AI-assisted Gaze Detection for Proctoring Online Exams
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909325746765824 |
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| author | Shih, Yong-Siang Zhao, Zach Niu, Chenhao Iberg, Bruce Sharpnack, James Baig, Mirza Basim |
| author_facet | Shih, Yong-Siang Zhao, Zach Niu, Chenhao Iberg, Bruce Sharpnack, James Baig, Mirza Basim |
| contents | For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16923 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-assisted Gaze Detection for Proctoring Online Exams Shih, Yong-Siang Zhao, Zach Niu, Chenhao Iberg, Bruce Sharpnack, James Baig, Mirza Basim Artificial Intelligence Human-Computer Interaction For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system. |
| title | AI-assisted Gaze Detection for Proctoring Online Exams |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.16923 |