Boosting students’ motivation for cultural sensitivity via the use of Metaverse in flipped classrooms
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
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.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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