Meaningful feedback through a sociocultural lens
Bibliographic record
Abstract
This AMEE guide provides a framework and practical strategies for teachers, learners and institutions to promote meaningful feedback conversations that emphasise performance improvement and professional growth. Recommended strategies are based on recent feedback research and literature, which emphasise the sociocultural nature of these complex interactions. We use key concepts from three theories as the underpinnings of the recommended strategies: sociocultural, politeness and self-determination theories. We view the content and impact of feedback conversations through the perspective of learners, teachers and institutions, always focussing on learner growth. The guide emphasises the role of teachers in forming educational alliances with their learners, setting a safe learning climate, fostering self-awareness about their performance, engaging with learners in informed self-assessment and reflection, and co-creating the learning environment and learning opportunities with their learners. We highlight the role of institutions in enhancing the feedback culture by encouraging a growth mind-set and a learning goal-orientation. Practical advice is provided on techniques and strategies that can be used and applied by learners, teachers and institutions to effectively foster all these elements. Finally, we highlight throughout the critical importance of congruence between the three levels of culture: unwritten values, espoused values and day to day behaviours.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".