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.
English
COVID-19; Avaluació dels estudiants; Anàlisi de regressió; COVID-19; Rating of students; Regression analysis
Institut d’Economia de Barcelona
Reproducció del document publicat a: https://ieb.ub.edu/wp-content/uploads/2021/04/Doc2021-04.pdf
IEB Working Paper 2021/04
[WP E-IEB21/04]
cc-by-nc-nd, (c) Arenas Jal et al., 2021
http://creativecommons.org/licenses/by-nc-nd/3.0/es/