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

<p>In order to develop students’ concentration in the classroom, this paper proposes to analyze students’ learning concentration from three dimensions, where the superiority of the random forest classification algorithm is found in the dimension of head posture estimation so that students’ attention ranges can be analyzed. For data acquisition, the OpenPose platform is used for real-time detection of body, foot, hand, and face key points. A comprehensive evaluation study of students’ concentration levels in the classroom was achieved through an effective algorithm. Finally, the multimodal information fusion algorithm was investigated, and it was concluded that the weights could be calculated by hierarchical analysis to achieve a comprehensive evaluation of students’ learning concentration. By analyzing the course data of 12 students, the distribution of SFR values, Yaw angle and Pitch angle distribution, PERCLOS value distribution, and correct/error rate distribution of answers were counted, and the final scores of respective learning engagement were obtained as 0.91, 0.62, 0.80, 0.36, 0.82, 0.73, 0.81, 0.63, 0.81, 0.81 The model scores were compared with the expert scores, and the accuracy rate reached 98.6%, which proved that the model proposed in this paper is effective and can correctly reflect the real state of students’ learning in the classroom.</p>
</abstract>

In order to develop students’ concentration in the classroom, this paper proposes to analyze students’ learning concentration from three dimensions, where the superiority of the random forest classification algorithm is found in the dimension of head posture estimation so that students’ attention ranges can be analyzed. For data acquisition, the OpenPose platform is used for real-time detection of body, foot, hand, and face key points. A comprehensive evaluation study of students’ concentration levels in the classroom was achieved through an effective algorithm. Finally, the multimodal information fusion algorithm was investigated, and it was concluded that the weights could be calculated by hierarchical analysis to achieve a comprehensive evaluation of students’ learning concentration. By analyzing the course data of 12 students, the distribution of SFR values, Yaw angle and Pitch angle distribution, PERCLOS value distribution, and correct/error rate distribution of answers were counted, and the final scores of respective learning engagement were obtained as 0.91, 0.62, 0.80, 0.36, 0.82, 0.73, 0.81, 0.63, 0.81, 0.81 The model scores were compared with the expert scores, and the accuracy rate reached 98.6%, which proved that the model proposed in this paper is effective and can correctly reflect the real state of students’ learning in the classroom.

Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data

Applied Mathematics and Nonlinear Sciences

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

In order to develop students’ concentration in the classroom, this paper proposes to analyze students’ learning concentration from three dimensions, where the...

Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data

Published Online: Oct 02, 2023

Page range: -

Received: Dec 12, 2022

Accepted: Apr 12, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00444

Keywords
Information fusion, Multimodal state, Hierarchical analysis, Random forest, OpenPose

© 2023 Foguang Lv et al., published by Sciendo

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

Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data

Published Online: Oct 02, 2023

Page range: -

Received: Dec 12, 2022

Accepted: Apr 12, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00444

KeywordsInformation fusion, Multimodal state, Hierarchical analysis, Random forest, OpenPose

© 2023 Foguang Lv et al., published by Sciendo

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

Keywords
Information fusion, Multimodal state, Hierarchical analysis, Random forest, OpenPose