Developing Second Language Learners’ Sociolinguistic Competence: How Teachers’ CEFR-Related Professional Learning Aligns with Learner-Identified Needs
Bibliographic record
Abstract
This article explores how teachers’ professional learning about the Common European Framework of Reference (CEFR) can re-orient their reported teaching practices to meet learner-identified sociolinguistic needs. To this end, the article first examines learners’ sociolinguistic needs by exploring the extent to which post-secondary French-as-a-second-language (FSL) learners, who completed their elementary and secondary schooling in Ontario, Canada, believe that they have successfully developed sociolinguistic competence in their target language. Specifically, it considers the learners’ assessment of their sociolinguistic abilities, the types of sociolinguistic skills they wish to develop further, a comparison with their actual sociolinguistic performance, and the ways in which they hope to develop the sociolinguistic skills they feel they lack. Second, the article explores Ontario elementary- and secondary-school FSL teachers’ reported focus on sociolinguistic competence in their teaching after having engaged in intensive and extensive CEFR-oriented professional learning. Specifically, it considers how the teachers’ professional learning influences the sociolinguistic relevance of their planning, classroom practice, and assessment and evaluation. The article concludes by considering whether the degree of “fit” between the learners’ self-identified needs and the teachers’ reports of their re-oriented practices is poised to improve the sociolinguistic outcomes of Ontario FSL learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".