A Comparison of Efficiency in Teacher Correction Strategies in Iranian EFL Learners’ Speaking Improvement
Bibliographic record
Abstract
The present study was an attempt to investigate the effect two types of corrective feedback (i.e., recast and metalinguistic) in order to find out which one is more effective on EFL learners’ speaking improvement and also to see if gender could play a role in the relative impact of the two types of corrective feedback on learners’ speaking ability. To this end, 65 EFL learners of intermediate level in one of language institutes in Shiraz, Iran were selected and divided into three groups including two experimental groups and one control. The instruments used to collect the data included IELTS test as the pre and post tests and Oxford Placement Test (OPT) in order to obtain the homogeneity in participants’ English proficiency. The collected data were codified and entered into SPSS Software (Version 22) and were analysed using descriptive statistics, t-test, and Tukey test. The results indicated that although applying these two types of corrective feedback could have made improvement in EFL learners’ speaking ability, there was not observed any significant difference between impacts of recast and metalinguistic on EFL learners’ production. The test results also indicated that there was not any significant difference regarding gender within the three groups. This homogeneity further shows that in this study, the gender variable did not have any effect on the role of corrective feedback and it can be concluded that the observed difference between metalinguistic group, recast group, and control group is just the result of the provided corrective feedback type which has acted as the intervening variable and the moderator variable such as gender did not prove to have any effect in the outcome of this study. The findings can contribute to syllabus design and teaching methodology areas.
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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".