Enhancing Mathematical Understanding Through Augmented Reality and Virtual Reality: An Experimental Investigation into Educational Benefits.
Year: 2024
Type: article
Study design: Clinical Trial
Abstract: The present research focuses on investigating the advantages of AR and VR to improve the students’ learning of math, compared to typical approaches. An experimental design was employed, involving three groups: AR, VR, and a control group, which will include basic conventional teaching methodologies. Quasi-experimental pre and post-test was used to determine learning outcomes, self-organized engagement surveys were used to check students’ interest in the content of the lesson, data was also gathered from focus group interviews and teacher observations. The outcome shows that overall application of both AR and VR technologies frequently enhanced student’s post-test scores, however, a more considerable increase in scores was observed in the VR team. The participants of the AR and VR groups stated they felt more engaged than the control group; such smart learning experiences motivate and enhance participation. Such testimonies continue to affirm the observational findings by pointing the light to the benefits that were passed on by respondents that included positive experience and improved conceptual knowledge that comes with the use of AR and VR in learning. From these conclusions it can be inferred that the inclusion of AR and VR into mathematics instructions presents a more enhanced and interactive learning than the conventional techniques. Such technologies have the capacity to enhance educational practices and hence the call for more studies to be done in order to resolve the issues concerning implementation of these technologies as well as to look at the long-term advantages. The outcomes support AR and VR to become important factors of educational process by improving student’s performances.
Source: Journal of Electrical Systems
Author: Archana Yadav
Institution: University of Delhi
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7217817573