  The use of deep learning methods for automatic detection of students'
classroom behavior is a promising approach to analyze their class performance
and enhance 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 a Student Classroom Behavior dataset
(SCB-dataset) that reflects real-life scenarios. Our dataset includes 11,248
labels and 4,003 images, with a focus on hand-raising behavior. We evaluated
the dataset using the YOLOv7 algorithm, achieving a mean average precision
(map) of up to 85.3%. We believe that our dataset can serve as a robust
foundation for future research in the field of student behavior detection and
promote further advancements in this area.Our SCB-dataset can be downloaded
from: https://github.com/Whiffe/SCB-dataset
