π€ AI Summary
To address the critical bottleneck of scarce large-scale, high-quality, real-world classroom behavioral annotation datasets in educational AI research, this paper introduces SCB-datasetβthe first fine-grained student behavior detection dataset specifically designed for authentic classroom settings. It comprises 4,003 images with 11,248 precise bounding-box annotations, emphasizing pedagogically significant interactive behaviors such as hand-raising. The dataset is open-source, rigorously annotated, and grounded in real classroom environments, thereby filling a longstanding gap in publicly available benchmarks for classroom behavior analysis. We conduct benchmark experiments using YOLOv7, achieving an mAP of 85.3% on SCB-dataset, empirically validating its quality and utility. SCB-dataset serves as a foundational resource for intelligent classroom behavior understanding and training of education-oriented large language models.
π Abstract
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