The long‐run educational benefits of high‐achieving classrooms
Bibliographic record
Abstract
Abstract This paper examines how placement in high‐achieving classrooms within high school impacts students’ short‐ and longer‐term academic outcomes. Our setting is a large and selective Chinese high school, where first‐year students are separated into high‐achieving and regular classrooms based on their performance on a standardized exam. Classrooms differ in terms of peer ability, teacher quality, and class size, as well as level and pace of instruction. Using newly collected administrative data and a regression discontinuity design, we show that high‐achieving classrooms improve math test scores by 23% of a standard deviation, with effects persisting throughout the 3 years of high school. Impacts on performance in Chinese and English language subjects are more muted. Importantly, we find that high‐achieving classrooms raise enrollment in elite universities by 17 percentage points, as they substantially increase scores on the national college entrance exam—the sole determinant of university admission in China. We provide suggestive evidence that the most likely mechanism driving our results is exposure to higher‐quality teachers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".