Written Corrective Feedback in EFL Context: Contextual and Individual Factors Influencing Students’ Responses
Bibliographic record
Abstract
This study explores the written corrective feedback (WCF) behaviors of English as a foreign language (EFL) students in an EFL context and the individual and contextual factors that shape these behaviors. Ten students from the English language and translation department were interviewed and their WCF behaviors were explored. The participants were aged between 19 and 23 years, and had studied at least two writing courses taught by different instructors at a Saudi University. The findings revealed that most students had positive affective attitudes toward WCF. Most of them appreciated receiving both positive and negative comments, as the former were encouraging, while the latter helped improve their writing skills. Students’ behavioral responses to WCF varied; while some students were eager to read the feedback and correct their errors, others could correct them only when a second draft was requested. Additionally, most students relied on instructors for error correction, while only a few attempted more autonomous approaches to correction using textbooks or electronic resources. This variation in behavior could be attributed to the students’ different levels of motivation to improve their writing skills and their different individual goals for the writing course. Moreover, students’ responses to WCF were influenced by contextual factors such as the type of WCF received, the instructor’s professionalism and relationship with the students, number of assignments, and time constraints.
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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 | 0.003 | 0.030 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".