Decision PBL: A 4-year retrospective case study of the use of virtual patients in problem-based learning
Bibliographic record
Abstract
BACKGROUND: In 2009, St George's University of London (SGUL) replaced their paper-based problem-based learning (PBL) cases with virtual patients for intermediate-level undergraduate students. This involved the development of Decision-Problem-Based Learning (D-PBL), a variation on progressive-release PBL that uses virtual patients instead of paper cases, and focuses on patient management decisions and their consequences. METHODS: Using a case study method, this paper describes four years of developing and running D-PBL at SGUL from individual activities up to the ways in which D-PBL functioned as an educational system. RESULTS: A number of broad issues were identified: the importance of debates and decision-making in making D-PBL activities engaging and rewarding; the complexities of managing small group dynamics; the time taken to complete D-PBL activities; the changing role of the facilitator; and the erosion of the D-PBL process over time. CONCLUSIONS: A key point in understanding this work is the construction and execution of the D-PBL activity, as much of the value of this approach arises from the actions and interactions of students, their facilitators and the virtual patients rather than from the design of the virtual patients alone. At a systems level D-PBL needs to be periodically refreshed to retain its 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".