[DC] Multicultural Learning in Virtual Reality to Promote Global Citizenship Education
Bibliographic record
Abstract
Due to the pandemic, we have become more reliant on digital technologies in every aspect of our lives. This has led to an increasing interest in teaching and training students and employees using immersive technologies and increasing multicultural learning and digital literacies in schools. This research-creation Ph.D. study aims to advance the understanding of the impact of interactive virtual reality non-fiction (VRNF) narratives as experiential learning tools to promote global citizenship education and to counter radicalization among high school and university students. The researcher will explore the effect of interactive VRNF narratives on open-mindedness trait and intercultural communication competence as moderators for multicultural learning in VR. This study also seeks to design and evaluate a VRNF narrative based on educators'/creators' feedback and contribution. This narrative will reflect a personal journey of multicultural learning in the Canadian context. A mixed method approach will be used: Delphi interview method and quasi-experimental study to examine the effects of interactivity and immersion on narrative transportation and user engagement. Results and recommendations will then be reported for further research and implementation.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".