The Impact of CEFR-Related Professional Learning on Second-Language Teachers’ Classroom Practice: The Case of French in Canada
Bibliographic record
Abstract
This study explores the impact of professional learning about the Common European Framework of Reference for Languages: Learning, Teaching, Assessment (CEFR) on second language (L2) teachers’ classroom practice. Ninety self-selected French as a second language (FSL) teachers across Canada responded to an online survey about their planning, teaching, and assessment/evaluation practices before versus after their professional learning. The results revealed that the impact of such professional learning is wide-reaching and remarkably consistent across all three areas of practice. The teachers reported that their professional learning spurred them to start presenting language through speech acts and based on students’ needs, to emphasize not only linguistic but sociolinguistic and pragmatic competence as well, and to focus more intently on students’ ability to communicate in the L2. The teachers also reported that they increased the use of authentic materials and developed communicative and action-oriented tasks that simulate real-life situations. The findings suggest that CEFR-related professional learning may be used successfully to inspire L2 teachers to implement CEFR-informed classroom practices.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".