Building Resilient Education Systems: Evidence from Large-Scale Randomized Trials in Five Countries
Bibliographic record
Abstract
Education systems need to withstand frequent shocks, including conflict, disease, natural disasters, and climate events, all of which routinely close schools.During these emergencies, alternative models are needed to deliver education.However, rigorous evaluation of effective educational approaches in these settings is challenging and rare, especially across multiple countries.We present results from large-scale randomized trials evaluating the provision of education in emergency settings across five countries: India, Kenya, Nepal, Philippines, and Uganda.We test multiple scalable models of remote instruction for primary school children during COVID-19, which disrupted education for over 1 billion schoolchildren worldwide.Despite heterogeneous contexts, results show that the effectiveness of phone call tutorials can scale across contexts.We find consistently large and robust effect sizes on learning, with average effects of 0.30-0.35standard deviations.These effects are highly cost-effective, delivering up to four years of high-quality instruction per $100 spent, ranking in the top percentile of education programs and policies.In a subset of trials, we randomized whether the intervention was provided by NGO instructors or government teachers.Results show similar effects, indicating scalability within government systems.These results reveal it is possible to strengthen the resilience of education systems, enabling education provision amidst disruptions, and to deliver cost-effective learning gains across contexts and with governments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".