Incorporating domestic students returning from international educational experiences into internationalization at home: Challenges and opportunities
Bibliographic record
Abstract
While domestic student—international student interactions have received attention in the literature as a means of advancing internationalization at home (IaH), the potential contributions of domestic students who return from international educational experiences (IEEs) have been noted but remain largely unexplored. This article seeks to initiate a conversation on this topic by identifying mechanisms through which such knowledge transfer might happen, barriers it might face, and possible approaches to facilitate greater knowledge transfer. The article’s analysis draws upon three antecedent bodies of literature to reflect upon findings from 20 original interviews with domestic returnee alumni of a comprehensive university in Ontario, Canada. The bodies of literature concern (1) foreign international students and IaH, (2) the experiences of domestic returnees, and (3) repatriate knowledge transfer (RKT) in business settings. The article finds that domestic returnees and their knowledge can make useful contributions to institutional efforts to promote IaH. It also identifies challenges, obstacles, and opportunities associated with domestic returnees as knowledge sources and nonmobile domestic students as knowledge recipients. Key findings include that returnees are less likely to share if they fear being the stereotypical student who only wants to talk about their IEE, but they tend to enjoy sharing in balanced conversations where their interlocutor has similar knowledge to share in return. The article applies its findings by proposing examples of intentional institutional efforts to incorporate returnees into IaH, including modifications to pre-departure and re-entry workshops and approaches to selecting and designing IaH events focused on domestic returnees’ experiences.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".