The Role of Error Types and Feedback in Iranian EFL Classrooms
Bibliographic record
Abstract
To facilitate successful language learning, teachers need to establish positive affect among students yet also engage in the interactive confrontational activity of error correction (Magilow, 1999). To shed more light on the issue, this study aims at the investigation of the error types, corrective feedback moves, and learner uptake, i.e., responses to feedback in Iranian communicatively-oriented EFL classrooms. The database is drawn from transcripts of audio-recordings of the elementary and high intermediate classes of a language institute including almost 12 hours of interaction among the students and teachers. Following the analysis, the errors were coded as grammatical, lexical, phonological or unsolicited use of L1 (first language) and corrective feedback moves as explicit correction, recast, clarification request, metalinguistic clues, elicitation, or repetition. Moreover, the suggested breakdown for uptakes included student-generated repair, repetition, and needs repair. Grammatical errors were the most frequent error type in the entire database (50.5%); however, phonological (26%) and lexical errors (22%) had lower rank error type. Moreover, the results indicated an overwhelming tendency for the teachers to use recasts (50.5%) in spite of their complete ineffectiveness at eliciting student-generated repair. Repetition (96%), metalinguistic feedback (86%), elicitation (67.5%), and clarification request (44%) –the negotiation of form feedback moves- were instead supposed to fulfill this aim.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".