Virtual Reality for Education from a User Experience Perspective: A Bibliometric Analysis

Education is shifting from traditional multimedia to immersive Virtual reality (VR) environments. Despite numerous reviews on VR in education, few focus on user experience. Existing reviews mainly use qualitative methods, limiting objective analysis due to small sample sizes. Quantitative analysis can address this by covering more studies. This study explores VR in education from a user experience perspective (VR-E-UEP) using bibliometric methods. Methods: We used the Web of Science (WOS) core collection database and employed VOSviewer and CiteSpace for keyword, evolutionary, and co-citation analyses. Results: VR-E-UEP research hotspots are divided into four clusters: 1) specific VR applications in education, 2) advantages and key concepts of VR in education, 3) data analysis techniques, and 4) key factors affecting user experience. Evolution analysis shows early research focused on VR technology and applications, mid-term research emphasized human factors, and recent studies highlight machine learning. Frequently co-cited research falls into five categories: 1) Definitions and Technologies of Virtual Reality and Augmented Reality, 2) Advantages and Effectiveness Evaluation of Virtual Reality Technology in Education, 3) Applications of Immersive Virtual Reality in Learning and Training, 4) User Experience Measurement, 5) Statistical Analysis Methods and Theoretical Models. Finally, future research directions are discussed.

Authors

Publication Details

Journal
AHFE international
Published
2025-01-01
DOI
https://doi.org/10.54941/ahfe1006014
Primary Topic
Virtual Reality Applications and Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Virtual Reality for Education from a User Experience Perspective: A Bibliometric Analysis

Yang Shi
AHFE international
Virtual Reality Applications and Impacts
article

Virtual Reality for Education from a User Experience Perspective: A Bibliometric Analysis

Yang Shi
article en

Abstract

Education is shifting from traditional multimedia to immersive Virtual reality (VR) environments. Despite numerous reviews on VR in education, few focus on user experience. Existing reviews mainly use qualitative methods, limiting objective analysis due to small sample sizes. Quantitative analysis can address this by covering more studies. This study explores VR in education from a user experience perspective (VR-E-UEP) using bibliometric methods. Methods: We used the Web of Science (WOS) core collection database and employed VOSviewer and CiteSpace for keyword, evolutionary, and co-citation analyses. Results: VR-E-UEP research hotspots are divided into four clusters: 1) specific VR applications in education, 2) advantages and key concepts of VR in education, 3) data analysis techniques, and 4) key factors affecting user experience. Evolution analysis shows early research focused on VR technology and applications, mid-term research emphasized human factors, and recent studies highlight machine learning. Frequently co-cited research falls into five categories: 1) Definitions and Technologies of Virtual Reality and Augmented Reality, 2) Advantages and Effectiveness Evaluation of Virtual Reality Technology in Education, 3) Applications of Immersive Virtual Reality in Learning and Training, 4) User Experience Measurement, 5) Statistical Analysis Methods and Theoretical Models. Finally, future research directions are discussed.

AHFE internationalVol. 162
Openalex Percentile: Top 85%
Virtual Reality Applications and Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.