Addressing Learning deficits and Student Well-being: International Insights for the Evaluation of the Dutch National Education Programme
Bibliographic record
Abstract
The COVID-19 pandemic resulted in severe disruptions to education systems worldwide. In efforts to mitigate the spread of the virus, governments enforced school closures, leading to widespread adoption of remote learning with limited preparation. These disruptions had far-reaching consequences, particularly in the form of learning deficits and harmful psychosocial effects. Students from lower socioeconomic backgrounds were especially vulnerable, facing educational and well-being challenges. Governments responded by initiating various recovery programmes. In the Netherlands, the Nationaal Programma Onderwijs (NP Onderwijs) was launched in 2021 as a large-scale effort to mitigate the COVID-19 learning deficits and deteriorations of mental well-being. As the NP Onderwijs nears completion in school year 2024-2025, this study provides a comparative perspective to support its evaluation by examining policy responses in ten countries. The report addresses four research questions using qualitative desk research, focusing on Belgium, France, Germany, Italy, Sweden, England, Canada, Japan, the United States, and the Netherlands. Data were drawn from academic literature, government documents, and international assessments such as PISA and PIRLS.
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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 | 0.100 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".