The Development of Digital Content in the Metaverse Combined with Participatory Communication and Learning with Religious Leader to Enhance Students’ Perception of the Community Mosque
Bibliographic record
Abstract
As a lot of juvenile Muslims tend to be less engaged with their religion and the history of mosques in their community, this study was conducted to develop a series of digital content in the metaverse combined with participatory communication and learning with religious leader to enhance students’ perception of community Mosque (Darun Naim, Thung Kru District, Thailand). The objectives of this research were to survey needs, to develop and assess the quality of the aforementioned series of digital content, to evaluate students’ perception, and to measure students’ satisfaction. Materials of this study included questionnaires and forms to survey needs, assess the quality of the digital content and media presentation, evaluate subjects perception, and measure their satisfaction. A total of 30 subjects who were students from Al-Bidayah Religious School, Thung Khru District, Bangkok and had been members of Young Muslim Association Ban Khru for at least 1 year were selected through purposive sampling. After questionnaire-based data collection, mean and standard deviation were used as a statistical tool for data analysis. The results of this study showed that the outcome of needs assessment was at a high level. The overall quality of the content and media presentation was at a good and very good level, respectively. The findings of the assessment of students’ perception and satisfaction were at the highest level. In summary, the developed digital content in the metaverse combined with participatory communication and learning with religious leader was of high quality and can be implemented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".