Frontiers in Educational Research, 2025, 8(3); doi: 10.25236/FER.2025.080315.
Zhenyue Sun1, Maoyue Zhang2, Yun Cheng3, Ming Yu4
1Huanggang Normal University, Huanggang, China
2Dezhou No.2 Middle School, Dezhou, China
3Huanggang Normal University, Huanggang, China
4Huanggang Normal University, Huanggang, China
The analysis of teaching behaviors is of great significance for teaching diagnosis and quality improvement. It can also serve as a basis for assisting teachers in reflection and evidence-based teaching. However, most of the previous automated research methods have used deep learning models to train on specific samples. The coding categories that can be automatically analyzed are single, which cannot be applied to different coding systems required by complex teaching scenarios. Nor can the coding be modified according to the needs of the scenarios or multi-coding analysis be carried out. Based on the review of the methods for analyzing classroom teaching behaviors based on artificial intelligence, this study proposes an automated collection process of teaching behaviors, which is "determining the analysis objectives—selecting the coding system—splicing prompts", and trains a large model in the vertical domain to judge the feasibility of AIGC (Artificial Intelligence Generated Content) in recognizing teaching behaviors.
Classroom Analysis; AIGC; Collection of Teaching Behaviors; Classroom Teaching
Zhenyue Sun, Maoyue Zhang, Yun Cheng, Ming Yu. AIGC-Supported Intelligent Collection and Application Research of Classroom Teaching Behaviors. Frontiers in Educational Research(2025), Vol. 8, Issue 3: 94-102. https://doi.org/10.25236/FER.2025.080315.
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