Positive Experiences, Dreams, and Expectations of International Master’s Students at a Southern Ontario University: An Appreciative Inquiry
Bibliographic record
Abstract
This study used appreciative inquiry (AI) as a methodological and theoretical framework and positive psychology theory to investigate international master’s students’ positive experiences, dreams, and expectations in their programs and institution to inform policies, programs, and practices. Although the literature describes international students’ mixed experiences in Canada, including developing critical thinking skills, making friends with other nationals, culture shock, and financial challenges, previous studies seldom focus on life-affirming conditions that enrich and improve such students’ schooling experiences. The first three stages of AI’s 4-D cycle—discovery, dream, and design—informed the study’s data collection methods (14 semi-structured individual interviews and three focus group discussions) to generate strength-based data for analysis, resulting in five key themes: (a) personal well-being and sense of belonging, (b) instructors’ pedagogical practices, (c) financial constraints and employment opportunities, (d) career development, and (e) policies. Based on its findings, the study makes six recommendations to inform international graduate student policy and practice: (a) allow international master’s students to study with their domestic counterparts, (b) increase international student diversity, (c) regularize socializing events for students and community members, (d) bridge the gap between theory and practice (hands-on experience), (e) work with all stakeholders to make international master’s students’ tuition fees more affordable, and (f) create on- and off-campus employment opportunities. Participants’ first-person accounts emphasize the need to include student voices in their own education and also shift the conversation from a deficit lens to a more positive discourse to balance the narratives around international students’ 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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".