Current Issue

Studies in Informatics and Control
Vol. 33, No. 3, 2024

Investigation on the use of Virtual Reality in the Teaching of Engineering Education Based on Functional Linked Neural Network

Wenhua ZHANG
Abstract

Contemporary teaching mode principally uses the tools of information technology to achieve the natural integration of information technology, education and teaching. Digital instructional resources are impacting the traditional classroom. Although research on applying Virtual Reality (VR) technology to engineering education remains in its theoretical stages, lacking practical application strategies and plans, the integration of VR with physical education has been a popular trend recently. This paper examines the purpose of VR technology in teaching engineering. The examination architecture on use of VR in the Teaching of Engineering Education based on Functional Linked Neural Network (VR-TEE-FLNN) is proposed. Initially, the data is gathered from student dataset. The control group is instructed using traditional teaching. Deep learning basis Virtual Reality assisted teaching is provided to study the group under Functional Linked Neural Network. In general, FLNN classifier does not adopt any optimization method to define the optimal parameters, in order to achieve the accurate student groups. Therefore, Quantum Henry Gas Solubility Optimization is employed to optimize Functional Linked Neural Network, which accurately classifies the student groups. The statistical analysis is performed by students t-test, logistic regression analysis, and analysis of variance (ANOVA). The metrics, like precision, recall, accuracy, F1-score, specificity, ROC, computational time are analyzed. The proposed VR-TEE-FLNN achieves a greater accuracy of 16.65%, 18.85%, 16.45% and a greater F1-score of 16.34%, 12.23%, 19.12%, when compared to the existing methods, like Deep learning in English Education Blended Teaching under Virtual Reality (DL-EEBT-VR), Combining Real-world in Deep Neural Network of Virtual Reality Biometrics (CRWDNN-VRB), Virtual Reality based on Chemical and Biochemical Engineering Education Training (VR-CBE-ET), respectively.

Keywords

Functional Linked Neural Network, Virtual Reality, Quantum Henry Gas Solubility Optimization.

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