Bibliometric Analysis of Global Research Trends on Higher Education Internationalization Using Scopus Database: Towards Sustainability of Higher Education Institutions
Bibliographic record
Abstract
Sustainability in education has continued to evolve, which in turn creates a research niche that is able to provide greater opportunities for interaction between Higher Education Institutions (HEIs) and their surroundings. Internationalization of higher education is one of the new forms of engagements in higher education for ensuring sustainability. This study seeks to understand the research in higher education internationalization on publication outcomes, co-authorships between authors and similar countries, and co-occurrences of author keywords. This can provide valuable opportunities in expanding collaborative networks to impart global perspectives into teaching, learning, and research development. For this purpose, a bibliometric analysis was carried out to identify a total of 1412 journal articles from between 1974 to 2020 using information taken from the Scopus database. The research wraps up similarities on the growth of research, with the United Kingdom, United States, Australia, China and Canada emerging as among the countries that publish the most. There is a growing popularity of the term ‘higher education internationalization’ as part of the global new trends of cross-cultural study in transnational education. Finally, this study calls for future research programs with a concern in developing the intercultural communication of graduate students for global competence skills towards sustainability of HEIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.184 | 0.260 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".