The Impact of High School on University Performance: The Relationship of High School Quality and its Characteristics to the Cumulative Average of its Graduates in the First Semester of the Preparatory Year at Taibah University
Bibliographic record
Abstract
The current study investigates the impact of high school on university performance of students, using survey data of 1,795 freshmen combined with data of student record at the admission office of Taibah University. Controlling for previous achievement as well as personal and socio-economic characteristics of students, urban schools outperformed rural schools by a quarter of a point of GPA (one third of the standard deviation). Graduates of public schools outperformed graduates of private schools; perhaps because private schools focus on short-term goals, such as admission standards more than the long-run success at university. School facilities were ineffective, since schools owning purpose-built buildings added no advantages to their graduates compared to schools operating in leasing buildings. Quality of school teachers, as perceived by students, was correlated with university performance, for all of school teachers and the math teacher but not the English Language teacher. Graduates of schools following a course-based mode of study did better than graduates of the traditional high schools. The most compelling evidence on school quality comes from the fixed-effect estimates. An average student graduating from any of the schools at the top 10% on the value-added ranking of schools can achieve 1.24 standard deviations (if he is a male) and 1.15 standard deviations (if she is a female) higher than an average graduate of schools that are ranked at the lowest 10%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".