Constructionism Imagineering Learning Model via Metaverse to Enhance Young Innovators
Bibliographic record
Abstract
Constructionism imagineering learning model via metaverse is an instrument for promoting self-learning through hands-on. To create new knowledge for young innovators by combining the concepts of technology and new learning platforms to create new ideas. Designing teaching and learning that can be used to learn in the new normal focuses on continuous learning at any time, anywhere, with the benefits of using technology. The sample group is six experts in designing and developing learning models and learning systems from various institutions in higher education by purposive sampling. The research instruments are as follows. 1) The constructionism imagineering learning model via metaverse to enhance young innovators. 2) The constructionism imagineering learning process via metaverse to enhance young innovators. 3) An assessment form for the constructionism imagineering learning model via metaverse to enhance young innovators. 4) An assessment form for the constructionism imagineering learning process via metaverse to enhance young innovators. Analyse data using mean and standard deviation. The researchers found that the constructionism imagineering learning model via metaverse and the constructionism imagineering learning model via metaverse, which is developed is appropriate to enhance young innovators at the highest level, following the research hypotheses.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".