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Record W4306950496 · doi:10.37074/jalt.2022.5.s2.8

Determinants of Mathematical Educational Achievement in Cameroon

2022· article· en· W4306950496 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Learning & Teaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsCarleton University
Fundersnot available
KeywordsStudent achievementMathematics educationFrenchSample (material)PsychologyAcademic achievementGeographyChemistry

Abstract

fetched live from OpenAlex

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.

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.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.286
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.332
Teacher spread0.312 · 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