Student Behavior

Research on student behavior focuses on automatically detecting and analyzing student actions in classroom settings, both in-person and online, to improve teaching effectiveness and address issues like academic dishonesty. Current efforts utilize computer vision techniques, particularly deep learning models like YOLOv7 and variations thereof, along with transformer architectures, to analyze video and image data, classifying behaviors such as hand-raising, reading, and even subtle indicators of disengagement. The development of large, publicly available datasets of student behavior is crucial for training and benchmarking these models, enabling advancements in automated classroom observation and personalized learning interventions.