The Project-based Learning using Design Thinking Model via Metaverse to Enhance Buddhism Innovators
Bibliographic record
Abstract
The project-based learning using design thinking model via metaverse to enhance Buddhism innovators is integrating project-based and design thinking processes. Besides, using technology in the virtual world promotes learning activities. The model focused on allowing learners to study independently with instructors as counselors and facilitating learning to encourage learners to Buddhism innovators, to spread Buddhism by organizing learning activities in a virtual environment. This research aims to create and study the results of developing project-based learning model using design thinking via metaverse to enhance Buddhism innovators. The samples were five experts in developing the model from various institutions in higher education, selected by purposive sampling. The results showed that 1) the project-based learning model using design thinking consists of preparation, define topics, create and test, present and evaluate, and 2) evaluating the appropriateness of the proposed model. It was found that (2.1) Project-based learning models using design thinking via metaverse (Integrated elements) are appropriate at the level of highest, (2.2) The project-based learning model using design thinking via metaverse (Individual element) is appropriate at the highest level, (2.3) The learning process with a project-based learning model using design thinking via metaverse is appropriate at the highest level, and (2.4) A project-based learning model based on design thinking via metaverse (Implementation), is appropriate at the highest level. It found that a project-based learning model using design thinking via metaverse can be a guideline for learning to enhance Buddhism innovators.
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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 | 0.005 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".