SCB-Dataset: A Dataset for Detecting Student and Teacher Classroom Behavior
This provides a dataset for researchers and practitioners in educational AI to analyze classroom performance, but it is incremental as it focuses on data creation rather than novel methods.
The authors tackled the lack of publicly available datasets for detecting student and teacher classroom behaviors by constructing SCB-Dataset, which includes 13,330 images with 122,977 labels for object detection and 21,019 images for image classification, and they benchmarked it using YOLO series algorithms and a large vision-language model.
Using deep learning methods to detect the classroom behaviors of both students and teachers is an effective way to automatically analyze classroom performance and enhance teaching effectiveness. Then, there is still a scarcity of publicly available high-quality datasets on student-teacher behaviors. We constructed SCB-Dataset a comprehensive dataset of student and teacher classroom behaviors covering 19 classes. SCB-Dataset is divided into two types: Object Detection and Image Classification. The Object Detection part includes 13,330 images and 122,977 labels, and the Image Classification part includes 21,019 images. We conducted benchmark tests on SCB-Dataset using YOLO series algorithms and Large vision-language model. We believe that SCB-Dataset can provide a solid foundation for future applications of artificial intelligence in education. Code:https://github.com/Whiffe/SCB-dataset