Harnessing digital issue in adopting metaverse technology in higher education institutions: Evidence from the United Arab Emirates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study delves into the intricate landscape of metaverse technology adoption within higher education institutions in the UAE, investigating the multifaceted interplay of accessibility, technology adaptability, and policies and regulations. Using a cross-sectional research design, data was meticulously collected through a multistage sampling approach, combining probability and non-probability methods. A pretested questionnaire underwent rigorous evaluation, ensuring unbiased item formulation and adherence to best practices. The investigation challenges and extends the Technology Acceptance Model (TAM) by revealing unexpected findings. The absence of a significant relationship between accessibility and metaverse adoption prompts a call for an expanded TAM framework. Surprisingly, a negative correlation between technology adaptability and adoption is highlighted, emphasizing the need for a cautious assimilation approach. Moreover, the research underscores the influential role of policies and regulations in metaverse adoption, advocating for a comprehensive TAM framework that encompasses regulatory dynamics. Findings offer practical implications for stakeholders, policymakers, and institutions, emphasizing diverse adoption facets beyond accessibility. The study contributes to the discourse on metaverse adoption and advances theoretical frameworks for technology integration within educational contexts. The methodology's meticulous design underscores the study's rigor, ensuring the robustness of the insights gleaned from the investigation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| 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 it