Real-time Multi-Screen Cheating Detection using K-Means Clustering - Purwanto, Yudhi Setyo; Putra, Rakhmadi Irfansyah (Universitas Bina Darma, 2025)
Ensuring academic honesty during online exams is becoming more and more challenging with students taking advantage of multiple screens and mirrored monitors. This research presents a privacy-sensitive, real-time multi-screen behavior detection model that does not rely on cameras or biometric sensors. The system tracks hardware and behavioral signals like screen-switch rate, focus-loss activity, idle time, and display change events. Utilizing K-Means clustering (k = 3), these metrics are segregated into three categories: Normal, Suspected, and Cheating. Implemented in Python and tested on simulated and real datasets, the model registered a silhouette score of 0.27 and showed discriminative behavior segregation through clustering analysis. Testing against a labelled dataset produced balanced accuracy of more than 80 percent, supported by confusion matrix and performance curve research. Findings show that hardware monitoring and activity-based could be an effective, camera-free means of detecting cheating in online examinations. The approach is privacy-respecting, computationally light in real time, and has understandable output for administrative exam. Drawbacks include the focus to date on Windows platforms and the need for more comprehensive cross-platform testing. Future studies will examine multimodal integration and larger scales to further increase detection accuracy and transferability.