The Use of Metaverse for Delivering School Education in the Future in UAE: Advantages and Challenges
Bibliographic record
Abstract
The researcher aimed at identifying the advantages of using Metaverse for delivering school education in UAE in the future. He also explored the challenges associated with such use. He used the descriptive analytical and quantitative approaches. He created a survey that consists from four main parts through using Google Form. The validity and reliability of the survey were checked. Then, the link of the survey was uploaded to several Facebook groups. Three hundred twenty seven (327) private school teachers in UAE filled in the survey. Thus, the random sampling method was used. After the use of the SPSS software, results were reached. 88.37 % of the respondents support such use of Metaverse. In terms of the advantages associated with such use, it was found that such use of Metaverse shall develop the students’ observation, teamwork, analytical thinking and problem solving skills. Such use of Metaverse shall also raise the students’ academic achievement levels and extent of retaining information. In terms of the associated challenges, it may make students feel isolated and lonely. It is associated with difficulties in protecting the students’ privacy and data security. It shall be associated with difficulty in protecting the students from becoming victims for commercial ads of all kinds.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".