2026-04-13T01:32:16Zhttps://recercat.cat/oai/request
oai:recercat.cat:2445/1862092025-12-05T04:23:45Zcom_2072_1057col_2072_478809col_2072_478917
RECERCAT author Arenas Jal, Andreu author Calsamiglia, Caterina author Loviglio, Annalisa 2022-06-03T08:54:25Z2024-08-01T05:10:07Z2021-08-012022-06-03T08:54:25Z 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. cc-by-nc-nd (c) Elsevier, 2021 https://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess COVID-19 Avaluació educativa Proves d'accés a la universitat Anàlisi de regressió COVID-19 Educational evaluation Entrance examinations for universities Regression analysis What is at stake without high-stakes exams? Students' evaluation and admission to college at the time of COVID-19 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion