Enhance Massive Open Online Courses Integrity: AI for Exam Proctoring
Main Article Content
Abstract
Massive Open Online Courses (MOOCs) are growing quickly, but it's challenging to ensure academic integrity during remote exams with many participants. Existing approaches to supervising students online have scalability, accuracy, and integration limitations. This paper proposes a scalable, accurate AI exam proctoring module compatible with MOOCs to address this issue. Our approach includes an AI server that handles video processing and coordinates cheating detection services. Another server uses Triton to analyze student video feeds quickly. It runs optimized deep learning models, such as face recognition. There is also an integrated MOOC client to capture, compress, and transmit video. The main innovations are the asynchronous AI server for handling multiple tasks simultaneously, efficient deep learning pipelines that use fewer computing resources, and the integration of inference pipelines into Triton for faster processing. The integration of the AI module into the MOOC has been successful. The system can monitor multiple test-takers at the same time and accurately detect any potential cheating. Evaluations showed the high accuracy on different AI models
Keywords
AI, face recognition, phone detection, face pose estimation, online proctoring, MOOCs platform
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
[1] Unesco, One year into covid-19 education disruption: Where do we stand?, 2021. [Online]. Available: https://www.unesco.org/en/articles/one-year-covid-19- education-disruption-where-do-we-stand Accessed on: Apr. 20, 2023.
[2] D. Shah, By the numbers: Moocs in 2020. class central, 2020. [Online]. Available: https://ww.classcentral.com/report/mooc-stats-2020/ Accessed on: Apr. 20, 2023.
[3] R. Rosmarin and J. P. 2021., The 20 most popular coursera online courses that students actually finish. [Online]. Available: https://www.businessinsider.com/guides/learning/cour sera-popular-courses-withhigh-completion-rates Accessed on: Apr. 20, 2023.
[4] Daotao.ai. [Online]. Available: https://daotao.ai/ Accessed on: Apr. 20, 2023. [5] Coursera. 2023. Coursera announces new AI content and innovations to help HR and learning leaders drive organizational agility amid relentless disruption. [Online]. Available: https://blog.coursera.org/trusted-content-and-aiinnovations-to-drive-organizational-agility-forlearning-leaders/ Accessed on: Apr. 20, 2023.
[6] A. Dubbaka and A. Gopalan, Detecting learner engagement in MOOCs using automatic facial expression recognition, 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, Apr. 27-30, 2020, pp. 447-456. https://doi.org/10.1109/EDUCON45650.2020.9125149
[7] Li, F., Zhang, X., Artificial intelligence facial recognition and voice anomaly detection in the application of English MOOC teaching system. Soft Comput, vol. 27, Apr. 2023, pp. 6855–6867. https://doi.org/10.1007/s00500-023-08119-7 [8] Sharma, K., Giannakos, M. & Dillenbourg, P., Eye-tracking and artificial intelligence to enhance motivation and learning. Smart Learn. Environ. 7, 13, 2020. https://doi.org/10.1186/s40561-020-00122-x
[9] Deng, Jiankang, et al. Retinaface: Single-shot multilevel face localisation in the wild. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, Jun. 13-19, 2020. https://doi.org/10.1109/CVPR42600.2020.00525
[10] Howard, Andrew G., et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, Apr. 17, 2017.
[11] Deng, Jiankang, et al. Arcface: Additive angular margin loss for deep face recognition, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jun. 15-20, 2019. https://doi.org/10.1109/CVPR.2019.00482
[12] Duta, Ionut Cosmin, et al. Improved residual networks for image and video recognition. 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, Milan, Italy, Jan. 10-15, 2021. https://doi.org/10.1109/ICPR48806.2021.9412193
[13] Ruiz, Nataniel, Eunji Chong, James M. Rehg. Fine-grained face pose estimation without keypoints. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, Jun. 18-22, 2018. https://doi.org/10.1109/CVPRW.2018.00281
[14] Wang, Chien-Yao, Alexey Bochkovskiy, Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, Jun. 17-24, 2023. https://doi.org/10.1109/CVPR52729.2023.00721
[15] NVIDIA Corporation, Triton Reference Server, 2021. [Online]. Available: https://developer.nvidia.com/triton-inference-server Accessed on: Apr. 20, 2023. [16] Apache JMeter. [Online]. Available: https://jmeter.apache.org Accessed on: Apr. 20, 2023
[2] D. Shah, By the numbers: Moocs in 2020. class central, 2020. [Online]. Available: https://ww.classcentral.com/report/mooc-stats-2020/ Accessed on: Apr. 20, 2023.
[3] R. Rosmarin and J. P. 2021., The 20 most popular coursera online courses that students actually finish. [Online]. Available: https://www.businessinsider.com/guides/learning/cour sera-popular-courses-withhigh-completion-rates Accessed on: Apr. 20, 2023.
[4] Daotao.ai. [Online]. Available: https://daotao.ai/ Accessed on: Apr. 20, 2023. [5] Coursera. 2023. Coursera announces new AI content and innovations to help HR and learning leaders drive organizational agility amid relentless disruption. [Online]. Available: https://blog.coursera.org/trusted-content-and-aiinnovations-to-drive-organizational-agility-forlearning-leaders/ Accessed on: Apr. 20, 2023.
[6] A. Dubbaka and A. Gopalan, Detecting learner engagement in MOOCs using automatic facial expression recognition, 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, Apr. 27-30, 2020, pp. 447-456. https://doi.org/10.1109/EDUCON45650.2020.9125149
[7] Li, F., Zhang, X., Artificial intelligence facial recognition and voice anomaly detection in the application of English MOOC teaching system. Soft Comput, vol. 27, Apr. 2023, pp. 6855–6867. https://doi.org/10.1007/s00500-023-08119-7 [8] Sharma, K., Giannakos, M. & Dillenbourg, P., Eye-tracking and artificial intelligence to enhance motivation and learning. Smart Learn. Environ. 7, 13, 2020. https://doi.org/10.1186/s40561-020-00122-x
[9] Deng, Jiankang, et al. Retinaface: Single-shot multilevel face localisation in the wild. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, Jun. 13-19, 2020. https://doi.org/10.1109/CVPR42600.2020.00525
[10] Howard, Andrew G., et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, Apr. 17, 2017.
[11] Deng, Jiankang, et al. Arcface: Additive angular margin loss for deep face recognition, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jun. 15-20, 2019. https://doi.org/10.1109/CVPR.2019.00482
[12] Duta, Ionut Cosmin, et al. Improved residual networks for image and video recognition. 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, Milan, Italy, Jan. 10-15, 2021. https://doi.org/10.1109/ICPR48806.2021.9412193
[13] Ruiz, Nataniel, Eunji Chong, James M. Rehg. Fine-grained face pose estimation without keypoints. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, Jun. 18-22, 2018. https://doi.org/10.1109/CVPRW.2018.00281
[14] Wang, Chien-Yao, Alexey Bochkovskiy, Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, Jun. 17-24, 2023. https://doi.org/10.1109/CVPR52729.2023.00721
[15] NVIDIA Corporation, Triton Reference Server, 2021. [Online]. Available: https://developer.nvidia.com/triton-inference-server Accessed on: Apr. 20, 2023. [16] Apache JMeter. [Online]. Available: https://jmeter.apache.org Accessed on: Apr. 20, 2023