Teacher Trainers' Perspectives and Practices Regarding Written Corrective Feedback in L2 Writing: A Mixed-Methods Study in a Venezuelan University
Bibliographic record
Abstract
This study takes place at a university in Venezuela where Spanish is the first language. The participants are teacher trainers on a five-year program in a subject area called English Practice, where future English language teachers develop their language skills. Adopting an interpretive stance by examining qualitative and quantitative data gathered from two online questionnaires, this exploratory research aims to explore the practices and beliefs teacher trainers have regarding written corrective feedback (WCF) on their learners’ writing in English. The findings reveal that trainers use more than one WCF strategy, favouring the use of codes and the provision of the correct form; the trainers report they aim to correct all errors encountered in their students’ written productions since they think it improves learners’ grammar accuracy while raising their language awareness. Data demonstrate that trainers WCF beliefs are influenced by previous experiences as language learners, institutional guidelines, views of second language teaching and learning and teacher development programs. Results show that trainers believe they should adopt a more rigorous WCF approach with pre-service teachers than with other learners due to the fact trainees are regarded as prospective language models who need to avoid errors in their future teaching practice.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".