Bibliographic record
Abstract
The use of virtual reality (VR) as an innovative learning tool in higher education has been rising steadily, as being actively engaged in a learning activity has repeatedly been shown to be beneficial for learning (Price et al. 2003). This major research project explores the potential use of VR for ‘soft skills’ training by addressing two main questions: 1) How can we train individuals for complex work environments without exposing vulnerable students to potentially harmful situations?; and 2) how can we create these environments with a role-player simulation? This research paper builds on how VR combined with digital storytelling can be used to build on communication skills training. It suggests that by creating a prototype for an impactful VR experience students can improve their communication skills, and demonstrate higher levels of goal completion required to successfully bring a project to fruition in the 21st-century creative workplace. Mirroring real-world engagements, users assume the role of the manager while working through these virtual challenges in three distinct steps; preparation, delivery, and transition. This novel research suggests that there is a way to effectively combine traditional role-playing techniques while adhering to the new digital standards.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".