Upward Feedback: Exploring Learner Perspectives on Giving Feedback to their Teachers
Bibliographic record
Abstract
Introduction: Feedback from learners is known to be an important motivator for medical teachers, but it can be de-motivating if delivered poorly, leaving teachers frustrated and uncertain. Research has identified challenges learners face in providing upward feedback, but has not explored how challenges influence learners' goals and approaches to giving feedback. This study explored learner perspectives on providing feedback to teachers to advance understanding of how to optimize upward feedback quality. Methods: We conducted semi-structured interviews with 16 learners from the MD program at the University of British Columbia. Applying an interpretive description methodology, interviews continued until data sufficiency was achieved. Iterative analysis accounted for general trends across seniority, site of training, age and gender as well as individual variations. Findings: Learners articulated well-intentioned goals in relation to upward feedback (e.g., to encourage effective teaching practices). However, conflicting priorities such as protecting one's image created tensions leading to feedback that was discordant with teaching quality. Several factors, including the number of feedback requests learners face and whether learners think their feedback is meaningful mediated the extent to which upward feedback goals or competing goals were enacted. Discussion: Our findings offer a nuanced understanding of the complexities that influence learners' approaches to upward feedback when challenges arise. In particular, goal conflicts make it difficult for learners to contribute to teacher support through upward feedback. Efforts to encourage the quality of upward feedback should begin with reducing competition between goals by addressing factors that mediate goal prioritization.
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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".