The Role of Contextual and Individual Factors in Shaping Instructors’ Approaches to Written Corrective Feedback in EFL Settings
Bibliographic record
Abstract
The present study investigates the behavior of EFL lecturers in relation to written corrective feedback (WCF) and the individual and contextual factors that influence this behavior. To answer the research questions, six lectures from the Department of English Language and Literature were interviewed and their WCF behavior was examined. The results showed that lectures applied a variety of feedback tactics to students' written work, including direct and indirect WCF, unfocused WCF, supplementary oral corrective feedback (OCF), and the use of positive comments and suggestions. Contextual factors such as the student’s language level, the type of error the student made, the curriculum, the instructional context (i.e., EFL), the students’ preferences, the lecture’s teaching load, class size, time constraints, culture, and the student’s psychology influenced the decision to use one method or another. In addition, the data revealed that personal characteristics influenced lectures' use of WCF. Examples include the teacher's personality, teaching experience and training courses, and personal experience with feedback as a student. Moreover, the findings showed that there are some challenges that could complicate feedback provision. These include some personal characteristics of the instructors, such as impatience, while others are related to the students, such as illegible handwriting, sensitivity, and carelessness.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".