Students' Abnormal Behavior in Online Exam Dataset

The Student Behavioral Dataset is a collection of labeled images designed to analyze and detect abnormal student behaviors during online assessments. The dataset categorizes five distinct behaviors: using a mobile device, hand movements, eye movements, mouth opening, and looking sideways.

This dataset was created for research purposes in collaboration with a group of students at the University of Information Technology and Communication and the Iraqi Commission for Computer and Informatics (Informatics Institute for Postgraduate Studies). It is formatted in the YOLO DarkNet53 COCO dataset labeling style, facilitating its use in machine learning applications. This version is original and augmented dataset.