Le sentiment de compétence : modérateur du lien entre le QI et le rendement scolaire en mathématiques
Bibliographic record
Abstract
La présente étude a pour but de vérifier si le QI et le sentiment de compétence intera‐ gissent lorsque l’élève doit performer à l’école et si, en l’occurrence, cette interaction est à son tour modérée par l’âge et le sexe des participants. Afin de vérifier ces hy‐ pothèses, les variables suivantes ont été étudiées chez 928 élèves de souche franco‐ canadienne fréquentant des écoles montréalaises de niveau secondaire : la moyenne en mathématiques, le QI, le sentiment de compétence en mathématiques, l’âge, le sexe et le statut socioéconomique. Tel que prévu, le QI et le sentiment de compétence en mathématiques corrèlent de façon positive et significative avec la moyenne en mathématiques de l’élève. Les analyses montrent également une interaction significa‐ tive entre le QI et le sentiment de compétence. Une fois décomposée, cette interaction indique que chez les élèves qui ont un sentiment de compétence élevé, la valeur prédictive du QI est plus élevée, alors que la relation entre le QI et la performance ne change pas de façon significative chez les élèves qui présentent un faible sentiment de compétence. Enfin, ni l’âge ni le sexe de l’élève n’influencent l’interaction entre le QI et le rendement scolaire, pas plus qu’ils ne sont corrélés avec le rendement scolaire. Les implications cliniques de cette recherche sont discutées. Mots‐clés : rendement scolaire en mathématiques, QI, sentiment de compétence, interaction, modérateur, âge, sexe The purpose of the present study was to establish whether students’ self competence beliefs and IQ interact when they need to perform at school and if so, whether this interaction is moderated by age and/or sex. The mean grade in mathematics, self‐ competence beliefs in mathematics, IQ, age, sex and socioeconomic status of 928 French Canadian students from Montreal high schools were analyzed. As predicted, the students’ IQ and self‐competence beliefs in mathematics correlated in a positive and significant way with their average grade in mathematics. Analyses also con‐ firmed a significant correlation between the IQ and students’ self‐competence beliefs. When broken down, this interaction shows that for students with high self‐ competence beliefs, the predictive value of IQ is higher, but for students with low self‐ competence beliefs, the relationship between IQ and performance does not change significantly. Finally, age and sex do not influence the interaction between the IQ and students’ self‐competence beliefs, nor are they correlated with academic performance. Clinical implications are discussed. Key words: academic achievement in mathematics, IQ, student’s self‐competence beliefs, moderator, age, sex
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".