Students’ learning preferences and experience in a globalised world: Opportunity to optimise internationalisation in physiotherapy education
Bibliographic record
Abstract
Background: Hong Kong is at the cross-road between Eastern and Western cultures. Increasing globalisation allows students to gather experiences from various educational contexts. While internationalisation has been promoted in higher education worldwide, the focus was often put on students from Chinese cultures integrating into Westernised education systems. Not much is known about how students from Chinese background with exposures to Western cultures, reintegrate into a Hong Kong university, characterised by a highly competitive system that potentially affects students’ well-being. Aim: To identify learning preferences by Hong Kong physiotherapy students who have been exposed to educational contexts in the USA, Australia or Canada, and to explore their subjective experiences regarding different educational approaches during their studies. Methods: Ten students participated in this phenomenological study. Semi-structured interviews were conducted in English, transcribed ‘ad verbatim’ and analysed using qualitative content analysis. Findings: Three themes emerged from the data: (1) Interaction between students and teachers, (2) past experiences that help with future dreams, and (3) obstacles and opportunities in learning. Conclusion: Physiotherapy students with globalised experience rely on their past educational exposure to give meaning to their future. They believe that a student-centred approach is crucial for learning. Their experiences shed light on consideration factors for optimally internationalising the physiotherapy curriculum.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".