The Role of Interlanguage Practices in Feedback Mechanisms: A Case Study with Saudi EFL Learners
Bibliographic record
Abstract
This study explored the efficacy and role of interlanguage feedback practices to and from EFL learners’ perspective at Qassim University which is a large university with a diverse Arabic speaker learner group. It also gauged the correlation between using interlanguage feedback practise, tolerance of language ambiguity and motivation to learn among the participants. A convenience sample of 48 EFL undergraduates at Qassim University were encouraged to judiciously adopt interlanguage in giving and receiving feedback in the EFL class for a period of eight weeks. Thereafter, a questionnaire was used to gather information on the predictors for interlanguage use in feedback and their outcomes on learning perceptions of the learners. Results indicated that students have moderate perceptions towards practicing interlanguaging feedback. Results also reported positive and moderate direct correlation between practicing interlanguaging feedback, tolerance of ambiguity and learning motivation. This was reflected in foreign language ambiguity tolerance, followed by learning motivation. Results also helped conclude that in the Saudi EFL context, an English-only classroom is not yet suitable to optimize learning, given the learners’ learning style and prevalent pedagogical methods, and as far as interlanguage use in feedback mechanisms is concerned, learners are positive to the idea as it aids in achieving their learning goals.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".