Quality Education Beyond Borders: An International Content Analysis for Transnational Distance Education
Bibliographic record
Abstract
Due to the forces of globalization, higher education institutions have increased their participation in international partnerships and the flow of academic services and resources across borders. This international academic mobility is known as internationalization, and transnational distance education is an example of an innovative internationalization strategy. Despite the exponential growth and the projected acceleration of transnational distance education enrollment, there is a dearth of knowledge in the measurement and practice of international quality dimensions and learning outcome indicators to support internationalization efforts for transnational distance education. This research utilized content analysis to organize networks of concepts and capture quality indicators from five internationally accepted frameworks to establish a quality dimension typology. Based on preliminary findings, 27 internationalization indicators were identified to support transnational distance education internationalization efforts. Findings support a need for more holistic quality frameworks with greater attention to internationalization quality dimensions to guide successful outcomes across borders.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".