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

<p>Students love basketball, but traditional teaching approaches are no longer able to suit their demands. In this article, artificial intelligence is combined with innovative teaching methods to create a new type of learning. Firstly, under the SPOC teaching mode, intelligent sensors are used to collect basketball sports data, and Kalman filtering is used for processing and segmentation. Then, through the definition of basketball sports gesture, the data division of basketball sports gesture, designing the basketball sports gesture feature extraction method based on unit division, and using the SVM method to analyze the basketball sports gesture features, designing intelligent SPOC hybrid basketball teaching. Finally, the experiments on data collection and motion attitude resolution in basketball are designed to investigate the effects of intelligent SPOC hybrid basketball teaching. The results show that the deviations of the collected acceleration and angular velocity are within 10<sup>−2</sup> orders of magnitude and 1.8°/s deviation, respectively, and the average recognition effect of various basketball postures is 0.925, which is able to effectively carry out basketball motion recognition. The experimental class 1’s physical quality improved by roughly seven after the teaching application was implemented, and the P-value for the improvement of different basketball sports was less than 0.05, indicating that the planned instruction can greatly enhance students’ physical quality and basketball abilities.</p>
</abstract>

Students love basketball, but traditional teaching approaches are no longer able to suit their demands. In this article, artificial intelligence is combined with innovative teaching methods to create a new type of learning. Firstly, under the SPOC teaching mode, intelligent sensors are used to collect basketball sports data, and Kalman filtering is used for processing and segmentation. Then, through the definition of basketball sports gesture, the data division of basketball sports gesture, designing the basketball sports gesture feature extraction method based on unit division, and using the SVM method to analyze the basketball sports gesture features, designing intelligent SPOC hybrid basketball teaching. Finally, the experiments on data collection and motion attitude resolution in basketball are designed to investigate the effects of intelligent SPOC hybrid basketball teaching. The results show that the deviations of the collected acceleration and angular velocity are within 10−2 orders of magnitude and 1.8°/s deviation, respectively, and the average recognition effect of various basketball postures is 0.925, which is able to effectively carry out basketball motion recognition. The experimental class 1’s physical quality improved by roughly seven after the teaching application was implemented, and the P-value for the improvement of different basketball sports was less than 0.05, indicating that the planned instruction can greatly enhance students’ physical quality and basketball abilities.

Design and Application of SPOC Hybrid Teaching for College Basketball Teaching Based on Artificial Intelligence Technology

Applied Mathematics and Nonlinear Sciences

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

Students love basketball, but traditional teaching approaches are no longer able to suit their demands. In this article, artificial intelligence is combined...

Design and Application of SPOC Hybrid Teaching for College Basketball Teaching Based on Artificial Intelligence Technology

Publié en ligne: 20 déc. 2023

Pages: -

Reçu: 20 janv. 2023

Accepté: 30 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01578

Mots clés
Smart sensor, Kalman filter, Feature extraction, SVM, SPOC hybrid teaching

© 2023 Duoduo Yan, published by Sciendo

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

Design and Application of SPOC Hybrid Teaching for College Basketball Teaching Based on Artificial Intelligence Technology

Publié en ligne: 20 déc. 2023

Pages: -

Reçu: 20 janv. 2023

Accepté: 30 juin 2023

DOI: https://doi.org/10.2478/amns.2023.2.01578

Mots clésSmart sensor, Kalman filter, Feature extraction, SVM, SPOC hybrid teaching

© 2023 Duoduo Yan, published by Sciendo

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

Mots clés
Smart sensor, Kalman filter, Feature extraction, SVM, SPOC hybrid teaching