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Record W2737439456 · doi:10.1162/edfp_a_00263

The Impact of Schooling Intensity on Student Learning: Evidence from a Quasi-Experiment

2018· article· en· W2737439456 on OpenAlexaff
Vincenzo Andrietti, Xuejuan Su

Bibliographic record

VenueEducation Finance and Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGraduation (instrument)Natural experimentQuantile regressionGermanTest (biology)Mathematics educationDemographic economicsFixed effects modelPsychologyAcademic achievementEconometricsEconomicsPanel dataStatisticsGeographyMathematics

Abstract

fetched live from OpenAlex

This paper uses a quasi-natural policy experiment in Germany, the G8 reform, to examine the impact of schooling intensity on student learning. The G8 reform compresses secondary school for academic-track students from nine to eight years, while holding fixed the overall academic content and total instruction time required for graduation, resulting in a higher schooling intensity per grade. Using German extension of the Programme for International Student Assessment data, we find that this reform improves test scores on average, but the effect differs across subgroups of students. The reform effect is larger for girls than for boys, for students with German-born parents than for those with immigrant parents, and for students having more books at home. The heterogeneous reform effects cannot be explained by changes in observed channels. Instead, quantile regression results suggest that unobserved heterogeneity plays an important role: Whereas high-performing students significantly improve their test scores, the lowest-performing students hardly improve at all after the reform. We interpret the unobserved heterogeneity as reflecting students’ capability to cope with the increase in schooling intensity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.446
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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