4C/ID in medical education: How to design an educational program based on whole-task learning: AMEE Guide No. 93
Bibliographic record
Abstract
Medical education increasingly stresses that medical students should be prepared to take up multiple roles as a health professional. This requires the integrated acquisition of multiple competences such as clinical reasoning and decision making, communication skills and management skills. To promote such complex learning, instructional design has focused on the use of authentic, real-life learning tasks that students perform in a real or simulated task environment. The four-component instructional design model (4C/ID) model is an instructional design model that starts from the use of such tasks and provides students with a variety of learning tools facilitating the integrated acquisition of knowledge, skills and attitudes. In what follows, we guide the reader on how to implement educational programs based on the 4C/ID model and illustrate this with an example from general practice education. The developed learning environment is in line with the whole-task approach, where a learning domain is considered as a coherent, integrated whole and where teaching progresses from offering relatively simple, but meaningful, authentic whole tasks to more complex tasks. We describe the steps that were taken, from prototype over development to implementation, to build five learning modules (patient with diabetes; the young child with fever; axial skeleton; care for the elderly and physically undefined symptoms) that all focus on the integrated acquisition of the Canadian Medical Education Directives for Specialists roles in general practice. Furthermore, a change cycle for educational innovation is described that encompasses practice-based challenges and pitfalls about the collaboration between different stakeholders (students, developers and teachers) and the transition from traditional, fragmented and classroom-based learning to integrated and blended learning based on sound instructional design principles.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.060 |
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".