<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Based on the demand to adapt to the future development of education, the application of virtual reality technology in the field of education is becoming more and more extensive and in-depth. This paper applies virtual reality technology to art history teaching, constructs an art history teaching method using virtual reality technology, and designs a complete teaching process. The EEG signals of students in the teaching process are collected and pre-processed. The features in the EEG signals are extracted by using the SPCNN model with dual convolutional kernels in parallel. The EEG features are output through the convolutional layer and the all-connected hierarchy processing. A support vector machine calculated the maximum distance between the samples and the hyperplane, and the classification and recognition results of EEG features were obtained. The frontal channel TBR change values of each subject student in virtual reality technology art history teaching were significantly lower than those of the traditional art history teaching mode (p&lt;0.01). The frontal F7 channel TBR values of the subject students in different teaching modes were significantly different (p=0.004&lt;0.01). Meanwhile, students gained a strong sense of presence in virtual technology teaching, and the motivation of students to learn art history after participating in virtual reality technology teaching was significantly higher than that of the pre-test. This paper’s teaching model is designed to promote students’ immersion and concentration in art history teaching.</p>
</abstract>

Based on the demand to adapt to the future development of education, the application of virtual reality technology in the field of education is becoming more and more extensive and in-depth. This paper applies virtual reality technology to art history teaching, constructs an art history teaching method using virtual reality technology, and designs a complete teaching process. The EEG signals of students in the teaching process are collected and pre-processed. The features in the EEG signals are extracted by using the SPCNN model with dual convolutional kernels in parallel. The EEG features are output through the convolutional layer and the all-connected hierarchy processing. A support vector machine calculated the maximum distance between the samples and the hyperplane, and the classification and recognition results of EEG features were obtained. The frontal channel TBR change values of each subject student in virtual reality technology art history teaching were significantly lower than those of the traditional art history teaching mode (p<0.01). The frontal F7 channel TBR values of the subject students in different teaching modes were significantly different (p=0.004<0.01). Meanwhile, students gained a strong sense of presence in virtual technology teaching, and the motivation of students to learn art history after participating in virtual reality technology teaching was significantly higher than that of the pre-test. This paper’s teaching model is designed to promote students’ immersion and concentration in art history teaching.

Virtual Reality Technology Applied to the Immersive Experience of Teaching Art History

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on the demand to adapt to the future development of education, the application of virtual reality technology in the field of education is becoming more...

Virtual Reality Technology Applied to the Immersive Experience of Teaching Art History

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 25 abr 2024

Aceptado: 09 jul 2024

DOI: https://doi.org/10.2478/amns-2024-1870

Palabras clave
Electroencephalographic signals, Feature extraction, SPCNN model, Support vector machine, Virtual reality technology

© 2024 Xiaopeng Pei., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Virtual Reality Technology Applied to the Immersive Experience of Teaching Art History

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 25 abr 2024

Aceptado: 09 jul 2024

DOI: https://doi.org/10.2478/amns-2024-1870

Palabras claveElectroencephalographic signals, Feature extraction, SPCNN model, Support vector machine, Virtual reality technology

© 2024 Xiaopeng Pei., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Electroencephalographic signals, Feature extraction, SPCNN model, Support vector machine, Virtual reality technology