EFL learner and teacher perspectives on corrective feedback and their effect on second language learning motivation
Bibliographic record
Abstract
The present mixed-methods research study examines the beliefs of 247 high school students and their 12 EFL teachers about corrective feedback in terms of its types, frequency, and their positive and negative attitudes towards it. The data were gathered by means of a questionnaire administered to all participants and in-depth interviews conducted with a subsample of 15 students and all 12 of the participating EFL teachers at two distinct research sites in Santiago, Chile: a private bilingual school and a semi-private institution. Teacher and learner perspectives on error correction were compared within and across schools in order to identify differences that might affect students' L2 motivation by quantitatively analyzing the questionnaire data by means of the Mann-Whitney U Test and by qualitatively examining the interviews through content analysis. The results revealed that in both research settings there were evident disparities between teacher and learner perspectives on corrective feedback. Whereas students expressed positive views of corrective feedback and its effectiveness as well as preferences for explicit types of correction, teachers were skeptical of its effectiveness and concerned about its effect on learners' self-confidence. Accordingly, teachers reported preferences for more implicit types of feedback. These results are discussed in terms of the potentially detrimental effects they may have on students' L2 motivation and in terms of their pedagogical implications.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".