Learning English in Tourism and Hospitality Internships Overseas: Reflections from Six Taiwanese College Students
Bibliographic record
Abstract
The purpose of this study was to examine the overseas internship experiences and determine whether they helped to enhance the intended English learning outcomes for students who will be working in the service industry. Seventeen entries of reflective journals written by six students who interned overseas were analyzed. The participants reported their frustrations in language learning and described how these experiences could complement their overall career development in the future. The students identified a gap between everyday language needs in the industry and their language training received in Taiwan. The results indicated that the participants thought the English courses in Taiwan have disproportionately emphasized reading or writing skills, whereas speaking and listening were in high demand in the workplace, particularly given the difficulties occurring in cross-cultural communication. The analysis also indicated that the participants’ oversea working experiences positively affected their English learning motivation. These experiences also helped the participants notice differences in interactions with people from various linguistic backgrounds. English learning in Taiwan is traditionally embedded in English as a foreign language (EFL) classrooms that are reading/writing-centered and examination-oriented. The course content should be revised to improve students’ oral communication skills for the workplace.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".