Drivers of academic achievement in high school: Assessing the impact of COVID-19 using machine learning techniques

Education is crucial for individual and societal growth. However, it was significantly impacted by the COVID-19 pandemic, with long-lasting effects. Estimates suggest that students’ learning decreased by up to 50% compared to a typical year, though the full impact remains unclear. This paper evaluates primary AA drivers to guide efforts addressing pandemic-related educational inequities. Using government data from virtually all public high school students in a European country, we applied advanced data science methods— Multiple Linear Regression, Decision Trees, Neural Networks, Support Vector Machines, Random Forest, and Extreme Gradient Boosting—to analyze AA determinants before and during the pandemic (2019 and 2020, respectively). Our data includes the most well-known potential AA drivers across four dimensions: students, parents, schools, and teachers. Our substantive findings highlight that student age and legal guardian education were key AA drivers, while Internet access and gender gained importance during the pandemic. Additional drivers, including school size, family nationality, and socioeconomic factors (such as the rate of students receiving school support), also emerged as relevant, particularly under pandemic conditions. This study quantitatively assesses these AA determinants across two distinct academic years, providing nuanced insights into the impact of COVID-19 on education. These results offer valuable guidance for policymakers to implement interventions addressing evolving needs and disparities exacerbated by remote learning. This study contributes to AA literature by utilizing extensive data and machine learning models to reveal enduring and emerging factors affecting educational outcomes during challenging times.