.

2021 April 27;118(17):e2022376118.


doi: 10.1073/pnas.2022376118.

Learning loss due to schoolhouse closures during the COVID-19 pandemic

Affiliations



  • PMID:

    33827987


  • PMCID:

    PMC8092566


  • DOI:

    10.1073/pnas.2022376118

Gratuitous PMC article

Learning loss due to school closures during the COVID-19 pandemic



Per Engzell
 et al.



Proc Natl Acad Sci U S A.



.

Complimentary PMC commodity

Abstract

Suspension of face-to-face up instruction in schools during the COVID-19 pandemic has led to concerns virtually consequences for students’ learning. So far, information to study this question have been limited. Here we evaluate the consequence of schoolhouse closures on principal schoolhouse performance using exceptionally rich data from The Netherlands (n
≈ 350,000). Nosotros use the fact that national examinations took identify before and after lockdown and compare progress during this period to the same catamenia in the 3 previous years. The Netherlands underwent only a relatively short lockdown (8 wk) and features an equitable organisation of school funding and the globe’s highest charge per unit of broadband access. Still, our results reveal a learning loss of almost 3 percentile points or 0.08 standard deviations. The effect is equivalent to one-fifth of a school year, the same period that schools remained airtight. Losses are up to 60% larger among students from less-educated homes, confirming worries about the uneven toll of the pandemic on children and families. Investigating mechanisms, we notice that virtually of the event reflects the cumulative impact of noesis learned rather than transitory influences on the day of testing. Results remain robust when balancing on the estimated propensity of handling and using maximum-entropy weights or with stock-still-furnishings specifications that compare students within the same school and family. The findings imply that students made little or no progress while learning from abode and suggest losses fifty-fifty larger in countries with weaker infrastructure or longer school closures.

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Keywords:

COVID-19; digital separate; learning loss; schoolhouse closures; social inequality.

Conflict of interest statement

The authors declare no competing interest.

Figures



Fig. 1.

Distribution of testing dates 2017 to 2020 and timeline of 2020 school closures. Density curves evidence the distribution of testing dates for national standardized assessments in 2020 and the iii comparing years 2017 to 2019. Vertical lines show the first and end of nationwide school closures in 2020. Schools closed nationally on March 16 and reopened on May 11, afterward 8 wk of remote learning. Our difference-in-differences design compares learning progress between the two testing dates in 2020 to that in the 3 previous years.

Fig. 2.



Fig. 2.

Divergence in test scores 2017 to 2020. Density curves show the difference betwixt students’ percentile placement betwixt the commencement and the second test in each of the years 2017 to 2020. Note that this graph does not adjust for confounding due to trends, testing appointment, or sample composition, which we address in subsequent analyses using a variety of techniques.

Fig. 3.



Fig. 3.

Estimates of learning loss for the whole sample and by subgroup and test. The graph shows estimates of learning loss from a difference-in-differences specification that compares learning progress between the two testing dates in 2020 to that in the iii previous years. Statistical controls include time elapsed between testing dates and a linear trend in year. Point estimates are with 95% confidence intervals, with robust standard errors accounting for clustering at the school level. One percentile point corresponds to


0.025 SD. Where non otherwise noted, furnishings refer to a composite score of math, spelling, and reading. Regression tables underlying these results tin can be found in


SI Appendix, section seven.1

.

Fig. 4.



Fig. 4.

Robustness to specification. The graph shows estimates of learning loss for the whole sample and separately by parental pedagogy, using a diverseness of adjustments for loss to follow-up. Point estimates are with 95% conviction intervals, with robust standard errors accounting for clustering at the school level. For details, see
Materials and Methods
and


SI Appendix, sections iv.two and 7.4–7.8

.

Fig. 5.



Fig. 5.

Noesis learned vs. transitory influences. The graph compares estimates for the composite achievement score in our main analysis (lite color) with test not designed to assess curricular content (night color). Both sets of estimates refer to our baseline specification reported in Fig. iii. Indicate estimates are with 95% confidence intervals, with robust standard errors bookkeeping for clustering at the school level. For details, see
Materials and Methods
and


SI Appendix, sections 3.1 and 7.8

.

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Source: https://pubmed.ncbi.nlm.nih.gov/33827987/