Adapting the Gamified Educational Networking (GEN) Learning Management System to Deliver a Virtual Simulation Training Module to Determine the Enhancement of Learning and Performance Outcomes
Bibliographic record
Abstract
Microtomy is a medical laboratory sciences procedure that medical laboratory technologists (MLTs) use to cut tissues for microscopic examination. Due to safety concerns and the potential to destroy tissue samples, learners must perform the procedure correctly. In order to allow for safe and controlled learning, this procedure should be conducted in a simulated setting before attempting with human tissues. The objective of this study is to describe the development and user-based evaluation of a virtual simulation training module. A research group developed the virtual simulation training module's content and design, and a local MLT expert provided the content. Nine students enrolled in a university-based medical laboratory sciences program provided feedback about the module. The results demonstrated that the virtual simulation training module was an effective and user-friendly learning tool for the medical laboratory sciences program. Although more validity and efficacy testing are required in the future, the students indicated a potential to use this module to prepare future students for hands-on exercise in a simulation laboratory setting.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".