Automated Online Exam Proctoring

• Published in: IEEE transactions on multimedia Vol. 19; no. 7; pp. 1609 - 1624
• Main Authors: Atoum, Yousef, Liping Chen, Liu, Alex X., Hsu, Stephen D. H., Liu, Xiaoming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Acoustic measurement; Analytics; Audio data; Automation; CAI; Classification; Computer assisted instruction; Computing time; Cost analysis; Covariance feature; Cues; Distance learning; Education; Environmental monitoring; Feature extraction; gaze estimation; Hardware; Monitoring; Multimedia; Multimedia communication; On-line systems; online exam proctoring (OEP); phone detection; Robustness; Sensors; Sliding; speech detection; text detection; user verification; Visualization; Voice recognition; Webcams
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2017.2656064

Massive open online courses and other forms of remote education continue to increase in popularity and reach. The ability to efficiently proctor remote online examinations is an important limiting factor to the scalability of this next stage in education. Presently, human proctoring is the most common approach of evaluation, by either requiring the test taker to visit an examination center, or by monitoring them visually and acoustically during exams via a webcam. However, such methods are labor intensive and costly. In this paper, we present a multimedia analytics system that performs automatic online exam proctoring. The system hardware includes one webcam, one wearcam, and a microphone for the purpose of monitoring the visual and acoustic environment of the testing location. The system includes six basic components that continuously estimate the key behavior cues: user verification, text detection, voice detection, active window detection, gaze estimation, and phone detection. By combining the continuous estimation components, and applying a temporal sliding window, we design higher level features to classify whether the test taker is cheating at any moment during the exam. To evaluate our proposed system, we collect multimedia (audio and visual) data from \text{24} subjects performing various types of cheating while taking online exams. Extensive experimental results demonstrate the accuracy, robustness, and efficiency of our online exam proctoring system.