Effectiveness of Instructors’ and Peers’ Oral Feedback on the Accuracy of English Writing: A Study of Pakistani ESL Undergraduate Learners
Bibliographic record
Abstract
The objective of the current research is to investigate the effect of instructors and peers’ oral feedback on the written English accuracy of ESL learners. In this quasi-experimental study, 90 participants are assessed on three distinct forms of feedback (i.e., instructor’s oral metalinguistic feedback along with indirect written feedback, peers’ oral interaction along with indirect written feedback and no feedback) for writing errors of three types (i.e., verb tense, preposition, and articles). The participants are assessed three times; pre-test, an immediate post-test and delayed post-test. ANOVA demonstrates that learners receiving instructors’ oral metalinguistic feedback along with indirect written feedback outperform those who receive peers’ oral interaction along with indirect written feedback and no feedback in two out of three linguistic forms in subsequent writing. The findings of the study suggest that employing oral metalinguistic instructors’ feedback along with written feedback in the Pakistani language learning context can help learners improve their English language learning. Consequently, language efficiency may improve overall academic performance and success ratio in academia.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".