Task-based Approach in Teaching Translation: A Case Study in Jouf University
Bibliographic record
Abstract
Task-based approach is commonly used in second language teaching and it has been adopted in translation teaching too. However, driven by the lack of studies on task-based approach in translation especially in the very early stages of teaching translation, Saudi universities are no exception, this study focuses on the significance of implementing this approach in teaching the first practical translation course in a translation academic programme. It mainly examines the development of translation competence through task-based approach. A case study at English language department in Jouf University has been carried out where 39 students were taught using this method in their first practical translation course. Li’s (2013) customized model, namely task-based teaching in translation, is adopted here and the tasks were specifically selected to develop students’ translation competence in general and their bilingual competence in particular. The results drawn from this study found tangible development of students’ translation competence as well as bilingual competence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".