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.
What is at stake without high-stakes exams? Students' evaluation and admission to college at the time of COVID-19.
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作者:Arenas Andreu, Calsamiglia Caterina, Loviglio Annalisa
| 期刊: | Economics of Education Review | 影响因子: | 1.800 |
| 时间: | 2021 | 起止号: | 2021 Aug;83:102143 |
| doi: | 10.1016/j.econedurev.2021.102143 | ||
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