L’enseignement‐apprentissage des sciences humaines: quelles finalités, quelles difficultés et quelles compétences professionnelles? Résultats d’une enquête auprès de futurs enseignants québécois du primaire
Bibliographic record
Abstract
Le texte présente les résultats d’une enquête par questionnaire menée auprès d’étudiants du baccalauréat en enseignement primaire de l’Université de Sherbrooke sur les finalités, les apprentissages, les difficultés et les compétences en enseignement. Les résultats révèlent que, pour les étudiants interrogés, cet enseignement vise surtout la socialisation des élèves. Selon les répondants, le caractère abstrait des contenus présente une difficulté pour les élèves alors que la maîtrise des savoirs à enseigner représente la principale difficulté pour les enseignants. La compétence en enseigne‐ ment est perçue comme reposant sur des attitudes favorables et une maîtrise de sa‐ voirs à enseigner. Enfin, les résultats révèlent une variation faible selon l’année de formation des étudiants. Mots‐clés: enseignement de sciences humaines, futurs enseignants, primaire. We present the results of a questionnaire given to students in the B. Ed. primary edu‐ cation program at the Université de Sherbrooke about the aims of teaching, as well as what they need to learn,what difficulties are involved in teaching and what compet‐ ences are required. The results show that for these respondents, teaching is above all intended to socialize students. They believe that the abstract nature of the subject matter presents difficulties for students, while the main challenge for teachers is mas‐ tering the knowledge to be taught. They see competence in teaching as based on fav‐ ourable attitudes and a mastery of the subject matter. The results showed little varia‐ tion between cohorts students based on many key attitudes. Key words: teaching social sciences, future teachers, primary
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".