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Record W3169653648 · doi:10.21203/rs.3.rs-568605/v1

The Impacts of Remote Learning in Secondary Education: Evidence from Brazil during the Pandemic

2021· preprint· en· W3169653648 on OpenAlexaboutno aff
Guilherme Lichand, Carlos Alberto Dória, Onício Leal Neto, João Cossi

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersInter-American Development Bank
KeywordsQuarter (Canadian coin)PandemicContext (archaeology)PsychologyGeographyCoronavirus disease 2019 (COVID-19)DemographyMedicineSociology

Abstract

fetched live from OpenAlex

Abstract Background: The transition to remote learning in the context of Covid-19 could lead to dramatic setbacks for school enrollment and learning outcomes, especially in developing countries – where a multiplicity of challenges, from limited connectivity to little support from parents, are bound to limit its effectiveness. To date, however, no study has rigorously documented the educational impacts of remote learning relative to in-person classes within primary and secondary education. Quantifying the extent of those losses, as well as the extent to which resuming in-person classes in the pandemic could at least partially offset them, is urgent, as governments worldwide struggle evaluating the trade-offs between the health and educational risks of reopening schools, with vaccination rates still dragging.Methods: Taking advantage of the fact that São Paulo featured in-person classes for the lion’s share of the first school quarter of 2020, but not thereafter, we estimate the effects of remote learning on secondary education, using a differences-in-differences strategy that contrasts variation in dropout risk and standardized test scores between the first and the last school quarters in 2020 to that in 2019, when all classes were in-person. We estimate heterogeneous effects by grade, student characteristics and school characteristics. We also estimate intention-to-treat (ITT) effects of reopening schools in the pandemic through a differences-in-differences strategy, contrasting differences between middle- and high-school students within municipalities that authorized in-person classes to partially return for the latter over the last quarter of 2020, to those within municipalities that did not.Findings: Dropout risk increased by 365% under remote learning. While risk increased with local disease activity, most of it can be attributed directly to the absence of in-person classes:we estimate that dropout risk increased by no less than 247% across the State, even at the low end of the distribution of per capita Covid-19 cases. Average standardized test scores decreased by 0.32 standard deviation, as if students had only learned 27.5% of the in-person equivalent under remote learning. Learning losses did not systematically increase with local disease activity, attesting that they are in fact the outcome of remote learning, rather than a consequence of other health or economic impacts of Covid-19. Authorizing schools to partially reopen for in-person classes increased high-school students’ test scores by 20% relative to the control group.Interpretation: Results show that the societal costs of keeping schools closed in the pandemic are very large. While the learning losses that we document are at least as large as those documented in developed countries on the aftermath of the first Covid-19 wave, the dramatic surge in dropout risk is unique to developing countries. Such massive impacts are likely to bring about long-lasting effects on employment, productivity, and poverty levels. Our findings highlight that reopening schools under safe protocols can prevent those costs from growing even larger. They also caution against recent enthusiasm for remote learning in primary and secondary education outside the context of Covid-19.Funding: Research funded by the Inter-American Development Bank (IADB) as part of a partnership between IADB and the São Paulo State Education Secretariat.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.139
GPT teacher head0.537
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2021
Admission routes1
Has abstractyes

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