Application analysis of virtual reality technology in teaching and learning
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
As an innovative educational tool, virtual reality technology is profoundly influencing the way teaching and learning are conducted. This paper first outlines the definition and characteristics of virtual reality technology and its theoretical foundations in education, including constructivist learning theory and multiple intelligences theory. By analyzing teaching applications in different subject areas and incorporating actual data tables, we explore the advantages of virtual reality technology in enhancing student satisfaction, engagement, and learning outcomes. The study finds that compared to traditional teaching methods, the teaching approach incorporating virtual reality technology increases the average score by 13% and improves the excellent rate by 25%. However, the application of virtual reality technology in education also faces challenges such as high hardware costs and insufficient teacher training. To address these issues, this paper proposes solutions like enhancing teacher training and developing high-quality teaching resources, and looks into the future development of virtual reality technology in education.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it