CVRriculum Program Faculty Development Workshop: Outcomes and Suggestions for Improving the Way We Guide Instructors to Embed Virtual Reality Into Course Curriculum
Bibliographic record
Abstract
Experiential education and student engagement are a main source of student attraction and retention in post secondary milieus. To remain innovative, it is imperative that universities look beyond the internet and traditional multimedia mediums and incorporate novel ways and cutting-edge technologies that can drastically change the way students and educators experience learning. The application of technology as an approach to experiential education is becoming more popular and has extensively impacted universities and other higher education organizations around the world. One approach to support this change in education delivery is to use immersive technologies such as virtual reality (VR). Our team has conducted a pilot study that focuses on embedding VR as a medium to teach empathy within higher education milieus. We began the study by conducting a pilot faculty development workshop to provide an understanding of VR and ways it can be embedded as a pedagogical approach to support curriculum design. Five faculty members from a local university were recruited to participate. Outcomes suggest that embedding VR into the curriculum is a feasible approach that provides an engaging learning environment that is effective for teaching an array of interpersonal skills. The workshop laid the foundation for future faculty training programs guiding the use of VR, prompting a dialog regarding plans for future workshops across a pan-university context.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".