Student and School Characteristics Associated With <scp>COVID</scp>‐19‐Related Learning Decline Among Middle and High School Students in K‐12 Schools*
Bibliographic record
Abstract
BACKGROUND: COVID-19-disrupted schools, including shifts to virtual learning which may have impacted academic progress. This study assessed characteristics associated with changes in academic grades (before and during the pandemic) for different learning modalities for US students ages 13-19. METHODS: Students (N = 2152) completed a web survey on school-related experiences during the 2020-2021 school year. County social vulnerability and SARS-CoV-2 transmission data were merged with survey data. Multivariable logistic regression analysis for grade change was conducted with student and school characteristics for each learning modality, controlling for community characteristics. RESULTS: Greater proportions of remote/virtual (34.4%) and hybrid (30.1%) learning students reported grade decline compared to in-person students (19.9%). Among in-person students, odds of reporting same/improved grades were 65% lower among non-Hispanic black students and 66% lower among non-Hispanic students from other races, compared to non-Hispanic white students. Among hybrid students, odds of reporting same/improved grades for students reporting anxiety were 47% lower than students without anxiety, and odds of reporting same/improved grades among students reporting substance use were 40% lower than students not reporting substance use. Among remote/virtual students, odds of reporting same/improved grades among students with depression were 62% lower than odds of students not reporting depression symptoms. Remote/virtual students who received school-provided educational services also had 1.55 times the odds of reporting same/improved grades, compared to remote/virtual students not receiving these services. CONCLUSIONS: Academic grades were negatively impacted during COVID-19 and learning mode may have contributed. Understanding these impacts is critical to student health and academic achievement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".