Grade expectations: how well can past performance predict future grades?

Autor:	Wyness, Gill, Macmillan, Lindsey, Anders, Jake, Dilnot, Catherine
Předmět:	STUDENTS MACHINE learning SOCIAL status SCHOOL children ACADEMIC achievement
Zdroj:	Education Economics; Aug2023, Vol. 31 Issue 4, p397-418, 22p, 10 Charts, 3 Graphs, 1 Map
Abstrakt:	Students in the UK apply to university with teacher-predicted examination grades, rather than actual results. These predictions have been shown to be inaccurate, and to favour certain groups, leading to concerns about teacher bias. We ask whether it is possible to improve on the accuracy of teachers' predictions by predicting pupil achievement using prior attainment data and machine learning techniques. While our models do lead to a quantitative improvement on teacher predictions, substantial inaccuracies remain. Our models also underpredict high-achieving state school pupils and low socio-economic status pupils, suggesting they have more volatile education trajectories. This raises questions about the use of predictions in the UK system. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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