Use of L2 Appropriate Formal Written Words by EFL Learners: A Study of the Contribution of DCF and MCF
Bibliographic record
Abstract
Considering the significance of corrective feedback and its effect on L2 vocabulary building, this study aimed to investigate the extent to which direct corrective feedback (DCF) and metalinguistic corrective feedback (MCF) could contribute to the use of L2 appropriate formal written words among Arabic L2 learners of English. A writing test (IELTS writing Task 2) as a pre and post-test was administered to gauge the participants’ (N= 96) L2 lexical resources. The sample was randomly divided into 3 groups according to the teaching feedback strategy applied: direct, metalinguistic, and control groups. The first two groups were given feedback based on their condition but the control group was given the conventional, unfocused feedback. Ten tutorial sessions on how to write formal words accurately were delivered to boost the appropriate use of L2 formal words in the writing tests. The target components of essay writing measured in the study were word choice, and the correct use of L2 formal words in writing. Findings showed the positive effects of both feedback types but metalinguistic groups outperformed the direct and control groups in the posttest. Additionally, the qualitative dimension of the study demonstrated that those who received metalinguistic feedback had more positive attitude than those who received direct feedback.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".