Primary and Secondary School Students’ Academic Achievement Trajectories Before and During Covid-19
Bibliographic record
Abstract
The COVID-19 pandemic had dramatic impacts on educational systems worldwide. The current study aimed to investigate whether some students experienced different patterns of mathematics and language arts achievement before and during the pandemic. Therefore, we used latent class trajectory analyses to investigate the distinct trajectories of 50,100 primary and 14,600 secondary school students' academic achievement between fall of 2018 and spring of 2021. We identified four classes of primary school achievement trajectories and five classes of secondary school student achievement. Overall, with the exception of a minority of students in at-risk trajectories, we found that most students' achievement remained relatively stable over time. Furthermore, all trajectories reflected trends that already appeared to be present prior to the pandemic. We also examined sociodemographic characteristics associated with students' membership in different classes. The results revealed that boys and students with special needs were overrepresented in at-risk achievement trajectories. Although our results highlight that the consequences of COVID-19 for students' achievement were less dramatic than expected, they also point out the need to develop interventions for at-risk 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 | 0.001 | 0.003 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".