Research Trends and Gaps in the Adoption of Immersive Reality Technologies in African Education Systems
Year: 2024
Type: article
Abstract: Immersive reality technologies (IMRT) hold transformative potential for educational practices. While there have been studies on the application of immersive technologies in education, fewer studies have attempted to map research trends and gaps in Africa with the continent’s peculiarities. This mixed-methods bibliometric study analyses scholarly publications by African researchers and institutions to identify trends, research hotspots, and collaboration networks in the application of IMRT in education and training. Utilizing the SCOPUS database to generate the dataset, descriptive analysis and vosViewer were used to make sense of the data on publication output trends, publication by country and affiliations, funding, citation metrics, and co-authorship patterns from 2014 to 2023. Findings reveal a steady increase in immersive reality technology research around education. Academics affiliated with African institutions mostly cite academics from the western world than they cite fellow African academics. Results suggest limited local funding for research with significant funding contributions coming from external organisations. This may suggest that research directions on IMRT in African is heavily influenced by outside academics. Keyword analysis suggests that research in IMRT related to education is more biased towards higher education than secondary and primary education. Data analysis on author citations suggest significant recent research focus on how IMRT could impact curriculum reform and implementation. Research gaps exist in the application of IMRT in primary and secondary education. Policy makers could increase local funding in research directed towards coupling the application of immersive technologies with generative artificial intelligence tools in primary and secondary education.
Author: Remeredzayi Gudyanga
Institution: University of the Free State
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405494840