‘Discovery Learning’: An account of rapid curriculum change in response to accreditation
Bibliographic record
Abstract
BACKGROUND/AIMS: The purpose of this study was to explore the attitudes and experiences of leaders responsible for making rapid changes to a medical school curriculum in response to an adverse accreditation report. The new curriculum was based on the principles of problem-based learning ('Discovery Learning'), with changes to the way that students were assessed. METHODS: We conducted semi-structured interviews with leaders responsible for education at the school two and a half years after the adoption of the new curriculum. We coded the resulting transcripts to identify major and minor themes expressed by participants. RESULTS: Thirty-five senior leaders, administrators and course directors were invited for the interview; 14 (40%) were interviewed. Five main themes were noted in the data: (1) organization and control of the curriculum; (2) changes in the practices of teaching and learning; (3) effects on faculty members; (4) sources of resistance and (5) attitudes to curriculum change in general. CONCLUSION: This study demonstrates that major curriculum change can be achieved successfully in a short period of time. This study also illustrates some of the problems associated with making rapid changes to the medical school curriculum, and highlights the importance of attitudes to change amongst the leadership of a medical school.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".