A novel model for curriculum design: Preparation, planning, prototyping, and piloting
Bibliographic record
Abstract
Dental education continuously strives to provide students with positive and meaningful learning experiences. Developing or improving a curriculum usually encompasses three main phases: design, implementation, and evaluation. Most research on curriculum development in dental education has focused on the last two phases. Our commentary addresses this gap by describing a new model for curriculum design that effectively guided the design phase of the complete overhaul of the four-year Doctor of Dental Surgery curriculum at the School of Dentistry, University of Alberta. Built on the strengths of pre-existing curriculum design models, the new model provided enough structure and rigour to support the complexity required during a complete curriculum redesign whilst still allowing sufficient consultation and flexibility to encourage stakeholder engagement. The steps of the new 4P's model (preparation, planning, prototyping, and piloting) and main actions within each step are described. Challenges observed in each step and strategies to address them are reported. Other institutions embarking on renewing or redesigning a curriculum at a program level may benefit from using a curriculum design process similar to the 4P's model. Recommendations are discussed including the inclusion of educational consultants in the curriculum renewal committee, the importance of a leadership that effectively supports curriculum reform, purposeful engagement of stakeholders during each step of the design phase and ensuring that project and change management occur concurrently.
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.
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 | 0.038 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".