Research Trends in the Use of Virtual Reality in STEM Education: A Content Analysis
Year: 2025
Type: article
Abstract: The aim of this study was to reveal research trends on the use of virtual reality in the field of K-12 STEM education through descriptive content analysis. Within the scope of the research, first of all, ERIC, Science Direct, Scopus and Web of Science databases were accessed and the key concepts of ‘Virtual Reality’ and ‘STEM Education’ were scanned on a topic basis. A total of 364 scientific studies were accessed and for content analysis, a total of 24 articles published between 2008 and 2023 years were included from the ERIC, Science Direct, Scopus, and Web of Science databases. The results show that virtual reality, STEM education, augmented reality, educational technology, and gamification were the most used keywords in scientific research. Content analysis results of articles at the K-12 level showed that ‘Learning/Academic Achievement’, ‘Learning Motivation’ and ‘Self-Efficacy’ have been the most examined variables in the articles. It was found that the most commonly used teaching approaches were experiential learning, digital game based learning and inquiry based learning. It was also observed that the majority of the studies reviewed used desktop VR, because desktop VR applications are less costly and easier to develop than immersive VR applications. In addition, it has been concluded that alternative tests, achievement tests and questionnaires are the most used data collection tools in these studies, and that the study groups generally consist of 31-300 people using easily accessible sampling method. Quantitative studies were the most used research design type. In light of the results obtained, in future studies on the use of VR in STEM education, more comprehensive and holistic results will be obtained in the relevant field by using qualitative and mixed research methods in addition to quantitative methods.
DOI: 10.46328/ijonse.1940
Authors: Adem Koc, Pelin Yildirim
Institutions: Mersin Üniversitesi, Atatürk University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4415138300
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