Architecture of the Micro-learning Platform Mixed with Gamification via Metaverse to Promote Creative Problem-solving Skills
Bibliographic record
Abstract
The architecture of the micro-learning platform mixed with gamification via metaverse is a research tool that was initiated by the concepts of micro-learning integrated with the gamification mechanism. It is intended to be employed as a guideline for the instruction management that encourages learners to perform self-learning with small or short units of content so that they can understand the contents easily and spend a short time in learning. In addition, it is expected that the use of the gamification mechanism, which is a learning mechanism in the form of a game, in this learning platform can motivate learners to engage more in learning and overcome the challenges therein. This study also relies on the pre-experimental research method with the one-shot case study, in which all the research participants were willing to complete the questionnaire and the evaluation form under the policy of confidentiality and anonymity. The research results show that (1) the overall suitability towards the architecture of the micro-learning platform mixed with gamification via metaverse in terms of overall elements is at the highest level (Mean = 4.91, SD = 0.21), and (2) the overall suitability of the elements of the architecture of the micro-learning platform mixed with gamification via metaverse is at the highest level (Mean = 4.85, SD = 0.30), respectively. According to the research results, it can be concluded that the architecture of the micro-learning platform mixed with gamification via metaverse designed in this study can be applied as a guideline to further design other micro-learning platforms mixed with gamification via metaverse that can be practically used to promote creative problem-solving skills. In the meantime, the learning platform of this kind is believed to enable learners to improve their critical thinking skills with flexible problem-solving processes. All of these skills are considered highly important for the future careers of vocational students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".