The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Automated Online Exam Proctoring
Abstract
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.
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Cites
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Cited by
Face anti-spoofing using patch and depth-based CNNs
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Self-citationA Systematic Review on AI-based Proctoring Systems: Past, Present and Future
Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10597-x · Cited by 267
A systematic review of research on cheating in online exams from 2010 to 2021
Noorbehbahani, F., Mohammadi, A., & Aminazadeh, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10927-7 · Cited by 236
A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption
Muzaffar, A. W., Tahir, M., Anwar, M. W., Chaudry, Q., Mir, S. R., & Rasheed, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3060192 · Cited by 149
Deep Video Anomaly Detection: Opportunities and Challenges
Ren, J., Xia, F., Liu, Y., & Lee, I. (2021) · 2021 International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw53433.2021.00125 · Cited by 84
Metadata
- Authors
- Atoum, Y., Chen, L., Liu, A. X., Hsu, S. D. H., & Liu, X.
- Venue
- IEEE Transactions on Multimedia
- Published
- 2017-07
- Volume
- 19
- Issue
- 7
- Pages
- 1609-1624
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2573323278
- Cited by
- 287
- References
- 31
- FWCI
- 17.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Atoum, Y., Chen, L., Liu, A. X., Hsu, S. D. H., & Liu, X. (2017). Automated Online Exam Proctoring. IEEE Transactions on Multimedia. https://doi.org/10.1109/tmm.2017.2656064
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