The Imagineering Learning via Metaverse: ILM Model via Metaverse to Promote Creative Thinking Skills
Bibliographic record
Abstract
The ILM model to promote creative thinking skills is concerning the application of the concepts of virtual technology in the instruction management, which is consistent and appropriate for learners in the digital age, so that they are able to learn anywhere and anytime by means of the brand-new teaching innovations. The objectives of this research are (1) to synthesise the conceptual framework of the ILM model, (2) to develop the ILM model, and (3) to study the results of the development of the ILM model. The research tools include (1) the ILM model, and (2) the evaluation form on the suitability of the ILM model. The results show that (1) the overall suitability of the ILM model (overall elements) is at the highest level (Mean = 4.88, SD = 0.14), and (2) the overall suitability of the ILM model is at the highest level (Mean = 4.90, SD = 0.18). This can be summarised that the ILM model is a kind of learning model that was developed by applying the concepts of virtual technology and imagineering learning process that can be used as guidelines to learn anywhere and anytime for the 21st century learners.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".