The Importance of the Application of the Metaverse in Education
Bibliographic record
Abstract
In the early 90's, and especially in some American universities, with the emergence of virtual reality, virtual environments and their manipulation began to be implemented, achieving important advances that have led to improvements in research through changes in the perception of the subject, modeling, communication processes and the development of 3D virtual classrooms. These advances have led to the metaverse, as an environment where humans interact socially as avatars, and especially its application in the world of education in the fields of entertainment, tele-education, educational research, learning environments, etc. According to technologists, this year will separate the thinkers from the builders, and then the technical advances of the last few years will produce the first steps this year in making the metaverse a reality. Advances will largely be attributed to ever-better graphics processing units (GPUs), photorealistic 3D engines, faster content generation through volumetric video and artificial intelligence, the increasing prevalence of cloud computing and 5G, as well as a more sophisticated and better understood blockchain infrastructure. But from a human experience perspective, one development stands out above all others: extended reality (XR) technologies. These include virtual reality (VR), augmented reality (AR) and brain-computer interfaces (BCI), which together are positioned as the next computing platforms in their own right.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".