Sense of Belonging and Academic Experiences of South Asian International Graduate Students in an Ontario Public University.
Bibliographic record
Abstract
Between 2010 and 2020, Canada saw a 170% increase in international students, drawn by its multicultural environment. Despite this, international students frequently face language, academic, life, and social barriers that impact their sense of belonging and academic success. This study investigated the sense of belonging and academic experiences of South Asian international graduate students, focusing on how belonging influences their academic performance and well-being, the role of interactions with educators and peers, strategies to address challenges, and the impact of microaggressions. The research integrated qualitative and quantitative data from surveys and semi-structured interviews with graduate students from South Asian countries. Findings reveal that a strong sense of belonging positively affected students' academic performance and well-being. Cultural recognition and inclusive practices are crucial for creating an environment where students feel valued and integrated. However, language barriers, administrative challenges, and perceived biases posed significant obstacles. Interactions with educators and peers significantly influenced students' sense of belonging, with positive and respectful engagement playing a pivotal role in fostering a supportive academic environment. Despite most students feeling respected, challenges related to language, cultural differences, and discrimination persist, were identified impacting their integration. The study underscores the need for universities to enhance support through comprehensive academic, practical, and emotional resources, including mentorship and culturally sensitive mental health services. Addressing microaggressions is also vital, as they contribute to social isolation, emotional distress, and academic disengagement. The research highlights the importance of institutional commitment to fostering a sense of belonging to improve academic performance and well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".