Exploring the Perspectives of Online Study Abroad Programs in Japan during the COVID-19 Pandemic: A Narrative Approach
Bibliographic record
Abstract
COVID-19 profoundly impacted countries worldwide between early 2020 and late 2021 due to travel restrictions, lockdowns, and health risks. Many higher education institutions responded to the health threat posed by COVID-19 by moving their on-campus programs online. Study abroad programs also needed to undergo adjustments, with some universities establishing alternative arrangements. This study uses a narrative approach to examine Online In-Country Study Abroad Program (ONIC-SAPs) from the perspective of Japan-based participants, academic, and administrative staff who were involved and participated. The program was operated by a university in Japan in collaboration with a partner university in Canada and a global language school during the height of the pandemic. The findings highlight that the advantages of ONIC-SAPs are also seen as the disadvantages of the programs. Acquiring language skills and experiencing intercultural encounters with the aid of technology may become even more common in the future, yet the extent to which this kind of learning is of benefit to all concerned should be considered. The study highlights that there is a need for further research into the content and curriculum of ONIC-SAPs to find ways to improve the value of online learning for all stakeholders, especially for study abroad programs. Although the participants were recruited from the Japanese side only, the findings will resonate with study abroad stakeholders and scholars of study abroad programs worldwide.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".