The Virtual Interactive Learning Model using Imagineering Process via Metaverse
Bibliographic record
Abstract
The virtual interactive learning model using imagineering process as a tool to promote happy learning for digital age learners. The concept is based on the combination of virtual learning environment and metaverse in order to create learning experience via virtual community. The objectives of this research are (1) to study and synthesise the conceptual framework of the virtual interactive learning model using imagineering process via metaverse, (2) to develop the virtual interactive learning model using imagineering process via metaverse, and (3) to study the results after using the virtual interactive learning model using imagineering process via metaverse. The participants in this research include seven experts from various institutions, all of whom are specialised in the design and development of instruction models and instruction systems. The research tools consist of (1) the virtual interactive learning model using imagineering process via metaverse, and (2) the evaluation form on the suitability of the virtual interactive learning model using imagineering process via metaverse. The results, which are in consistence with the expectation of the researchers, show that (1) this research can be used as a guideline to develop the virtual interactive learning system using imagineering process via metaverse, which can promote happy learning, and it consists of six steps of imagineering process integrated with learning through virtual environments via metaverse; thereby, users can interact in the virtual world and exchange knowledge with one other through virtual reality technology, (2) the overall suitability of the development of the virtual interactive learning model using imagineering process via metaverse (overall elements) is at the very high level, and (3) the overall suitability of the development of the virtual interactive learning model using imagineering process via metaverse is at the very high level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".