An integrated framework for simulation-based training on video and in a virtual world
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Becoming a skilled professional requires both the acquisition of theoretical knowledge and the practice of skills relevant to one’s profession. When learning by doing, students consolidate their knowledge of domain-specific facts by applying them as necessary to accomplish the tasks involved in their profession. Simulation-based learning methods are a family of methods that enable this learning mode. New computer related technologies, including high performance networking, high definition displays, distributed multiplayer game engines, and virtual worlds, bring new opportunities for simulation-based learning methods and systems. In this work, we describe our software framework for specifying simulation-based lesson plans and their implementations on two different platforms: a video based tool and a virtual world environment. We discuss the software architecture of the system, illustrate its functionality with an example lesson on how to conduct oneself in corporate interviews, outline our plans for experimental evaluation, and argue for its usefulness in today’s efforts to creatively use virtual worlds for educational purposes.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it