  The use of deep learning methods to automatically detect students' classroom
behavior is a promising approach for analyzing their class performance and
improving teaching effectiveness. However, the lack of publicly available
datasets on student behavior poses a challenge for researchers in this field.
To address this issue, we propose the Student Classroom Behavior dataset
(SCB-dataset3), which represents real-life scenarios. Our dataset comprises
5686 images with 45578 labels, focusing on six behaviors: hand-raising,
reading, writing, using a phone, bowing the head, and leaning over the table.
We evaluated the dataset using the YOLOv5, YOLOv7, and YOLOv8 algorithms,
achieving a mean average precision (map) of up to 80.3$\%$. We believe that our
dataset can serve as a robust foundation for future research in student
behavior detection and contribute to advancements in this field. Our
SCB-dataset3 is available for download at:
https://github.com/Whiffe/SCB-dataset
