Exploring the Landscape of Virtual Reality in Education
Bibliographic record
Abstract
Virtual reality (VR) technology has emerged as a promising tool for enhancing education and transforming the way learners interact with the learning environment. This bibliometric and thematic analysis aims to explore the use of VR in education and the challenges and opportunities in this area. A systematic search of the Scopus database was conducted using specific keywords such as “virtual reality,” “education,” and “learning.” The search resulted in a total of 110 articles published that met the inclusion criteria. The bibliometric analysis revealed an increasing trend in the publication of articles on VR in education over the past decade. The most active journals in this field were Journal of Professional Issues in Engineering Education and Practice, Technology in Society, and IEEE Transactions on Professional Communication. The authors and institutions with the most publications on this topic were from the United States and Canada. Thematic analysis identified six main themes related to the use of VR in education: 1) technology and its application in various fields, 2) use of technology to enhance and transform the learning experience in higher education, 3) concept of technology-enhanced education, and 4) concept of online learning. Future research ideas like effectiveness of VR in enhancing learning outcomes, 2) impact of VR on student engagement and motivation, 3) integration of VR into the curriculum, 4) cultural and international implications of using VR in management and business education, and 5) accessibility and affordability of VR technology in management and business education. The analysis revealed that VR technology has the potential to transform education by creating immersive and interactive learning environments. Virtual reality has been found to enhance learning experiences and improve student engagement by allowing learners to experience concepts and situations that may not be possible in real life. The opportunities for the use of VR in education include improved retention of information, the potential to enhance the development of spatial reasoning skills, and the creation of new learning experiences.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".