Conditions Fostering International Graduate Students’ Happiness and Engagement During the COVID-19 Pandemic
Bibliographic record
Abstract
This paper focuses on eight conditions that kept international master’s students (IMS) in a Southern Ontario university happy and engaged in their studies during the first and second waves of the COVID-19 pandemic. Using the first phase of the Appreciative Inquiry’s (AI) 4-D cycle—i.e., discovery—this doctoral study conducted 14 individual interviews and three focus group discussions to identify conditions that made the IMS students happier and more engaged despite pandemic-related challenges. The study is crucial in advancing positive experiences of IMS because existing literature has focused primarily on their challenges and problems. The study’s use of AI, a strength-based theoretical and methodological approach, suggests the need to highlight the quality experiences of this minoritized group. Data revealed specific factors that brought about happiness and boosted IMS engagement in their studies, namely: financial and emotional support from family; responsive instructors; employment opportunities during the pandemic; and learning and engaging in extracurricular activities with colleagues and friends. Other conditions also proved crucial to participants’ happiness and engagement in their studies, including: professionalism of non-teaching staff; the institution’s learning management system and supporting online platforms; virtual access to campus software and other learning resources; and reduced travel time. Study findings aim to inform international student policy and enrich the international student experience literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".