Enhancing Intercultural Collaborative Learning in a Multinational Classroom: Case of Taiwan
Bibliographic record
Abstract
With more international programs being offered by universities in Asia nowadays, students have the opportunity to study together with foreign classmates. However, in practice, there exists a level of mistrust and apprehension between local and foreign students, especially among those with different physical appearances and distant cultural backgrounds. This research aimed to identify and understand intercultural obstacles to in-class collaboration among local Taiwanese students and their foreigner classmates in a Taiwan international college. Data were collected through an open-ended questionnaire and participant observation from 52 students of different nationalities (Haitian, Indian, Malaysian, Indonesian, Thai, American-Chinese, New Zealand-Chinese, Mongolian and Taiwanese). In order to minimize cultural bias, two researchers jointly conducted content analysis in combination with ethnographic observation. This study found that differences in physical appearance and communication styles strongly deterred intercultural communication among the students in the beginning. It was found that in-class group discussions and group projects helped to dispel negative stereotypes, by cultivating greater mutual respect and understanding among the students in a multinational classroom. In fact, several misunderstandings and cultural conflicts could have been resolved in the classroom. Findings suggest that teachers have a crucial role in developing students' intercultural competence by implementing collaborative learning methods.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".