SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior
This provides a benchmark for researchers in educational technology to analyze student performance and improve teaching effectiveness, though it is incremental as it introduces a new dataset rather than a novel method.
The authors tackled the lack of publicly available datasets for detecting student classroom behavior by proposing SCB-Dataset3, a dataset with 5686 images and 45578 labels for six behaviors, achieving up to 80.3% mean average precision using YOLO algorithms.
The use of deep learning methods to automatically detect students' classroom behavior is a promising approach for analyzing their class performance and improving 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 the Student Classroom Behavior dataset (SCB-dataset3), which represents real-life scenarios. Our dataset comprises 5686 images with 45578 labels, focusing on six behaviors: hand-raising, reading, writing, using a phone, bowing the head, and leaning over the table. We evaluated the dataset using the YOLOv5, YOLOv7, and YOLOv8 algorithms, achieving a mean average precision (map) of up to 80.3$\%$. We believe that our dataset can serve as a robust foundation for future research in student behavior detection and contribute to advancements in this field. Our SCB-dataset3 is available for download at: https://github.com/Whiffe/SCB-dataset