Design of the Architecture of the Virtual Learning Community: VLC via Metaverse to Promote Digital Teacher’s Competency
Bibliographic record
Abstract
The architecture of the virtual learning community via metaverse, or VLC via metaverse, to promote digital teacher’s competency is related to the application of the concepts of virtual learning community integrated with virtual reality technology to promote the competency of teachers in the digital age. This is also to equip these teachers with comprehensive digital competency in order that they can apply it in the instruction management in a more efficient manner. In addition, this competency is believed to encourage teachers to have a step-by-step thinking process and enable them to use information technology media more practically with more understanding and creativity. The objectives of this study are (1) to synthesize the conceptual framework of the VLC via metaverse to promote digital teacher’s competency, (2) to design the architecture of the VLC via metaverse to promote digital teacher’s competency, and (3) to study the results of the design of the architecture of the VLC via metaverse to promote digital teacher’s competency. The participants in this research are 7 experts from different institutions, all of whom are specialized in the design and development of instruction systems. The research tools include (1) the architecture of the VLC via metaverse, and (2) the assessment form on the suitability of the architecture of the VLC via metaverse. The results of this research show that (1) the overall suitability of the design of the architecture of the VLC via metaverse (overall elements) is at the highest level (Mean = 4.71, SD. = 0.57), and (2) the overall suitability of the design of the architecture of the VLC via metaverse is at the highest level (Mean = 4.70, SD. = 0.52).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".