Iranian EFL Learners’ Grammatical Knowledge: Effect of Direct and Metalinguistic Corrective Feedback
Bibliographic record
Abstract
The present study was conducted to compare the impact of direct and metalinguistic written corrective feedback on Iranian EFL learners’ grammatical knowledge. The participants were a convenient sample of students in two intact writing classes. The instruction provided in both groups was similar; however, the students in one group received direct feedback and the students in the other group received metalinguistic feedback in the form of error codes on writing accuracy (i.e., grammar, vocabulary, and punctuation) of their in-class written texts. Moreover, all the students took a grammar test serving as pre- and posttests before and after the treatment. In addition to the computation of gain scores, descriptive statistics and a mixed between-within subjects ANOVA were run to analyze the data. Descriptive statistics revealed that the grammatical knowledge of the learners in both groups developed as a result of the two types of feedback; nonetheless, there was not a statistically significant difference between the students’ performance on the grammar test before and after the treatment. Furthermore, although the direct feedback seemed to be more effective in improving grammatical knowledge, no statistically significant difference was found between the two groups’ gain scores on the grammar test. Accordingly, it was concluded that either of the feedback types may be employed to effectively develop EFL learners’ knowledge of grammar.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".