Mathematic Achievement of Canadian Private School Students
Bibliographic record
Abstract
Very little Canadian research has examined the academic achievement of private school students. Data from The Programme for International Student Assessment (PISA) 2003 were used to examine the achievement of private school students. The study found that private school students outperformed their public school peers. In addition, the students’ morale, motivation, interest in mathematics, expected education, effort invested in the PISA test, and socioeconomic status were significantly and positively related to their academic performance. Surprisingly, the cost of their tuition fees, reported hours spent on math homework, sense of belonging, and higher ratio of instructional time on mathematics were significantly, but negatively, related to the students’ math performance. Au Canada, le rendement académique d’élèves dans les écoles privées a très peu fait l’objet de recherche. Nous avons étudié les données du Programme international pour le suivi des acquis des élèves (PISA) de 2003 pour évaluer le rendement des élèves dans les écoles privées; notre étude est similaire à une étude récente portant sur le rendement académique d’élèves dans les écoles publiques au Canada (Wei, Clifton, & Roberts, 2011). Nos résultats indiquent que le rendement des élèves dans les écoles privées est supérieur à celui des élèves dans les écoles publiques. De plus, nous avons trouvé plusieurs facteurs ayant un effet significatif et positif sur le rendement académique : le moral, la motivation, l’intérêt pour les mathématiques, les attentes quant à leur scolarisation, les efforts consentis pour bien réussir au PISA et le statut socioéconomique. Étonnamment, les facteurs suivants exerçaient un effet significatif, mais négatif, sur la performance des élèves en mathématiques : le cout des frais de scolarité, les heures qu’ils disaient passer à faire des devoirs en mathématiques, le sentiment d’appartenance et un rapport plus élevé d’heures d’enseignement des mathématiques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".