The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Potential to use metaverse for future teaching and learning
Open accessAbstract
Metaverse, a virtual shared space integrating augmented reality and virtual reality technologies, is often hailed as the “Internet of the future” for its potential to revolutionize online communication, collaboration, learning, and work. However, despite its promising strategic and business applications, there is a dearth of empirical evidence for evaluating its educational value. This qualitative study examined the impact of Metaverse on teaching and learning by examining its advantages and challenges. The outcomes highlight Metaverse’s remarkable potential for personalized and adaptive learning thanks to its immersive and interactive capabilities. An analysis of the existing literature suggests that Metaverse can create engaging learning experiences in which students can explore tailored virtual environments and interact within them. Semi-structured in-depth interviews were conducted to confirm the perception of metaverse-based education among students, educators, and program administrators, who view it as an innovative and effective educational delivery method. Several barriers require attention to ensure the successful integration of Metaverse in education, such as network connectivity, reliability, standardization and certification, organizational readiness, and the specialized skills necessary to leverage Metaverse’s potential. This study offers a comprehensive understanding of Metaverse’s potential, limitations, and critical factors contributing to its education success. By providing immersive and interactive learning experiences tailored to individual student needs, Metaverse holds the power to revolutionize teaching and learning. However, addressing challenges related to interoperability and the need for more evidence on its efficacy requires further research and development efforts. The significance of this study hinges on the continued exploration of Metaverse to enhance educational experiences, offering a novel and innovative approach to teaching and learning.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
Mystakidis, S. (2022) · Encyclopedia · doi:10.3390/encyclopedia2010031 · Cited by 1,977
Sampling in Qualitative Research: Insights from an Overview of the Methods Literature
Gentles, S., Charles, C., Ploeg, J., & McKibbon, K. A. (2015) · The Qualitative Report · doi:10.46743/2160-3715/2015.2373 · Cited by 771
Fertile LAND: Pricing non-fungible tokens
Dowling, M. (2022) · Finance Research Letters · doi:10.1016/j.frl.2021.102096 · Cited by 408
Augmented reality in education: An overview of twenty-five years of research
Bacca-Acosta, J., Avila-Garzon, C., Kinshuk, ., Duarte, J., & Betancourt, J. (2021) · Contemporary Educational Technology · doi:10.30935/cedtech/10865 · Cited by 206
Baía Reis, A., & Ashmore, M. (2022) · International Journal of Performance Arts and Digital Media · doi:10.1080/14794713.2021.2024398 · Cited by 99
Metadata
- Authors
- Onu, P., Pradhan, A., & Mbohwa, C.
- Venue
- Education and Information Technologies
- Published
- 2023-09-02
- Volume
- 29
- Issue
- 7
- Pages
- 8893-8924
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4386391938
- Cited by
- 300
- References
- 6
- FWCI
- 27.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.