Teachers’ Beliefs and Practice about Written Corrective Feedback: A Case Study in a French as a Foreign Language Program
Bibliographic record
Abstract
Despite ample research examining second (L2) and foreign language (FL) teacher feedback, research situated in French as a foreign language (FFL) contexts is scarce, in particular studies that examine the beliefs and practices of corrective written feedback (WCF) among FFL teachers. The present study seeks to address this gap by investigating the WCF beliefs and practices of FFL teachers in an undergraduate program in Costa Rica. The participants in this study were five teachers teaching in an FFL program in the Modern Languages School at a large university in Costa Rica. Data were gathered using an online questionnaire, a semi-structured interview, and samples of students’ writing with teacher feedback. The findings revealed that the participants held common beliefs concerning writing, teaching writing, feedback provision in an FL, and the interdependent relationship among teaching, learning, and feedback in an FFL writing class. The results also showed that participants’ beliefs and practices regarding various aspects of written corrective feedback (CF) tended to be aligned, specifically in terms of the use of comprehensive indirect error-coded WCF and the use of evaluation grids. Implications and future research avenues are discussed.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".