The Transnational Student Learning Experience: Giving Voice to Internationalization Practices that Enhance Lifelong Learning and Transformation
Bibliographic record
Abstract
Transnational distance education is a strategic practice that contributes to the internationalization of higher education. However, little is known about the transnational student learning experience and the practices necessary to support intended outcomes, including preparing post-graduates with essential skills and competencies for employment and lifelong learning within their local communities, country of origin, and globalized economy. Therefore, this study explores the factors contributing to the success and challenges encountered during a graduate program undertaken at an open, distance education university in Canada from the perspective of Greek female graduates. By employing a collaborative autoethnography approach, researcher-participants explored critical components including accessibility, communication, international perspectives and application, and transformation for lifelong learning to support quality dimensions in internationalization practices. As a result, we find a need for a more purposeful and comprehensive integration of internationalization practices across an institution to support and enhance the knowledge process that flows across borders through online learning environments and communication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".