Behavioral Analysis Using Machine Learning Algorithms in Online Proctored Assessments; The Potential of Mobile Proctoring in E Assessments: A Review

Abstract

The rapid expansion of online learning has intensified the need for secure and reliable examination systems. While artificial intelligence (AI) and machine learning (ML) are increasingly employed to safeguard integrity through behavioral analysis, current proctoring solutions remain largely desktop-centric and poorly adapted to mobile environments, where smartphones dominate access. These limitations—spanning heterogeneous hardware, unstable lighting and connectivity, algorithmic bias, privacy concerns, and limited explainability—undermine fairness and trust in misconduct detection. This study reviews the potential of mobile-based proctoring systems that leverage ML for real-time behavioral analysis in e-assessments. A systematic search across Scopus, IEEE Xplore, SpringerLink, and Google Scholar identified 200 publications from 2015–2025. After applying inclusion and exclusion criteria, 30 peer-reviewed empirical studies were analyzed in depth. Findings reveal that ML-driven behavioral analytics enhance accuracy and fairness by detecting anomalies such as gaze aversion, facial emotion changes, and environmental inconsistencies. However, research gaps persist in mobile optimization, ethical safeguards, privacy protection, and bias mitigation. The review concludes that hybrid mobile frameworks integrating multimodal behavioral features and adaptive ML models are essential to strengthen proctoring accuracy while ensuring accessibility and privacy. Future research should prioritize context aware and explainable AI to foster equitable and trustworthy online assessment environments.