Examining the Effect of Automatic Promotion on Students’ Learning Achievements in Uganda’s Primary Education
Bibliographic record
Abstract
This study employed a difference-in-differences analysis technique to estimate the average treatment effect ofautomatic promotion on students’ cognitive learning outcomes in Uganda’s primary education. Regression resultsindicate a positive policy effect on learning achievements in literacy and numeracy at primary three (P3) and primarysix (P6). Specifically, the implementation of automatic promotion policy has translated in to an increase in learningoutcomes in reading and mathematics at P3 and P6, all statistically significant at conventional levels. Decomposingthe effect along gender and school location (rural or urban) dimensions reveals positive and statistically significanteffect on literacy and numeracy in both grades. The effect on students’ scores in rural areas is higher than that onstudents’ in urban schools. In terms of gender, the effect is relatively similar for female students and their malecounterparts. These results are contrary to the popular belief among many Ugandans, but consistent with earlierscholarly works that have attributed automatic promotion with positive impact on learning outcomes.
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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 | 0.004 | 0.001 |
| 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".