Factors influencing behavior intentions to use virtual reality in education
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
Virtual reality (VR) is a new technology that has applications in a variety of sectors, including medical, education, gaming, psychology, and sociology. The application of VR in education is intriguing and warrants further examination, but research on the subject is currently restricted. VR can benefit education by allowing students to participate in memorable and engaging experiences that they would not otherwise be able to have. Traditional approaches are still used to teach students, which is an essential element of the curriculum for those who want to conceive problem-solving. As a result, there is a scarcity of study on VR deployment. In this paper, we investigated the factors affecting the adoption of VR in higher educational institutes. To this end, we extended the technology Acceptance Model (TAM) with four additional factors and formulated a set of hypotheses. The hypotheses are then evaluated using a dataset collected from 503 Jordanian students. The result shows that the factors perceived facilitating condition, perceived effort expectancy, and perceived compatibility significantly affected the intention to use VR systems and tools for educational purposes. We believe that this study will help decision makers to build sustainable learning and educational systems in Jordan universities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.003 | 0.002 |
| 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