Smart proctoring with automated anomaly detection

With the emergence of Artificial Intelligence (AI), smart education has become an attractive topic. In a smart education system, automated classrooms and examination rooms could help reduce the economic cost of teaching, and thus improve teaching efficiency. However, existing AI algorithms suffer from low surveillance accuracies and high computational costs, which affect their practicability in real-word scenarios. To address this issue, we propose an AI-driven anomaly detection framework for smart proctoring. The proposed method, namely, Smart Exam (SmartEx), consists of two artificial neural networks: an object recognition network to locate invigilators and examinees, and a behavior analytics network to detect anomalies of examinees during the exam. To validate the performance of our method, we construct a dataset by annotating 6,429 invigilator instances, 34,074 examinee instances and 8 types of behaviors with 267,888 instances. Comprehensive experiments on the dataset show the superiority of our SmartEx method, with a superior proctoring performance and a relatively low computational cost. Besides, we also examine the pre-trained SmartEx in an examination room in our university, which shows high robustness to identify diversified anomalies in real-world scenarios.