Determinants of Mathematical Educational Achievement in Cameroon
Bibliographic record
Abstract
This article presents a quantitative analysis of the determinants of student scores on a standardized mathematics assessment in Cameroon, a sub-Saharan African country with Francophone and Anglophone school systems. Using the mathematics component of the 2014 Programme d’Analyse des Systèmes Educatifs de la CONFEMEN survey, we examined the importance of community, school and classroom resourcing, teacher attributes, student characteristics, and family circumstances. Our results generally suggest that both school and family factors play a role in determining student achievement in mathematics. We found that student mathematics scores are highest for males, younger students, non-grade repeaters, urban students, when teachers are better resourced, and when students come from well-off family situations with parents who are able to read. Analyses of sub-samples and an expanded sample (with interaction effects) generally corroborate these results but also reveal further differences, including between the Francophone and Anglophone school systems. Finally, the results indicate that the kindergarten program is not systematically associated with better mathematics test scores, suggesting that this policy may need further study and modification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".