Exploring Native and Non-Native EFL Teachers’ Oral Corrective Feedback Practices: An Observational Study
Bibliographic record
Abstract
Commonly defined as L2 teachers’ responses to learners’ erroneous utterances, oral correctivefeedback (OCF) is an interactional classroom phenomenon which frequently occurs in foreignlanguage classes and has gained growing momentum in SLA research in recent years.However, how OCF preferences of English teachers vary in terms of their native-nonnativespeaker status remains as an uncharted territory of inquiry specifically in an expanding-circlecontext. This study aims to reveal the differences between in-class OCF practices of native andnon-native English-speaking teachers (NESTs & NNESTs) in Turkish EFL context and toexplore the cross-cultural influences that might affect these practices. To these ends, structuredclassroom observations and interviews were conducted with seven NESTs and seven NNESTs.The findings of the observations showed that the NESTs’ and NNESTs’ in-class OCF practicesdiffered considerably in terms of their tolerance of errors, preferred OCF types, the amount ofOCF and different types of OCF to different types of errors. Moreover, the follow-up interviewfindings demonstrated some similar and different dispositions between the teacher groupsconcerning several dimensions (whether, how, when, and which errors should be corrected, andby whom) including the effect of teaching experience and teacher education on their OCF-giving patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".