Comparaison de la perception des exigences professionnelles par les futurs enseignants du primaire à la fin des première, deuxième et troisième années de formation à l'enseignement à Zurich (Suisse)
Bibliographic record
Abstract
Nous présentons les résultats d’une recherche réalisée auprès de futurs enseignants de Zurich (Suisse) et basée sur une approche longitudinale comptant trois sessions de collecte de données. Pour ce faire, 153 étudiants d’une seule cohorte ont répondu à un questionnaire à trois moments de leur formation: en première, deuxième et troisième année. L’outil EABest-K (Keller- Schneider, 2014) comporte 29 questions couvrant sept domaines d’exigences professionnelles évaluées chacune sous trois angles par les étudiants: le degré de sollicitation d’une exigence, le sentiment de compétence vis-à-vis de cette exigence et la perception de sa pertinence. Nous concluons avec des pistes pour une recherche consécutive canado-suisse. (English version) Comparison of Future Teachers’ Perception of Professional Requirements at the End of Year One, Two and Three of Their Primary Teacher Education Program in Zurich (Switzerland) We present the results of a research conducted among future Swiss teachers from Zurich (Switzerland). Based on a longitudinal approach involving three sessions of data collection, 153 students from a single cohort completed a questionnaire at three points during their teacher training: in the first, second and third year. The EABest-K tool (Keller-Schneider, 2014) consists of 29 questions covering seven areas of professional requirements, each evaluated from three angles by the students: the degree to which a requirement is considered as a challenge, the experience of competence with regard to this requirement and the perception of its relevance. We conclude with avenues for a consecutive Canadian-Swiss research project.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".