The impact of the pandemic and school closures on non-cognitive characteristics: Evidence from PISA
Bibliographic record
Abstract
This study examines the impact of the COVID-19 pandemic on non-cognitive skills using survey data from the Programme for International Student Assessment (PISA). We analyzed survey findings from PISA 2022 and compared these results with previous test administrations to evaluate the influence of school closures on non-cognitive outcomes across Canada, the United States, Australia, New Zealand, and Europe. Our findings indicate that students in the 2022 PISA cohort experienced a contrasting short- and-long term effect on their sense of belonging. In the short term, there was a loss of 0.040 standard deviations (SD) in their sense of belonging to school. However, in the long term, there is evidence of a recovery of 0.049 SD, particularly in the USA and Canada. Additionally, the pandemic was associated with a 0.08 SD decline in growth mindset and a 5 % reduction in the likelihood of being frequently bullied, compared to past student cohorts. The study reveals that the COVID-19 pandemic disproportionately affected non-cognitive skills among girls, migrants, and economically disadvantaged students, exacerbating bullying and prompting a shift towards a fixed mindset, particularly among female students.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".