The Application of a Design-Based Research Framework for Simulation-Based Education
Bibliographic record
Abstract
In research, the adoption of a framework is essential. It enables researchers to operate with specified parameters and provides structure and assistance with research projects, programs, and technologies. The incorporation of a framework also facilitates the organizing and planning of our research efforts with respect to the breadth and depth of what we want to discover. Frameworks are equally important in research focused on simulation-based education. Simulation-based education is a form of experiential learning that provides participants with the opportunity to acquire or improve real-world-like knowledge and skills in a simulated environment. The Medical Research Council framework, historically developed to guide clinical research, has been proposed as a framework to guide simulation research as well. However, because simulation-based education is positioned at the intersection of clinical and educational sciences, certain questions cannot be addressed using a clinical research framework. Thus, in this paper, we introduce an alternative framework, derived from educational sciences, to be considered and possibly adapted into simulation research. The design-based research (DBR) framework consists of four stages centered on design, testing, evaluation, and reflection. This editorial asserts that the DBR is an excellent framework for projects and programs of research in simulation.
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 | 0.143 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".