The impact of the COVID-19 pandemic on academic performance among developmental age students: a systematic review with meta-analysis.
Bibliographic record
Abstract
Objective: The COVID-19 pandemic has disrupted educational systems worldwide, raising concerns about its impact on academic performance, particularly among developmental age students. Methods: A systematic review with meta-analysis aimed to evaluate the association between the COVID-19 pandemic and the academic performance in this population was performed according to PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus and Embase were searched on December 2023 to identify relevant studies. Both fixed and random effect models were performed. The Effect size was reported as Cohen's d with a 95% Confidence Interval. Studies' quality was assessed using the Newcastle-Ottawa scale. The protocol was registered in PROSPERO. Results: A total of 30 studies met the inclusion criteria, but only 13 could be combined in the meta-analysis. Based on a sample size of 4,893,499 students, pooled Cohen's d was -0.07 [(95% CI = -0.10; -0.03); p-value <0.001]. Subgroup analyses by subject suggested that performance in math was affected the most, Cohen's d= -0.14 [(-0.18; -0.10); p-value <0.001]. Conclusion: The findings revealed a significant negative association between the COVID-19 pandemic and academic performance among developmental age students. Interventions to mitigate the adverse effects of the pandemic on educational outcomes in this population are needed.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".