Student's Readiness in Using Virtual Reality for Physics Learning
Bibliographic record
Abstract
Virtual Reality (VR) technology in learning activities assists in visualizing abstract phenomena of physics concepts. This technology supports the delivery of effective and meaningful learning experiences. The main objectives of this study, therefore, are to analyze the level of students' readiness to use VR technology in higher education, and to analyze the factors most affecting students' readiness to use VR technology in physics learning. The research employed a questionnaire-survey method with 127 physics education students from a university in Indonesia, distributed based on age, gender, study level, geographical background, and family economic status. Data collection uses a Likert-scale questionnaire containing ten factors of student readiness for using VR. Data analysis techniques included percentages, the Pearson Product Moment Correlation, and multiple regression. The results of this study indicate that the level of students' readiness to use VR technology falls into the intermediate category (71%). The two factors most influencing students' readiness, that were identified from the correlation coefficients, are the availability of access to VR devices and basic technical skills in operating VR technology. Students' readiness to use technology in learning serves as the basis for determining which steps should be prioritized to prepare for technology-enhanced learning, ensuring that the technology positively impacts the learning quality.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".