Arenas Andreu, Calsamiglia Caterina, Loviglio Annalisa
Universitat de Barcelona and Institut d'Economia de Barcelona (IEB), Spain.
ICREA, IPEG and IZA, Spain.
Econ Educ Rev. 2021 Aug;83:102143. doi: 10.1016/j.econedurev.2021.102143. Epub 2021 Jun 25.
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.
2020年新冠疫情的爆发抑制了面对面教育，并限制了考试进行。在全球许多国家，学年末举行的高风险考试决定着大学录取。本文研究了利用学校历史数据和高风险考试成绩来训练一个模型，以便根据春季可用数据预测高风险考试的影响。结果发现，最透明且准确的模型是一个以高中平均绩点作为主要预测指标的线性回归模型。对预测结果的进一步分析反映了高风险考试与不同人群亚组在高中时的平均绩点之间的关系。预测分数对女性和社会经济地位较低的个体略有优势，他们在高风险考试中的表现相对比在高中时更差。我们首选的模型解释了高风险考试样本外变异的约50%。平均而言，使用预测分数得出的学生排名与实际排名相差近17个百分位。这表明，要么高风险考试考察了高中成绩未衡量的个人技能，要么高风险考试是对同一技能的一种有噪声的衡量方式。
