The UK Student Visa Cut and its Implication to International Education in Thailand
Bibliographic record
Abstract
This paper discusses the flow of international students in higher education to five most popular English-speaking countries i.e. Australia, Canada, New Zealand, the UK, the US and also Thailand which is included as a typical ASEAN source as well as destination of international students. The possible effects of the new UK student visa rule, aiming to reduce student visa by 25% starting April 2011 are studied. A source-destination student flow model is developed based on secondary data to explain the extent of the change in student enrolment in higher education due to the UK student visa reduction. A questionnaire interview was carried out on stakeholders in student flow from Thailand to the UK including education service agents, higher education institutes representatives from the UK, and prospective Thai students seeking for education in the UK. These stakeholders felt neutral about the overpopulation of international students in the UK and the need to reduce the number of student visa. They, however, realized that the student visa cut was inevitable and they have to find strategies to co33.3pe with this visa cut. The majority of 66.7% of the education service agents will maintain the present level of effort in recruiting Thai students for the UK while 33.3% adopt the wait and see attitude and none will “increase the effort” in student recruitment for the UK. 50% of the higher education institutes in the UK consider both the “maintain the present effort” while another 50% adopt a “wait and see” policy and none will “increase the effort” for student recruitment. As for the prospective students, the majority of 83.3% adopt a “look elsewhere” for education destinations while a minority of 16.7% will “maintain the present effort” in trying to find a seat in the higher education institutes in the UK, and none will “increase the effort” in trying to get education in the UK. Most of the stakeholders agree that there is an opportunity for the growth of international education in Thailand which can accept students from ASEAN countries. However, the improvement of quality and the reduction of cost may need to be considered. The problem of low English skills in Thai students seems to be a major obstacle to international education in Thailand which uses English as the international language. The problem about low English skills of Thai students needs to be solved.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".