Exploring Jadara University Students' Attitudes toward the Use of Computer-Assisted Translation (CAT) Tools in Language Instruction
Bibliographic record
Abstract
The study investigates 200 Jadara University translation students' thoughts on CAT curriculum integration to the curricula. It primarily concerns English translation students and how CAT tools impact their learning and professional development of their mastery of English skills. To utilize technology effectively, instructors must understand students' pre-adoption ideas and expectations. Quantitative surveys and qualitative interviews analyze students' CAT tool expectations, concerns, and acceptance. The findings indicate enthusiasm for efficiency increases and concern about complexity and traditional translation abilities. The study shows that CAT tool integration requires technical training and detailed education to address student concerns. This study aids Jadara University and other translation instructors in CAT tool integration. When implementing essential features, such as translation memory (TM), terminology management, and alignment tools, students will be abler to understand and practice real-world translation workflows.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".