Using Appreciative Inquiry to Understand the Role of Teaching Practices in Student Well-being at a Research-Intensive University
Bibliographic record
Abstract
Appreciative inquiry (a research approach comprising four stages: Discovery, Dream, Design, and Destiny) was used at a research-intensive university to investigate which teaching practices positively influence student well-being (i.e., their health and quality of life). In a survey, undergraduate students were asked to select the teaching practices they believed best supported their well-being. Focus groups also were conducted, with: (1) students, and (2) instructors identified by students as using teaching practices that supported their well-being. Mixed-methods data-analyses subsequently were used to identify instructional strategies that support student well-being. L’enquête appréciative (une approche de recherche qui comprend quatre étapes : découverte, rêve, conception et destinée) a été utilisée dans une université centrée sur la recherche pour enquêter sur les pratiques d’enseignement et déterminer lesquelles influencent positivement le bien-être des étudiants (c’est-à-dire leur santé et leur qualité de vie). Dans un sondage, on a demandé aux étudiants de premier cycle de choisir les pratiques d’enseignement qui, selon eux, favorisaient le mieux leur bien-être. Des groupes de discussion ont également été organisés, avec (1) des étudiants et (2) des instructeurs identifiés par les étudiants comme étant ceux qui employaient des pratiques d’enseignement qui favorisaient leur bien-être. Ensuite, les données ont été analysées selon des méthodes mixtes pour identifier les stratégies d’instruction qui favorisent le bien-être des étudiants.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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 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".