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

翻译：利用深度学习方法自动检测学生课堂行为是分析其课堂表现并提升教学效果的一种有前景的方法。然而，缺乏公开可用的学生行为数据集对该领域的研究人员构成了挑战。为解决这一问题，我们提出了一个反映真实场景的学生课堂行为数据集（SCB-dataset）。该数据集包含11,248个标签和4,003张图像，重点关注举手行为。我们使用YOLOv7算法对该数据集进行了评估，平均精度均值（mAP）达到85.3%。我们相信，该数据集能够为未来学生行为检测领域的研究提供坚实基础，并推动该领域的进一步进展。我们的SCB-dataset可从以下地址下载：https://github.com/Whiffe/SCB-dataset