The Effectiveness of Cooperative Work Procedure in Enhancing Translation Skills among Saudi Students of Translation at King Khalid University
Bibliographic record
Abstract
The present paper aimed at exploring the effectiveness of using the translation techniques, namely, Cooperative Work Procedure. (Gerding- Salas, 2000) on teaching translation courses for the students of translation at King Khalid University. The researcher used two tools to collect data, a translation test, and a questionnaire which were designed by the researcher. The purposive sample of the study consisted of forty-eight students of translation who were assigned randomly to two groups, i.e., experimental and control. One group extensively utilized Cooperative Learning, whilst the other did not. The paper revealed that the utilization of Cooperative learning (CL) in teaching translation courses take an active part in promoting translation students' performance in translating texts. The experimental group subjects' results show positive obtains through the synthesis of Cooperative Learning (CL) in teaching translation courses. Moreover, data from the questionnaire indicated the students of translation have spectra responses towards cooperative learning and a majority of them favored working alone. The study also revealed that nearly all of the students of translation appreciated fully the issue of discussing and debating their renditions tasks with their colleagues while they do not like the idea of working in groups.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".