Translation Learning Needs and Motivations Within Blended Teaching at a Chinese Application-Oriented University
Bibliographic record
Abstract
With the rapid development and popularization of information technology and online learning platforms, university teaching no longer needs to rely solely on traditional face-to-face instruction. This is particularly relevant for translation courses, which are both theoretically demanding and practically challenging. Blended teaching models can be established by integrating appropriate online learning platforms with varying learning materials. This enables students to learn anytime, anywhere, at a pace suited to their individual needs. Prior to designing such instruction and intervention, it is essential to gain a comprehensive understanding of students’ specific translation learning needs, including their bilingual competence in English and Chinese, as well as their academic and career aspirations. The present study, designed and conducted by the researcher, involved 96 junior English-major undergraduates from Guangdong University of Science and Technology (GUST) in China. As an empirical investigation, it sought to identify the translation learning needs of students at application-oriented universities and to explore how blended teaching, informed by social constructivism approaches, influences learners’ L2 Motivational Self System (L2MSS), particularly its three core dimensions. The findings aim to provide both empirical evidence and theoretical insights for the enhancement of translation pedagogy in Chinese application-oriented universities, offering implications for improving translation curriculum design, aligning blended teaching with students’ professional development, and strengthening sustained translation learning motivation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".