Evidence-Informed Facilitated Feedback: The R2C2 Feedback Model
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
Abstract Introduction While feedback continues to pose challenges, new understanding is emerging. Feedback is now being seen as an interaction in which learner engagement, supportive relationships, reflection, and cooperative planning are important. In response and through research, we developed and tested the R2C2 model and teaching materials to support its use. Methods R2C2 is an evidence-based reflective model for providing assessment feedback. It includes four phases: (1) relationship building, (2) exploring reactions to the feedback, (3) exploring understanding of feedback content, and (4) coaching for performance change. It provides a strategy for facilitating feedback conversations that promote engagement with performance data and enable coaching for improvement. This package of educational materials includes paper-based and video resources designed to support interactive learning and skills development in facilitating feedback and coaching. Specific strategies are described and demonstrated for each phase of the R2C2 model and include a learning change template for the coaching phase. Resources can be used by an individual or group. A workshop outline with presentation slides and a practice scenario are also included. Results Through research, invited and peer-reviewed presentations, and feedback from colleagues who have used the materials and the R2C2 model, we have learned that the model is intuitive and easy to use, that it can engage the learner and support coaching, and that the educational materials are clear and useful. Discussion The model is intuitive, especially within competency-based education, is easy to follow, and makes sense to faculty, which makes it easy to implement in most programs.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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