Student-centred pedagogical practices to support undergraduate student mental health
Bibliographic record
Abstract
The prevalence of mental illness continues to increase worldwide, and university campuses have not been immune to this trend. A recent national survey found that 43.3% of Canadian post-secondary students indicated anxiety was an “impediment to academic performance” while 30.4% indicated that depression had the same effect. More alarmingly, the same survey found that 2.9% of Canadian post-secondary students attempted suicide in the previous twelve months. At a university the size of Guelph, that translates to roughly 860 undergraduate students. The classroom is the ‘front line’ of education/student interaction and the one common element in every student’s university experience. Instructors play a vital role as both ambassadors of their institutions and as key figures accountable for the learning and growth of their students. The choices they make in their classrooms have an impact that goes beyond course content. Drawing from the University of Guelph’s mission, a guiding principle for my research is commitment “to the highest standards of pedagogy, to the education and well-being of the whole person, to meeting the needs of all learners in a purposefully diverse community”. Using an online survey and semi-structured interviews with students, I hope to determine how instructors’ pedagogical choices positively and negatively impact student mental health. Using this data, I will then design a toolbox of low-risk, easily implementable interventions and recommendations for instructors and program administrators to support student mental health within the science, engineering, technology and mathematics (STEM) classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".