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

<p>Ethnic traditional sports are forms of physical activity unique to one or more ethnic groups. Effective evaluation of the teaching effectiveness of ethnic traditional sports helps to promote the development of ethnic sports and the transmission of ethnic traditional sports. Currently, the evaluation of ethnic traditional sports is mainly a scoring system, which evaluates students’ performance in ethnic traditional sports through teachers’ scoring, and this evaluation method is difficult to assess the teaching effectiveness of ethnic traditional sports accurately. In this paper, based on the video text generation technology, the 3D residual attention network model is constructed by introducing the attention mechanism into the 3D residual module in the video feature extraction through the encoding-decoding video text description framework and improving the traditional deep residual network for evaluating the teaching effect of ethnic sports. After experimental validation, it is shown that the 3D residual attention network model can more accurately describe the evaluation of ethnic traditional sports teaching effectiveness using natural language.</p>
</abstract>

Ethnic traditional sports are forms of physical activity unique to one or more ethnic groups. Effective evaluation of the teaching effectiveness of ethnic traditional sports helps to promote the development of ethnic sports and the transmission of ethnic traditional sports. Currently, the evaluation of ethnic traditional sports is mainly a scoring system, which evaluates students’ performance in ethnic traditional sports through teachers’ scoring, and this evaluation method is difficult to assess the teaching effectiveness of ethnic traditional sports accurately. In this paper, based on the video text generation technology, the 3D residual attention network model is constructed by introducing the attention mechanism into the 3D residual module in the video feature extraction through the encoding-decoding video text description framework and improving the traditional deep residual network for evaluating the teaching effect of ethnic sports. After experimental validation, it is shown that the 3D residual attention network model can more accurately describe the evaluation of ethnic traditional sports teaching effectiveness using natural language.

The application of video text generation technology in assessing the effectiveness of teaching ethnic traditional sports

College of Sports, Hunan University of Science and Engineering

Applied Mathematics and Nonlinear Sciences

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

Ethnic traditional sports are forms of physical activity unique to one or more ethnic groups. Effective evaluation of the teaching effectiveness of ethnic...

The application of video text generation technology in assessing the effectiveness of teaching ethnic traditional sports

Publicado en línea: 10 mar 2023

Páginas: 3085 - 3104

Recibido: 01 abr 2022

Aceptado: 04 jul 2023

DOI: https://doi.org/10.2478/amns.2023.2.00023

Palabras clave
Ethnic traditional sports, Teaching effectiveness evaluation, Video text generation technique, 3D residual attention network

© 2023 Zhi Tang et al., published by Sciendo

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

The application of video text generation technology in assessing the effectiveness of teaching ethnic traditional sports

Publicado en línea: 10 mar 2023

Páginas: 3085 - 3104

Recibido: 01 abr 2022

Aceptado: 04 jul 2023

DOI: https://doi.org/10.2478/amns.2023.2.00023

Palabras claveEthnic traditional sports, Teaching effectiveness evaluation, Video text generation technique, 3D residual attention network

© 2023 Zhi Tang et al., published by Sciendo

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

Palabras clave
Ethnic traditional sports, Teaching effectiveness evaluation, Video text generation technique, 3D residual attention network