Profiling the Dynamics of EMI Effectiveness Factor and Students’ EMI Course Satisfaction: The Case of Vietnam and Taiwan
Bibliographic record
Abstract
This study explored the relationships between English as a medium of instruction (EMI) effectiveness factors and students’ EMI course satisfaction. In addition, responding to the call for adaptive EMI, it also examined how students and teachers’ background characteristics could shape such relationships. Using the convenience sampling method, 821 undergraduate students participated in the survey. The study affirms that three EMI effectiveness factors positively predict student EMI course satisfaction, while characteristics that are related to how students approach learning have the most effect on their satisfaction with EMI courses. These findings also affirm the complexity of student EMI course experiences, when considering both student and teacher demographic and background differences. The moderating effect of English proficiency and prior EMI experience differs significantly among such factors and student satisfaction. This research highlights that a cross-cultural outlook is more influential for Taiwanese students and courses with local teachers, while teaching characteristics are a stronger predictor for male students. The significance of each factor may fluctuate within diverse national contexts and is influenced by students and teacher backgrounds. Understanding and adapting to these contextual nuances will play a key role in elevating overall student satisfaction with EMI courses.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".