Understanding the Connection Between Student Wellbeing and Teaching and Learning at a Canadian Research University: A Qualitative Student Perspective
Bibliographic record
Abstract
Postsecondary students’ ability to learn is affected by their mental health and wellbeing. Research in the teaching and learning context, however, has predominantly focused on teaching practices that facilitate motivation, learning, and academic success while overlooking the importance of student mental health and wellbeing. The current study aimed to fill this gap by using qualitative interviews to explore student perspectives on current and possible future supports that can cultivate student mental health and wellbeing in the teaching and learning context. Through 14 one-on-one interviews with students, five major themes were developed: (1) prioritize mental health, (2) provide and guide to accessible supports, (3) increase mental health literacy, (4) foster connections and social support, and (5) strengthen best practices in teaching and learning. Students emphasized that the institution has a role to play in several of these areas and elaborated on what practices and policies were least and most supportive of student mental health and wellbeing in teaching and learning. This study has implications for higher education institutions, and how they promote mental health and wellbeing, disseminate information and resources, and how faculty and staff can support students through their policies (e.g., flexibility in deadlines), course materials (e.g., assessments), course delivery (e.g., equity, diversity, and inclusion [EDI] considerations), and interactions (e.g., normalizing mental health conversations).
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".