Improving teaching effectiveness: feedback preferences by teachers on a faculty facing dashboard
Bibliographic record
Abstract
To improve clinical teaching skills, feedback on teachers’ strengths and weaknesses needs to be reliable, timely, and relevant. To provide timely feedback we undertook development of an analytical dashboard to provide learner feedback to our faculty. As dashboard data displays are limited we performed a modified Delphi (mDelphi) method to determine what feedback would be preferred by our faculty. Our study was used to develop a group consensus of how our faculty’s teaching effectiveness data should be presented on an online electronic dashboard to support their needs. A working group of junior and senior faculty, a resident and fellow were asked to provide topics that provided formative and summative feedback for our faculty. Thirteen topics were identified and these were used in a mDelphi process to choose 4–5 topics which were relevant to faculty and be able to be displayed on a faculty facing dashboard. Two rounds of the mDelphi were performed using faculty experts in education of varying levels of experience. The first round of the mDelphi identified ten topics which were given high priority by our experts and the other three were discarded. In the second and final round four topics were given the highest importance for inclusion on the faculty dashboard. Our study identified 4 high priority topics for a faculty teaching scorecard. This study showed that anesthesiology faculty prefer topics relevant to formative rather than summative assessment with an emphasis on benchmarking to other faculty with the goal of improving teaching 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 | 0.033 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".