Metaverse Virtual Learning Management Based on Gamification Techniques Model to Enhance Total Experience
Bibliographic record
Abstract
This research aimed to develop metaverse virtual learning management based on gamification techniques model (MVLM-Gt model) and to evaluate the appropriateness of the model. The research methodology was divided into two parts in accordance with these aims. The first part was the design of the learning step using metaverse virtual learning management and gamification techniques based on the gamification process and active learning theory. The second part consisted of an evaluation of the appropriateness of the model. The MVLM-Gt model was submitted to seven experts followed by an appropriateness questionnaire. The MVLM-Gt model had four core components: the inputs, the learning process, the evaluation, and the feedback. The learning process had five steps: motivation and setting goals, constructing content, discussion and interaction, practice and mission, and summarizing and feedback. After evaluation of the appropriateness of the MVLM-Gt model, the experts said that it was excellent (Mean=4.82, S.D.=0.38). After considering each component, the feedback component had the highest appropriateness value (Mean=5.00, S.D.=0.00), followed by the learning process component (Mean= 4.86, S.D.=0.38), and the evaluation component (Mean=4.71, S.D.=0.49). The results showed that this MVLM-Gt model could be adopted to enhance total experience of students.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".