Diverse Pathways, Common Themes: A Complexity-Informed, Human-Oriented Comparative Case Study of Teacher Candidates' Experiences of French Language Proficiency Development in Concurrent Teacher Preparation in Ontario
Bibliographic record
Abstract
This dissertation explores the French-language-proficiency development experiences of diverse teacher candidates (TCs) in two concurrent teacher preparation programs in Ontario, Canada, where French is a minority language and many future teachers of French as a Second Language (FSL) use it as an additional language. To gain insight into TCs’ language proficiency development experiences, a central aspect of their learning as future teachers, this study addresses three main questions: (1) What views do various actors hold on the importance of and the priorities within French language proficiency development in concurrent initial teacher preparation (ITP) ? (2) What French language proficiency development trajectories are teacher candidates experiencing in two concurrent ITP programs? and (3) What thematic similarities and differences emerge from this study among individuals’ experiences and across programs? This study sought thematic, non-evaluative comparisons using a qualitative and collegial approach. Within a complexity-informed, human-oriented conceptual framework, language proficiency includes both how skillfully language is used (Richards Schmidt, 2002), and how it engages individuals emotionally and socially (e.g., Piccardo Aden, 2014). This qualitative study included TCs’ and university Educators’ perspectives collected through a questionnaire, reflective interviews, and TC case studies. In line with its complexity-informed design, attention to change over time was highlighted. Results suggest varied views and experiences among diverse participants, with some prominent themes emerging from the qualitative, iterative data analysis. Among these themes are agreement on the importance of language proficiency development, the centrality of courses as a location for language proficiency development, and the importance of emotion and power relations in understanding TCs’ language proficiency development experiences. Thematic comparisons suggest that individuals’ experiences are unique and influenced by multiple factors, and that similar themes emerged across programs. The findings support a complexity-informed, human-oriented understanding of language proficiency development, providing insights of use to researchers and teacher educators.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".