G. Wyness, L. Macmillan, Jake Anders, Catherine Dilnot
{"title":"Grade expectations: how well can past performance predict future grades?","authors":"G. Wyness, L. Macmillan, Jake Anders, Catherine Dilnot","doi":"10.1080/09645292.2022.2113861","DOIUrl":null,"url":null,"abstract":"ABSTRACT 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.","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Education Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09645292.2022.2113861","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT 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.
期刊介绍:
Education Economics is a peer-reviewed journal serving as a forum for debate in all areas of the economics and management of education. Particular emphasis is given to the "quantitative" aspects of educational management which involve numerate disciplines such as economics and operational research. The content is of international appeal and is not limited to material of a technical nature. Applied work with clear policy implications is especially encouraged. Readership of the journal includes academics in the field of education, economics and management; civil servants and local government officials responsible for education and manpower planning; educational managers at the level of the individual school or college.