Do Low-Achieving Students Benefit More from Small Classes? Evidence from the Tennessee Class Size Experiment
Bibliographic record
Abstract
Recent evidence about the effects of class size on academic achievement from randomized experiments points to positive effects of small classes. However, the evidence about the mechanism producing these effects is less clear. Some scholars have argued for mechanisms that would imply greater effects of small classes for low-achieving students. This article investigates possible differential effects of small classes on achievement using data from Project STAR, a four-year, large-scale randomized experiment on the effects of class size. We examined the differential effects of small classes for students in the bottom half and bottom quarter, respectively, of their class's achievement distribution in kindergarten. Although small class effects are somewhat larger for low-achieving students in reading, the differential effects (interactions) are not statistically significant. Moreover, the small class effects for low-achieving students in mathematics are actually smaller than those for higher achieving students. Thus while there are unambiguous positive effects of small classes on achievement, there is no evidence for differentially larger effects of small classes for lower achieving 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".