Does Volunteering in a Language Learning Centre Help Non-Native English Speaking Students’ Emotional Well-Being?
Bibliographic record
Abstract
Feeling connected as one navigates life as a post-secondary student is a challenge for many students, both domestic and international.Many non-native speakers of English may not feel a sense of belonging or social connectedness at university and have emotional and other needs hindering their adjustment and success at university.Institutions often have various opportunities for students to volunteer at events, support centres, and other university units.Self-access centres, such as language learning centres and writing centres, have become common at many institutions and often have many student volunteers, making these an ideal environment for research on students.This study took place in one of these centres at a university in Canada -a language learning centre.In this study, researchers used a mixed methods approach to explore student volunteer perceptions.Survey responses that related to the emotional well-being of student volunteers were highlighted for this study.The data from the survey were then cross-referenced with the transcripts of the focus group study for further confirmation.Findings indicate that the act of volunteering in the centre made an impact on student volunteers' emotional well-being.It gave students a sense of belonging and the feeling that they were part of a greater community.It also helped reduce loneliness and build self-esteem.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".