2022-06-03T08:54:25Z
2024-08-01T05:10:07Z
2021-08-01
2022-06-03T08:54:25Z
The outbreak of COVID-19 in 2020 inhibited face-to-face education and constrained exam taking. In many countries worldwide, high-stakes exams happening at the end of the school year determine college admissions. This paper investigates the impact of using historical data of school and high-stakes exams results to train a model to predict high-stakes exams given the available data in the Spring. The most transparent and accurate model turns out to be a linear regression model with high school GPA as the main predictor. Further analysis of the predictions reflect how high-stakes exams relate to GPA in high school for different subgroups in the population. Predicted scores slightly advantage females and low SES individuals, who perform relatively worse in high-stakes exams than in high school. Our preferred model accounts for about 50% of the out-of-sample variation in the high-stakes exam. On average, the student rank using predicted scores differs from the actual rank by almost 17 percentiles. This suggests that either high-stakes exams capture individual skills that are not measured by high school grades or that high-stakes exams are a noisy measure of the same skill.
Article
Accepted version
English
COVID-19; Avaluació educativa; Proves d'accés a la universitat; Anàlisi de regressió; COVID-19; Educational evaluation; Entrance examinations for universities; Regression analysis
Elsevier
Versió postprint del document publicat a: https://doi.org/10.1016/j.econedurev.2021.102143
Economics of Education Review, 2021, vol. 83, num. 102143
https://doi.org/10.1016/j.econedurev.2021.102143
cc-by-nc-nd (c) Elsevier, 2021
https://creativecommons.org/licenses/by-nc-nd/4.0/
Economia [1045]