Grade Expectations: How well can we predict future grades based on past performance?
Jake Anders (),
Catherine Dilnot (),
Lindsey Macmillan () and
Gill Wyness ()
Additional contact information
Catherine Dilnot: Oxford Brookes Business School
Gill Wyness: Centre for Education Policy and Equalising Opportunities, UCL Institute of Education, University College London
No 20-14, CEPEO Working Paper Series from Centre for Education Policy and Equalising Opportunities, UCL Institute of Education
The Covid-19 pandemic has led to unprecedented disruption of England's education system, including the cancellation of all formal examination. Instead of sitting exams, the class of 2020 will be assigned "calculated grades" based on predictions by their teachers. However, teacher predictions of pupil grades are a common feature of the English education system, with such predictions forming the basis of university applications in normal years. But previous research has shown these predictions are highly inaccurate, creating concern for teachers, pupils and parents. In this paper, we ask whether it is possible to improve on teachers' predictions, using detailed measures of pupils' past performance and non-linear and machine learning approaches. Despite lacking their informal knowledge, we can make modest improvements on the accuracy of teacher predictions with our models, with around 1 in 4 pupils being correctly predicted. We show that predictions are improved where we have information on 'related' GCSEs. We also find heterogeneity in the ability to predict successfully, according to student achievement, school type and subject of study. Notably, high achieving non-selective state school pupils are more likely to be under-predicted compared to their selective state and private school counterparts. Overall, the low rates of prediction, regardless of the approach taken, raises the question as to why predicted grades form such a crucial part of our education system.
Pages: 32 pages
Date: 2020-08, Revised 2020-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-edu and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://repec-cepeo.ucl.ac.uk/cepeow/cepeowp20-14.pdf First version, 2020 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ucl:cepeow:20-14
Access Statistics for this paper
More papers in CEPEO Working Paper Series from Centre for Education Policy and Equalising Opportunities, UCL Institute of Education Contact information at EDIRC.
Bibliographic data for series maintained by Jake Anders ().