The causal effects of increased learning intensity on student achievement: Evidence from a natural experiment
Bibliographic record
Abstract
I exploit the quasi-experimental setting offered by a recent reform of the German educational system that reduced the length of academic-track high school by one year without reducing curricular content to investigate how the resulting increase in learning intensity affected student achievement. Using 2000-2009 PISA data and a differences-in-differences approach, I find robust evidence that the reform significantly improved (on average by 0.10-0.11 standard deviations) the reading, mathematics, and science literacy skills acquired by academic-track high school students upon treatment. A more direct estimate of the effects of the increased learning intensity - as measured by state- and grade-specific variation in weekly instructional hours - corroborates the latter finding. Furthermore, I find that the effect on reading skills is driven by girls, while the performance of students that experienced grade retention is significantly worsened after the reform. Finally, although there is no evidence of a significant average effect of the reform on high school grade retention, I do find evidence of heterogenous effects: High school grade retention increases significantly for students with a migration background and for students in the former East states.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".