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The use of deep learning methods for automatic detection of students'classroom behavior is a promising approach to analyze their class performanceand enhance teaching effectiveness. However, the lack of publicly availabledatasets 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 11,248labels and 4,003 images, with a focus on hand-raising behavior. We evaluatedthe 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 robustfoundation for future research in the field of student behavior detection andpromote further advancements in this area.Our SCB-dataset can be downloadedfrom: https://github.com/Whiffe/SCB-dataset

SCB-dataset: A Dataset for Detecting Student Classroom Behavior