Untangling feedback: Mapping the patterns behind the practice
Bibliographic record
Abstract
Although feedback is widely recognized as essential to improving performance and learning outcomes, what feedback involves and what it achieves can vary significantly according to researchers and practitioners. This variability reflects the lack of a shared conceptual framework to unite feedback practices, theories, findings and recommendations. In this paper, the authors use a recently developed pattern system to compare different models of feedback as a way of building a more united perspective. The authors conducted a comparative case study and framework analysis of 11 feedback models across four categories of feedback (augmented sensorimotor feedback, coaching, audit and feedback and multisource feedback). Each model was analysed to identify which aspects of feedback it addressed, and which were overlooked or excluded. The analysis revealed both divergence and convergence in how feedback models mapped onto the pattern system. Divergence was evident in the variability of elements (pattern representations) across models and diversity in expression and granularity of those elements. Conversely, convergence was observed in recurring clusters of elements, such as Performance measurement, Sensor, Judgement and Assessment, which appeared consistently across categories. Overall, the mapping exercise showed significant variations in how feedback is conceptualized, even within specific subcategories such as "coaching," "audit and feedback" and "multisource feedback." These differences have important implications for advancing research and practice in these areas. Pattern theory and pattern mapping offer a promising framework for exploring and addressing the conceptually contested nature of feedback in medical education and may facilitate the future development of a pattern language of feedback.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".