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E-learning engagement gap during school closures: differences by academic performance

Author

Listed:
  • Josep Amer-Mestre
  • Alaitz Ayarza-Astigarraga
  • Marta C. Lopes

Abstract

We study the impact of COVID-19 school closures on differences in online learning usage by regional academic performance. Using data from Google Trends in Italy, we find that during the first lockdown, regions with a previously lower academic performance increased their searches for e-learning tools more than higher-performing regions. Analysing school administrative and survey data before the pandemic, we find that both teachers and students in lower performing regions were using no less e-learning tools than higher performing ones. These two findings suggest that the COVID-19 shock widened the e-learning usage gap between academically lower and higher-performing regions. Exploiting the regional variation in school closure mandates during the 2020–2021 academic year, we report that the patterns detected after the first lockdown were no longer present. Regions with different previous academic performance had the same response in terms of online learning usage when faced with stricter school closures.

Suggested Citation

  • Josep Amer-Mestre & Alaitz Ayarza-Astigarraga & Marta C. Lopes, 2024. "E-learning engagement gap during school closures: differences by academic performance," Applied Economics, Taylor & Francis Journals, vol. 56(3), pages 337-359, January.
    • Handle: RePEc:taf:applec:v:56:y:2024:i:3:p:337-359
    DOI: 10.1080/00036846.2023.2167924
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    Cited by:

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    2. Jo Blanden & Matthias Doepke & Jan Stuhler, 2022. "Education inequality," CEP Discussion Papers dp1849, Centre for Economic Performance, LSE.

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare

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