Hybrid Project-Based Learning Model on Metaverse to Enhance Collaboration
Bibliographic record
Abstract
The Hybrid Project-Based Learning Model on Metaverse to Enhance Collaboration. The concept is based on the integration of hybrid learning, project-based learning, and metaverse. This research has the objective: (1) To study and synthesize the conceptual framework of the hybrid project-based learning model on metaverse to enhance collaboration. (2) To develop the hybrid project-based learning model on metaverse to enhance collaboration. (3) To study the suitability of the hybrid project-based learning model on metaverse to enhance collaboration. Research hypothesis: The suitability of the hybrid project-based learning model on metaverse to enhance collaboration is at the high level. The participants in this research include seven experts from various institutions, all of whom are specialized in the design and development of instruction models and systems. The results, show that (1) This research can serve as a guideline for developing a hybrid project-based learning via metaverse that can enhance collaboration, consisting of a 6-step hybrid project-based learning process, integrated with metaverse. (2) the overall suitability of the development to the hybrid project-based learning model on metaverse to enhance collaboration is at a very high level (Mean = 4.92, S.D. = 0.18, IR = 0.04, Q.D. = 0.02), (3) The results of the evaluation certify the suitability of using the hybrid project-based learning model on metaverse to enhance collaboration is suitable for actual use at a very high level (Mean = 4.71, S.D. = 0.76, IR = 0.00, Q.D. = 0.00).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".