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

<p>The integration of artificial intelligence and education is a general trend, and it has become necessary to utilize artificial intelligence for teaching. This study takes the application of artificial intelligence technology in teaching strategy as the starting point, constructs an intelligent classroom model for recognizing students’ faces and emotions, and better understands their learning state. The method first carries out emotion recognition based on a support vector machine, then realizes the fusion of the decision layer through a posteriori probability, completes the construction of a multimodal emotion recognition model, and finally applies the PAD scale to quantify the emotion and analyze the emotion and state of the sample students in various stages of the classroom. The artificial intelligence teaching strategy that integrates multimodal emotion recognition is constructed and its application effectiveness is examined. More than 60% of the students were in a calm state in the 10 minutes before and after the class, and 78% of the students were in a positive learning mood in the 25 minutes of the class, so the multimodal emotion recognition model has a good effect of recognizing students’ emotions. After the teaching practice is carried out, the student’s performance in the experimental class is 12.29% higher than that in the control class. The learning stability is better, reflecting the superiority of the teaching strategy in this paper. This study can provide a reference for the teaching of the classroom of the future integration of artificial intelligence technology.</p>
</abstract>

The integration of artificial intelligence and education is a general trend, and it has become necessary to utilize artificial intelligence for teaching. This study takes the application of artificial intelligence technology in teaching strategy as the starting point, constructs an intelligent classroom model for recognizing students’ faces and emotions, and better understands their learning state. The method first carries out emotion recognition based on a support vector machine, then realizes the fusion of the decision layer through a posteriori probability, completes the construction of a multimodal emotion recognition model, and finally applies the PAD scale to quantify the emotion and analyze the emotion and state of the sample students in various stages of the classroom. The artificial intelligence teaching strategy that integrates multimodal emotion recognition is constructed and its application effectiveness is examined. More than 60% of the students were in a calm state in the 10 minutes before and after the class, and 78% of the students were in a positive learning mood in the 25 minutes of the class, so the multimodal emotion recognition model has a good effect of recognizing students’ emotions. After the teaching practice is carried out, the student’s performance in the experimental class is 12.29% higher than that in the control class. The learning stability is better, reflecting the superiority of the teaching strategy in this paper. This study can provide a reference for the teaching of the classroom of the future integration of artificial intelligence technology.

A Study of Teaching Strategies Optimized with the Integration of Artificial Intelligence Technologies

Applied Mathematics and Nonlinear Sciences

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

The integration of artificial intelligence and education is a general trend, and it has become necessary to utilize artificial intelligence for teaching. This...

A Study of Teaching Strategies Optimized with the Integration of Artificial Intelligence Technologies

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 17. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1195

Schlüsselwörter
Support vector machine, Multimodal emotion recognition, PAD scale, Artificial intelligence technology, Teaching strategy

© 2024 Shan Cong, published by Sciendo

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

A Study of Teaching Strategies Optimized with the Integration of Artificial Intelligence Technologies

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 17. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1195

SchlüsselwörterSupport vector machine, Multimodal emotion recognition, PAD scale, Artificial intelligence technology, Teaching strategy

© 2024 Shan Cong, published by Sciendo

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

Schlüsselwörter
Support vector machine, Multimodal emotion recognition, PAD scale, Artificial intelligence technology, Teaching strategy