Understanding Students’ Experiences of Well-Being in Learning Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>With the recent release of a new international charter on health promoting universities and institutions of higher education, universities and colleges are increasingly interested in providing learning experiences that enhance and support student well-being. Despite the recognition of learning environments as a potential setting for creating and enhancing well-being, limited research has explored students’ own perceptions of well-being in learning environments. This article provides a qualitative exploration of students’ lived experiences of well-being in learning environments within a Canadian post-secondary context. A semi-structured focus group and interview protocol was used to explore students’ own definitions and experiences of well-being in learning environments. The findings illuminate several pathways through which learning experiences contribute to student well-being, and offer insight into how courses may be designed and delivered in ways that enhance student well-being, learning and engagement. The findings also explore the interconnected nature of well-being, satisfaction and deep learning. The relevance for the design and delivery of higher education learning experiences are discussed, and the significance of the findings for university advancement decisions are considered.</p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it