International Student Enrollment Trends from 2008-2014: Country Case Studies and Comparison to a Midwest Liberal Arts Institution.
Bibliographic record
Abstract
The international student recruitment and overall cross-border education have constantly been evolving. In the past two decades, higher education institutions were developing and implementing their plan of campus internationalization. Various universities and colleges have different approaches to the internationalization. However, through the implementation of the process, most of the institutions realized that the cornerstone of the internationalization is the international student recruitment. Despite providing the financial health to the institutions and overall to the process of internationalization, international student recruitment is the provider of the campus multiculturalism and diversity. This research is concentrated on the external factors of the cross-border education and overall trends of the student recruitment at the Midsize Midwestern University. The researcher attempted to deepen understanding of the student mobility trends through the observations of the 10 sample countries and the relationship to external factors for the period of 2008 to 2014. The researcher used the following external factors: students enrolled in the United States, GDP per capita (PPP), political turmoil, and change in currency against USD, national disasters, and crime rate. The country participants were Venezuela, France, Mongolia, Canada, Brazil, Germany, China, Japan, Panama, and Spain. This study is based on the secondary data used from the publicly available databases of the Institute of International Education (IIE), The World Bank, UNESCO, OECD, SEVP, as well as the data from the Mid-size Midwestern University, for the period of 2008 to 2014. The study used quantitative, non-experimental, cross-sectional, descriptive design. The multiple regression analysis models identified the relationship between dependent and several independent variables. The study provides in-depth analysis of the findings, as well as provides future recommendations to the institutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".