Meaningful feedback through a sociocultural lens
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
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.
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 | 0.001 | 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) | 0.052 | 0.004 |
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