Collaborative Online Multimedia Problem-Based Learning Simulations (COMPS)
Bibliographic record
Abstract
This chapter describes the development, implementation and evaluation of a Collaborative Online Multimedia Problem-based Learning Simulation (COMPS) instructional model designed to help students and practitioners in the health professions develop clinical reasoning and diagnostic skills. Both students and instructors are searching for effective learning platforms and pedagogical models that enable them to collaborate, study, and work at a distance. In order to address this need, COMPS was developed to support a case-based tutorial model where learners can work together online to solve authentic problems no matter where they are located. The model aims to bring together the strongest features of simulations, namely engagement and immersiveness, with one of the strongest features of face-to-face learning—social interaction. The COMPS model combines these strengths to create a new learning system for health education and examines how students learn in this online environment. This chapter also discusses the next steps in our research and development, investigating the use of a COMPS model on a dedicated platform.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".