Challenge/Competence Appraisal by Swiss Two-Way Immersion Teachers of the “Cursus bilingue/Bilingualer Studiengang” in Their Professionalization Process and Career-Entry Stage Implications
Bibliographic record
Abstract
The growing number of public one-way and two-way immersion schools (French/German) in the Swiss cantons of Bern, Jura, and Neuchâtel has resulted in an increased need for teachers who are explicitly prepared for those schools. This need led to the thoughtfully conceptualized two-way immersion curriculum “Cursus bilingue/Bilingualer Studiengang,” jointly offered by two universities of teacher education located in the French- and German-speaking areas of Switzerland. This study explores the professionalization process of future primary teachers from Bern, Jura, and the bilingual program who are confronted with professional requirements. A scientific tool, used in a longitudinal perspective (2020–2023), documents how students of the two-way immersion and the French and German programs (n = 1021) weigh these requirements in the competence and challenge dimensions. A linear mixed-effects model was used to analyze their appraisal within each dimension across time and context. Independently of the context, the competence weighting increases for all students between t1 and t3. However, the challenge weighting exhibits a peak at t2 in all contexts, maybe due to the consciousness-raising of faced challenges after half of the program. With teaching internships in two linguistic regions, two-way immersion students demonstrate a capacity for adaptability. Research-based and practitioner-informed implications for an induction program aiming to foster the further professionalization needed to master the new complexities during the career-entry stage are presented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".