A Critical Re-imagining of French-Language Teacher Learning and Professional Identities
Bibliographic record
Abstract
As an official bilingual country, Canada depends on French as a Second Language (FSL) teachers to promote bilingualism. However, FSL programs suffer from what I call ‘FSL teacher flight’: FSL teachers who enter the profession do not remain in it, they either leave or transition out of teaching French. FSL teachers report feeling disenfranchised, marginalized and isolated in their practice (Macfarlane Hart, 2002; E. M. Richards, 2002). One way to address these issues is to offer FSL teachers the opportunity to participate in Computer-supported Collaborative Learning (CSCL) networks (Hollins-Alexander, 2013; Kitsantas Dabbagh, 2010; Kreijns Kirschner, 2004). These blended (i.e., online and face-to-face) professional learning networks, based on the principles of learning as a social and transformational process, make teachers agents of their professional learning and help them negotiate the complexity of their practice, and develop their professional identity and well-being through shared discursive practices. This longitudinal multiple case study is a unique in-depth look into the professional learning and identity formation of two core French teachers over the course of four years (2011-2015). The data consist of 40 monthly two-hour group meeting discussions video recorded during a full release-day for site-based research, over 150 posts and essays shared in an online forum, open-ended survey responses and extensive field notes. Findings reveal that FSL teachers use a wide range of complex and interrelated discursive practices to help them negotiate their professional learning and identity formation. Their research inquiries were highly contextualized, based on their local needs. They also reported that the learning experience in the CSCL network helped them feel supported and engage in their practice in new and creative ways. The re-professionalization (Kooy, 2015) of FSL teachers’ practice through their learning experiences reveals the potential contribution of this study to address the issue of FSL teacher retention. The study provides a much-needed deeper understanding of what happens when FSL teachers actively participate in professional learning networks. It also re-imagining the narratives around FSL teachers so as to provide a model for long-term FSL teacher learning that promotes collaborative professionalism (Fullan Hargreaves, 2016) and teacher well-being.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.051 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".