A Longitudinal Study on an EAP Course for MTI Students: Introduction to Engineering Knowledge and Its Translation
Bibliographic record
Abstract
With the development of international exchanges and globalization, there is an increasing need for translators and interpreters. Language students of translation majors usually lack scientific or technical knowledge. This paper introduces an innovative EAP (English for Academic Purposes) course－Introduction to Engineering Knowledge and Its Translation to students of Master of Translation and Interpreting (MTI) at a top university in China. The course is innovative in terms of teaching objectives, content of learning, pedagogy and aims to introduce fundamental knowledge as well as core vocabulary in four major engineering fields (Electronic and Telecommunication Technology, Mechanical & Electrical Engineering, Civil Engineering, and Materials Science & Engineering) to enable students to read specialized articles, perform engineering English-Chinese and Chinese-English translation engineering. The paper mainly focuses on this EAP course’s design and the improvement of the teaching effectiveness, which has implications for other EAP course designs. Course development can be divided into two stages: 2017-2020; 2021-now. TPCK (Technological Pedagogical Content Knowledge) is adopted in stage 1 and 2; POA (Production-Oriented Approach) approach is adopted in stage 2 to improve the teaching effectiveness. Surveys are conducted to get students’ feedback for improvement and students’ evaluation to the course obtained from the school’s teaching website shows that students’ satisfaction to the course increases over the years. Analysis of the students’ translation indicates the usefulness of POA in enhancing students’ competence of engineering translation and the course assessment shows the accomplishment of the teaching objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".