Using deep learning methods to detect students' classroom behavior automatically is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available spatio-temporal datasets on student behavior, as well as the high cost of manually labeling such datasets, pose significant challenges for researchers in this field. To address this issue, we proposed a method for extending the spatio-temporal behavior dataset in Student Classroom Scenarios (SCB-ST-Dataset4) through image dataset. Our SCB-ST-Dataset4 comprises 754094 images with 25670 labels, focusing on 3 behaviors: hand-raising, reading, writing. Our proposed method can rapidly generate spatio-temporal behavioral datasets without requiring annotation. Furthermore, we proposed a Behavior Similarity Index (BSI) to explore the similarity of behaviors. We evaluated the dataset using the YOLOv5, YOLOv7, YOLOv8, and SlowFast algorithms, achieving a mean average precision (map) of up to 82.3%. The experiment further demonstrates the effectiveness of our method. This dataset provides a robust foundation for future research in student behavior detection, potentially contributing to advancements in this field. The SCB-ST-Dataset4 is available for download at: https://github.com/Whiffe/SCB-dataset.

翻译：利用深度学习方法自动检测学生的课堂行为，是分析其课堂表现并提升教学效果的一种有前景的方式。然而，学生行为公开时空数据集的缺乏，以及人工标注此类数据集的高昂成本，给该领域的研究人员带来了巨大挑战。为应对这一问题，我们提出了一种通过图像数据集扩展学生课堂场景中时空行为数据集的方法（SCB-ST-Dataset4）。我们的SCB-ST-Dataset4包含754094张图像和25670个标签，聚焦于三种行为：举手、阅读、书写。我们提出的方法无需标注即可快速生成时空行为数据集。此外，我们提出了一种行为相似度指数（BSI）来探究行为的相似性。我们使用YOLOv5、YOLOv7、YOLOv8和SlowFast算法对该数据集进行了评估，平均精度均值（mAP）最高达到82.3%。该实验进一步证明了我们方法的有效性。该数据集为学生行为检测的未来研究提供了坚实基础，有望推动该领域的进展。SCB-ST-Dataset4可在以下链接下载：https://github.com/Whiffe/SCB-dataset。