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: this https URL

利用深度学习方法自动检测学生的课堂行为是分析他们的课堂表现和提高教学效果的一种有前途的方法。为了解决公开可用的学生行为时空数据集缺乏以及手动标注这类数据集的高成本问题，我们提出了一种通过图像数据集来扩展学生课堂行为数据集的方法（SCB-ST-Dataset4）。该数据集包括了 754,094 张图像和 25,670 个标签，侧重于三种行为：举手，阅读和书写。我们提出的方法可以快速生成时空行为数据集，无需进行注释。此外，我们还提出了一种行为相似度指数（BSI）来探索行为的相似性。我们使用 YOLOv5、YOLOv7、YOLOv8 和 SlowFast 算法评估了数据集，达到了 82.3% 的平均精度。实验证明了我们方法的有效性。这个数据集为学生行为检测的未来研究提供了牢固的基础，有可能推动该领域的进展。SCB-ST-Dataset4 可在此链接下载：https://URL