Campus Life for International Students: Exploring Students’ Perceptions of Quality Learning Environment at a Private University
Bibliographic record
Abstract
The number of international students enrolling at higher learning institutions in Malaysia is increasing each year. However, the quality of learning environment is not always easy to measure, particularly for private universities which are not financially aided by the government, where the learning environment is characterized by their physical construct, quality of staff and academic atmosphere. There have been numerical quantitative researches on the perceptions of university quality learning environment but it is argued that a qualitative approach would add to existing knowledge by providing deeper insights, and from a different perspective. The purpose of the present study was to explore international students’ perceptions of a private university through individual in-depth interviews. 15 international students from ten different countries were selected for this study. Various themes emerged from the interviews, some of which have not yet been uncovered in past research investigating learning environment. The findings provide evidence that students expressed their common expectations, concerns, and hopes for a quality university learning environment. This study also provides support for the employment of qualitative approach in the study of perception and quality learning environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".