Breaking Traditional Boundaries in Translation Pedagogy; Evaluating How Senior Lecturers Have Incorporated Digital Tools to Enhance Translation Teaching
Bibliographic record
Abstract
Digital technology has brought significant transformation in translation pedagogy, mainly in helping lecturers integrate digital tools in teaching translation courses. However, it is significant to gain insights from the experiences of the senior lecturers who are gradually accepting the integration of technology in translation pedagogy. The focus of this paper is to gain insights from Professors in translation pedagogy on their challenges in transiting from traditional teaching systems to digital technological systems, also sharing their solutions to the challenges. Through the use of both survey questionnaires and semi-structured interviews, data was gathered from 93 extensively experienced professors in translation. The gathered data was analyzed using thematic analysis and statistical measures. The results of the data from the interviews showed four main themes, including the theme of transition challenges, the theme of assessment and evaluation challenges, the theme of inclusion and accessibility in digital technology, and the theme of actions the professors had taken in digital technology. The professors confirmed actions such as “finding appropriate online platforms that allowed for real-time cooperation" (Professor 2), "using virtual translation technologies that enabled real-time collaboration on documents" (Professor 5), and "encouraging collaborative translation exercises in real-time Google Docs" (Professor 2)”. The data from the survey questionnaire unveiled specific ways in which digital tools have assisted the senior lecturers in teaching translation courses, including teaching materials for translation courses are now prepared more quickly due to AI technologies, and automated grading systems driven by AI have reduced assessment time and generated feedback for students' translation projects. The Professors generally accepted the impacts of technological advancements, mainly AI tools, in teaching translation and improving the general performance of the learners.
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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 | 0.052 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".