Drivers of Academic Achievement in High School - Assessing the Impact of COVID-19 Using Machine Learning Techniques

Aug 4, 2026

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Education is a cornerstone for individual and societal development, yet the COVID-19 pandemic caused major, persistent disruptions that reduced students’ learning and deepened inequities. This paper evaluates primary academic achievement (AA) drivers using government data covering virtually all public high school students in a European country. Multiple Linear Regression, Decision Trees, Neural Networks, Support Vector Machines, Random Forest, and Extreme Gradient Boosting are applied to determinants in 2019 vs. 2020. Results show student age and legal guardian education as key AA drivers, while internet access and gender increase in importance during the pandemic, alongside school and socioeconomic factors. Findings guide policymakers on targeted interventions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Abstract

Introduction

Education and academic achievement context

COVID-19 and disruptions in learning

Motivation for machine learning-based analysis

Drivers of academic achievement in high school: Assessing the impact of COVID-19 using machine learning techniques Ana Beatriz-Afonso1*, Frederico Cruz-Jesus1, Catarina Nunes1, Mauro Castelli1, Tiago Oliveira1 and Luísa Canto e Castro2 1NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070–312 Lisboa, Portugal // 2Faculdade de Ciências, Centro de Estatística e Aplicações e Fundação Francisco Manuel dos Santos, Universidade de Lisboa, Lisbon, [Portugal // aafonso@novaims.unl.pt //](Portugal // aafonso@novaims.unl.pt //)[ ](Portugal // aafonso@novaims.unl.pt //)[fjesus@novaims.unl.pt // cnunes@novaims.unl.pt // mcastelli@novaims.unl.pt // toliveira@novaims.unl.pt//](fjesus@novaims.unl.pt // cnunes@novaims.unl.pt // mcastelli@novaims.unl.pt // toliveira@novaims.unl.pt//) [lloura@ffms.pt](lloura@ffms.pt) *Corresponding author (Submitted November 21, 2023; Revised October 29, 2024; Accepted December 17, 2024) ABSTRACT: 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 ofCOVID-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. Keywords: Education, Academic achievement, Data science, COVID-19 1. Introduction Education is the first pillar of social rights in the European Union (European Commission, 2017), deeply intertwined with all aspects of life and well-being. It aligns with the United Nations ’ target of ensuring all youth complete primary and secondary education with relevant skills by 2030 (United Nations, 2015) . Education not only enhances job prospects and income but also provides access to culture and knowledge, equipping individuals to navigate life ’s complexities. Education is crucial for addressing economic and social challenges. Education fosters competitive and adaptable societies by supplying knowledge and skilled individuals (OECD, 2019) . Historically, pandemics like the Spanish Flu of 1918 have significantly impacted education, leading to innovations like distance learning (Spielman & Sunavala-Dossabhoy, 2021) . While diseases like HIV and Ebola had severe effects on society, COVID-19 proved to be on a different scale entirely. COVID-19 affected virtually every country worldwide, resulting in widespread lockdowns, travel restrictions, and drastic changes in daily life for billions of people. Its global reach led to prof

FAQs

What data and modeling approach does the study use?

The study uses government data covering nearly all public high school students in a European country and applies multiple machine learning models, including regression, tree-based models, neural networks, support vector machines, random forests, and extreme gradient boosting.

Which academic achievement drivers matter most before and during COVID-19?

Student age and legal guardian education are key drivers overall. During the pandemic, internet access and gender gain greater importance, alongside factors such as school size and socioeconomic conditions.

How does the study compare the impact of COVID-19 over time?

It analyzes determinants across two distinct academic years—2019 and 2020—to identify both enduring and emerging factors affecting educational outcomes under pandemic conditions.

Aug 4, 2026

Upload by Quinn