Implementing Virtual Simulated Person Methodology to Support the Shift to Online Learning: Technical Report
Bibliographic record
Abstract
The COVID-19 pandemic has dramatically changed how education is delivered worldwide. The resultant rise of e-learning, whereby teaching is undertaken remotely and on digital platforms, has extensively impacted universities and other higher education organizations around the world. One approach to support this change in education delivery is the use of virtual simulation approaches. Our team at SimXSpace has piloted a virtual workshop using Zoom, an online video-conferencing platform, and virtual simulated persons (SPs) to support communication and interpersonal skills among learners. The main objective of the pilot virtual workshop was to develop and implement the SP methodology remotely via the Zoom platform (Zoom Video Communications, San Jose, California) and to evaluate its effectiveness as an immersive environment for simulation. The virtual workshop involved four instructors who intend to implement virtual SPs within their courses, two workshop facilitators, and two SPs. The workshop was conducted synchronously using Zoom features. The workshop followed a predefined structure and was completed as planned. Outcomes suggest that remote simulation delivery using virtual SPs and delivered online via Zoom is feasible and provides an effective environment in which to conduct SP methodology to teach communication and interpersonal skills. The findings suggest that remote simulation and virtual SPs can support experiential education and provide an effective and engaging learning environment. The virtual workshop was successful and laid a foundation for future online training programs for the use of SP methodology. Moreover, it formed an effective outline for subsequent iterations of this virtual training workshop and prompted discussion of plans for future workshops with various programs across a pan-university context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".