A Digital Learning Ecosystem through Metaverse Experiences to Develop Modern Digital Entrepreneurs Competencies
Bibliographic record
Abstract
This study explores the development and evaluation of a digital learning ecosystem through metaverse experiences aimed at enhancing the competencies of modern digital entrepreneurs. The objectives were as follows: (1) to study the digital learning ecosystem through metaverse experiences, (2) to design and develop a digital learning ecosystem using a metaverse experience, and (3) to study the results of using the digital learning ecosystem through metaverse experiences. The research method was divided into three phases based on these objectives. The first phase involved studying the digital learning ecosystem through metaverse experiences. In the second phase, the digital learning ecosystem for design and development was evaluated for its effectiveness by twelve experts using a suitability assessment form. The third phase examined the outcomes of using the digital learning ecosystem through metaverse experiences, focusing on digital entrepreneurs’ competencies and innovative thinking skills. The study included 30 participants from higher education institutions in Thailand. The findings revealed a significant improvement in learners’ digital competencies post-intervention with a large effect size (Cohen’s d = 1.72), indicating the substantial impact of the digital learning ecosystem through metaverse experiences. Additionally, there was a notable enhancement in innovative thinking skills as evidenced by high mean scores in creativity, problem-solving, value creation, presentation, and implementation. The experts found the digital learning ecosystem through metaverse experiences to be highly appropriate and effective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".