Should I stay, or should I go? A mixed method study of pre-service English second language teacher efficacy-identity development in Quebec
Bibliographic record
Abstract
Research tells us that teachers with high self-efficacy are more resilient and less likely to leave the profession. It also tells us that teacher self-efficacy (TSE) beliefs are most pliable early in learning, and that teacher education programs are a key site for professional identity construction. In this dissertation, I use a mixed method approach to look at the ways in which pre-service English Second Language (ESL) teachers from two different universities in Quebec developed their self-efficacy within their professional teacher identity. I collected quantitative data before and after a major practicum to see if experiences during the practicum influenced the participants’ TSE scores or their intentions to remain in the teaching profession. I also collected narrative, thematic and graphic data at three different times during the practicum to explore what kinds of experiences the participants were having and how these experiences influenced their understanding of the kind of teacher they were becoming. Key findings from this research suggest: 1) that teacher-self efficacy (TSE) and professional identity development does occur during the practicum; 2) that recognition of status from students and members of the teaching community is essential to building professional identity; 3) that TSE is most effectively built through a process of trial, error and reflection – especially when teaching alone; and finally that 4) the structure of evaluation creates conditions unfavourable for the consolidation of either TSE or professional identity. The dissertation concludes with a discussion of the ways in which its findings will contribute to our understanding of the role teacher education programs can play in preventing future language teacher attrition
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".