important implications for student's labour market outcomes and social mobility in general.”

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Drawing on extensive research in the field, CEPEO’s submission to the Education Select Committee also highlighted the potential impact of the government's plans to calculate grades this year following the cancellation of formal exams, which could penalise “atypical” students such as high achievers in historically low-performing schools.

The government publication states, “A submission from University College London’s Centre for Education Policy and Equalising Opportunities warned that the use of historic performance data for standardisation could penalise “atypical” students such as high achievers in historically low-performing schools”.

The Centre advocates that calculated grades should be validated and externally available information should be used where possible to validate ‘atypical’ students, based on their prior achievement before grading individual students down.

The initial working paper from CEPEO, led by Richard Murphy (University of Austin, Texas) and Dr Wyness, reported research which finds that under-predicted applicants are 10 percentage points more likely than applicants whose grades were accurate or over-predicted to have applied to a university that they are over-qualified for. Such “undermatch” could lead to these students having worse outcomes in the labour market, and hence has implications for social mobility.

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