Measuring Chinese Undergraduate English Majors’ Motivation to Learn Translation in Higher Education Translation Courses
Bibliographic record
Abstract
Many studies have suggested that Chinese English majors tend to have a lower level of motivation to learn translation due to the conventional teaching method still prevalent in many a translation classroom. In this regard, from the standpoint of instruction, a translation course design that employed motivation as a central concept of the design may have positive impacts on students’ motivation to learn. Systemic Functional Linguistics (SFL) is known for its efficacy in making meaning-oriented choice in language use in context. And since translation is about recontextualized meaning transfer, a teaching method integrated with SFL may be motivating for students to learn translation. Grounded on SFL and guided by Keller’s ARCS Motivational Design, a new genre-based method was proposed. This study aimed to explore effects of an SFL guided genre-based method and the conventional method on Chinese students’ motivation to learn translation. Two intact groups were selected as the Experimental Group and the Control Group, each consisting of 37 students. The Experimental Group was instructed using the genre-based method while the Control Group using the conventional method. A motivation questionnaire adapted from Keller’s Course Interest Survey was administered to both groups’ students before and after the pedagogical intervention. Data collected from the survey were analyzed using ANCOVA test in SPSS (Version 25). The results show that students in the Experimental Group scored significantly higher mean in overall motivation and four constructs of the ARCS Model, namely, Attention, Relevance, Confidence, and Satisfaction after they were taught using the genre-based method in comparison with their counterparts in the Control Group. The findings indicate that in general, the genre-based method is effective in enhancing students’ motivation in learning translation. Therefore, future translation instructors may consider adopting the SFL-informed genre-based method as an alternative pedagogical tool to motivate students to learn translation.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".