Studying Abroad from Home: An Exploration of International Graduate Students’ Perceptions and Experiences of Emergency Remote Teaching
Bibliographic record
Abstract
The temporary shift from face-to-face instruction to online teaching at North American universities as an alternative solution in response to the COVID-19 pandemic brought significant challenges to international students who had to study abroad from their home countries. Studies on how international students perceive their study-abroad-from-home experiences in such an emergency remote teaching (ERT) context remain scarce. Through the lens of community of inquiry and an additional perspective of emotional presence, this study explored 13 first-year international graduate students’ perceptions and experiences of their learning in ERT. Based on the analyses of the pre-learning questionnaire survey results and a series of three reflection journal entries, the study finds that teaching presence has played a vital role in shaping students’ understanding and experiences when they participated in a study-abroad graduate program from their home countries. In addition, the participants demonstrated mixed emotions of both frustration and appreciation/thankfulness as well as an isolation–connectedness emotional trajectory during their learning process. The study inspires an exploration of more diverse options for international education programs and continued effort in providing institutional support to ensure better learning experiences in a post-COVID community of inquiry.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".