Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data
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 ra...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00444 |
| _version_ | 1797340776014807040 |
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| author | Lv Foguang Wang Wei Ren Jianhua |
| author_facet | Lv Foguang Wang Wei Ren Jianhua |
| author_sort | Lv Foguang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:08:11Z |
| format | Article |
| id | doaj.art-dcb9e63fc92e41729e9ae6153e6eed86 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:08:11Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-dcb9e63fc92e41729e9ae6153e6eed862024-01-29T08:52:32ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00444Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big dataLv Foguang0Wang Wei1Ren Jianhua21Hebei University of Engineering, Handan, Hebei, 056000, China.2Hebei Finance University, Baoding, Hebei, 071000, China.1Hebei University of Engineering, Handan, Hebei, 056000, China.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.https://doi.org/10.2478/amns.2023.2.00444information fusionmultimodal statehierarchical analysisrandom forestopenpose97d60 |
| spellingShingle | Lv Foguang Wang Wei Ren Jianhua 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 information fusion multimodal state hierarchical analysis random forest openpose 97d60 |
| title | Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data |
| title_full | Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data |
| title_fullStr | Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data |
| title_full_unstemmed | Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data |
| title_short | Analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data |
| title_sort | analysis on the innovative education mode of the integration of information technology and traditional teaching in the era of big data |
| topic | information fusion multimodal state hierarchical analysis random forest openpose 97d60 |
| url | https://doi.org/10.2478/amns.2023.2.00444 |
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