<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Teaching behavior recognition has a wide range of applications in the smart classroom and is one of the important means to achieve educational intelligence. To improve the performance of indoor teaching behavior recognition using CSI in complex scenes, this paper proposes an indoor teaching behavior recognition algorithm based on multi-feature fusion MLSTM by eliminating background noise to circumvent the influence of the experimental environment on CSI. To address the problem, the model cannot generalize in recognizing new users, and the labeled samples of new users are difficult to obtain in large quantities in a short period of time. In this paper, a new user recognition algorithm based on the SSGAN model is constructed, and then the input and output of MLSTM are modified as the discriminator of SSGAN to improve the recognition performance of the model for new users by semi-supervised learning. The recognition accuracy of the M-LSTM model on the sports, daily, and dance datasets is 0.985, 0.966, and 0.944, respectively, and the recognition accuracy of the SSGAN model on the three datasets is also around 90%, as verified by different experiments. The p-value is less than 0.05, and the student’s interest in physical education in the experimental group is 2.6 times higher than that in the control group. Therefore, the model proposed in this paper has good practicality.</p>
</abstract>

Teaching behavior recognition has a wide range of applications in the smart classroom and is one of the important means to achieve educational intelligence. To improve the performance of indoor teaching behavior recognition using CSI in complex scenes, this paper proposes an indoor teaching behavior recognition algorithm based on multi-feature fusion MLSTM by eliminating background noise to circumvent the influence of the experimental environment on CSI. To address the problem, the model cannot generalize in recognizing new users, and the labeled samples of new users are difficult to obtain in large quantities in a short period of time. In this paper, a new user recognition algorithm based on the SSGAN model is constructed, and then the input and output of MLSTM are modified as the discriminator of SSGAN to improve the recognition performance of the model for new users by semi-supervised learning. The recognition accuracy of the M-LSTM model on the sports, daily, and dance datasets is 0.985, 0.966, and 0.944, respectively, and the recognition accuracy of the SSGAN model on the three datasets is also around 90%, as verified by different experiments. The p-value is less than 0.05, and the student’s interest in physical education in the experimental group is 2.6 times higher than that in the control group. Therefore, the model proposed in this paper has good practicality.

Research and reflection on college physical education classroom teaching based on SSGAN model

School of Physical Education and Health Care

Applied Mathematics and Nonlinear Sciences

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

Teaching behavior recognition has a wide range of applications in the smart classroom and is one of the important means to achieve educational intelligence. To...

Research and reflection on college physical education classroom teaching based on SSGAN model

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 02. Dez. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00405

Schlüsselwörter
Physical education classroom teaching, CSI, M-LSTM model, SSGAN model, Feature fusion.

© 2023 Yubing Liao, published by Sciendo

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

Research and reflection on college physical education classroom teaching based on SSGAN model

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 02. Dez. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00405

SchlüsselwörterPhysical education classroom teaching, CSI, M-LSTM model, SSGAN model, Feature fusion.

© 2023 Yubing Liao, published by Sciendo

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

Schlüsselwörter
Physical education classroom teaching, CSI, M-LSTM model, SSGAN model, Feature fusion.