International Student Satisfaction: A Comparative Analysis of Student Perceptions, Expectation and Reality, and their Relationship with Recruitment Policies and Approach
Bibliographic record
Abstract
International student recruitment is a significant contributor to the higher education sector, with many countries actively seeking to attract international students to their institutions. This study examines the factors affecting the satisfaction of international students studying in the UK, USA, Australia, and Canada. Using a mixed-method research approach, including both qualitative and quantitative methods, attempting to measure the gap between student expectations and perceptions using the SERVQUAL instrument. Analysis identifies eight latent constructs as significant predictors of student satisfaction: accommodation, technology, lifestyle, image and prestige, social orientation, education, settlement, and economic factors. The article highlights the importance of addressing these factors to optimise student satisfaction and provides insights for policymakers in higher education institutions and governments looking to expand the overseas education sector. It is the authors' assertion that new technologies can be used to integrate and monitor the processes of promotion, analysis, application, and recruitment to improve overseas student recruitment processes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".