TY - GEN
T1 - Automated Analysis of Teaching Models Based on Artificial Intelligence Detection Algorithms
AU - Shuai, Nie
AU - Chongwen, Wang
AU - Zening, Jin
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper proposes an automated analysis of teaching models based on artificial intelligence detection algorithms. It aims to efficiently analyze actual classroom teaching models, providing insights that enable appropriate adjustments to teaching strategies, thereby improving the quality of education. We analyze classroom audio data streams using an improved Emphasized Channel Attention, Propagation and Aggregation-Time Delay Neural Network (ECAPA-TDNN) model and analyze video data streams using an enhanced You Only Look Once-v3 (YOLO-v3) model. Subsequently, the obtained information, such as head-raising rates and speaker identification, is automatically processed using an improved S-T analysis method to derive teaching models. Experimental results show that our analytical method, while ensuring that the results closely reflect reality, achieves an analysis speed 2.81 times faster than traditional methods. This demonstrates the advantages of applying automated teaching model analysis in the educational field.
AB - This paper proposes an automated analysis of teaching models based on artificial intelligence detection algorithms. It aims to efficiently analyze actual classroom teaching models, providing insights that enable appropriate adjustments to teaching strategies, thereby improving the quality of education. We analyze classroom audio data streams using an improved Emphasized Channel Attention, Propagation and Aggregation-Time Delay Neural Network (ECAPA-TDNN) model and analyze video data streams using an enhanced You Only Look Once-v3 (YOLO-v3) model. Subsequently, the obtained information, such as head-raising rates and speaker identification, is automatically processed using an improved S-T analysis method to derive teaching models. Experimental results show that our analytical method, while ensuring that the results closely reflect reality, achieves an analysis speed 2.81 times faster than traditional methods. This demonstrates the advantages of applying automated teaching model analysis in the educational field.
KW - Artificial Intelligence
KW - Automated Teaching Model Analysis
KW - Deep Learning
KW - S-T Analysis Method
KW - Teaching Model Analysis
UR - https://www.scopus.com/pages/publications/105004985410
U2 - 10.1109/ICEIT64364.2025.10976154
DO - 10.1109/ICEIT64364.2025.10976154
M3 - Conference contribution
AN - SCOPUS:105004985410
T3 - 2025 14th International Conference on Educational and Information Technology, ICEIT 2025
SP - 135
EP - 140
BT - 2025 14th International Conference on Educational and Information Technology, ICEIT 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Educational and Information Technology, ICEIT 2025
Y2 - 14 March 2025 through 16 March 2025
ER -