English-Medium Instruction in Self-Financing Tertiary Institutions in Hong Kong – Views and Practices from the Students
Bibliographic record
Abstract
The medium of instruction (MOI) has been a bone of contention in Hong Kong, a former British colony, since its colonial days. Despite the Hong Kong government’s effort to promote the “biliterate and trilingual” language policy, advocating Cantonese, English and Putonghua as the three official spoken languages and emphasizing the importance of literacy in both written Chinese and English, most tertiary institutions today still adopt English as the medium of instruction (EMI). However, with the expansion of tertiary education in the early 1990s and the decline in the general English language proficiency of university students, some university lecturers have found it difficult to teach in English as required. This raises the issue of the practicality of the indiscriminate adoption of the EMI policy at tertiary level, particularly at the self-financing tertiary institutions where students are generally known to have under-performed in the English subject. In order to understand whether or how the EMI policy is upheld in these institutions, focus group interviews were conducted with students from various programmes of five self-financing tertiary institutions in Hong Kong. The findings indicate these students’ strong preference for English-medium instruction with the belief that it can improve their English proficiency, though their actual approaches to coping with the demand on their limited English and how they view and use the three languages in class deserve policy makers’ serious consideration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".