Individual Face-To-Face Feedback and the Saudi EFL Learners: Evaluating Enhancement of Writing Skills
Bibliographic record
Abstract
This research analyzed Saudi undergraduate students’ writing before and after individual face-to-face feedback. The intervention was in the nature of individual written feedback on Saudi EFL students' paragraph writing. The participants were 23 EFL Saudi students exposed to a pre and post-test across six criteria that targeted to evaluate their writing with individual corrective feedback from the teacher. The intervention was one semester long. The study reported that individual corrective feedback plays an important role in developing students' writing skills. Results showed that development occurred in all the six criteria evaluation criteria adapted from Savage and Shafiei (2007), with significant statistical values (Sig. <.05). Furthermore, the criteria were ranked as: inclusion of specific words; inclusion of correct adjectives; writing good conclusion; writing good topic sentence; adding more descriptive details; and the use of background information. The study recommends making use of face-to-face corrective feedback in developing students' abilities in different language skills.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".