Millions of Students Are (Still) Above Grade Level: Achievement and Achievement Variability in Mathematics and Reading Before and During COVID-19 in the United States
Bibliographic record
Abstract
According to a 2017 article by Peters and colleagues, millions of students have already demonstrated they know the material slated to be taught that year. Consequently, grade-level standards are unlikely to be appropriately challenging for these students. In this paper, we conceptually replicated and extended this prior study. Using data from schools that administered the Renaissance Star assessment in Fall 2018 and Fall 2021, we quantified pre-COVID and mid-COVID average achievement and variance in achievement in mathematics and reading in fifth grade and estimated the grade level of instruction needed for students. Our results indicate that (a) achievement dropped during COVID-19 relative to pre-COVID, but the drop in mathematics was larger, and (b) achievement variability increased during COVID-19, but the variability in reading was slightly more pronounced. Further, our results replicated Peters et al.’s (2017) results showing that large numbers of students still performed above grade level, and substantial variability in achievement was present within schools.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".