Boosting students’ motivation for cultural sensitivity via the use of Metaverse in flipped classrooms
Bibliographic record
Abstract
Cultural sensitivity, the ability to interact with diverse cultural backgrounds, is often developed through experiential learning, such as service-learning. Flipped learning involves students reviewing theoretical information before practicing skills in class. Undergraduate students increasingly consist of Generation Z, whose growing environment filled with technology, prefer active online learning. This study examined the use of Metaverse as a flipped learning approach to enhance undergraduate students’ cultural sensitivity. Twenty-two undergraduates used the Metaverse to learn about ethnic minorities and engage in interactive activities before designing service projects. Questionnaires were used to gather their insights on this experience. Results indicated high satisfaction with the Metaverse, as it enhanced their understanding of ethnic minorities and offered reflection opportunities. Interactive activities and the design of virtual environment (e.g., tasks-orientated) in the Metaverse were key in motivating students to develop cultural sensitivity through peer collaborations. These findings offer valuable insights for educators on optimizing Metaverse design.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".