An Exploration of Feedback Using Hattie and Timperley’s Feedback Levels
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Effective feedback is recognized as essential to clinical training. Hattie and Timperley conducted a comprehensive review of feedback to develop their Model of Feedback to Enhance Learning (MFEL). The MFEL proposes that effective feedback can focus on any of four levels: task, process, self-regulation, and self. While Hattie and Timperley are frequently cited for their review, few studies in medical education have used the MFEL to explore feedback. We used the MFEL to examine the content of documented workplace-based feedback to explore how this model applies in a family medicine residency program. METHODS: We conducted this retrospective cross-sectional observational secondary data analysis (learning analytics) study in a Canadian university-based family medicine residency program. Our data source was de-identified field notes (a tool to document workplace-based feedback) for residents at two teaching sites. We coded the feedback using the levels from the MFEL. We used descriptive statistics to analyze the frequencies of each level and combinations of levels. RESULTS: Of the 2,250 field notes examined, 422 (18%) were excluded because they contained no feedback. The majority (1,105; 60%) included a single feedback level, while 705 (38%) contained two levels, and 17 (1%) included three levels. No field notes included all four levels. Of the field notes containing one feedback level, the most common levels were task (835; 76%) and process (248; 22%). The most common combination of levels was process and task (649; 92.1%). CONCLUSIONS: Hattie and Timperley's MFEL offers a way to explore feedback documented in medical education programs and may help programs identify opportunities for faculty development to improve feedback effectiveness.
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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 | 0.037 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".