Abstract
Online teaching accommodates learners from distant locations and also saves time to commute, reduces traffic, etc. Exams are an important component of online educational programs, and students’ cheating during the exam is a widespread phenomenon around the world. Also, applicants tend to cheat more under no monitoring in the online exam. So, exam proctoring and monitoring methodology to detect cheating and reduce its possibility is the need of an hour. In the past few years, various research methods have been introduced to ensure smooth and efficient online exam monitoring. But, such methodologies are costly and require much human intervention. Also, proctoring in online exams requires precision and accuracy. The proposed methodology involves continuous user validation and verification to ensure his/her integrity. Proposed approach of monitoring during the test includes subtle micro-expression detection such as laughter detection, eye gaze tracking to predict applicant’s viewing direction, eyes blinking/close duration, and head activity/head movement detection. Any suspicious activity or moment by the applicant will be tracked, and associated penalty is imposed. The work uses artificial intelligence for classifying the applicant’s activity. Having remotely proctored exams can significantly reduce logistic efforts, reduce evaluation time, and make it easier to reach distant test takers. The preliminary results demonstrate efficiency of the proposed approach for the same.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atoum, L. Chen, A. Liu, Hsu, S., Liu, X.: Automated online exam proctoring. IEEE Trans. Multim. 19(7), 1609–1624 (2017)
Jung, I., Yeom, H.: Enhanced security for online exams using group cryptography. IEEE Trans. Educ. 52(3), 340–349 (2009)
Upgrad Survey Result Page: www.upgrad.com/online-education-challenges/. Last accessed 2016/01/10
Ranjan, R., Bansal, A., Zheng, J., Xu, H., Gleason, J., Lu, B., Nanduri, A., Chen, J., Castillo C., Chellappa, R.: A fast and accurate system for face detection, identification, and verification. IEEE Trans. Biometrics, Behav., Identity Sci. 1(2), 82–96 (2019)
Tawari, A., Martin, S., Trivedi, M.M.: Continuous head movement estimator for driver assistance: issues, algorithms, and on-road evaluations. IEEE Trans. Intell. Transp. Syst. 15(2), 818–830 (2014)
P. Smith, M. Shah and N. da Vitoria Lobo.: Determining driver visual attention with one camera. IEEE Transactions on Intelligent Transportation Systems, vol. 4, no. 4, pp. 205–218 (2003).
H. Nguyen and A. Caplier.: Local Patterns of Gradients for Face Recognition. IEEE Trans. on Information Forensics and Security, vol. 10, no. 8, pp. 1739–1751 (2015).
A. Kiruluta, M. Eizenman and S. Pasupathy: Predictive head movement tracking using a Kalman filter. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 27, no. 2, pp. 326–331 (1997).
Pantic, M., Rothkrantz, L.J.M.: Facial action recognition for facial expression analysis from static face images. IEEE Trans. Syst. Man Cybern. 34(3), 1449–1461 (2004)
Li, H., Sun, J., Xu, Z., Chen, L.: Multimodal 2D+3D Facial Expression Recognition with Deep Fusion Convolutional Neural Network. IEEE Trans. Multimedia 19(12), 2816–2831 (2017)
Hansen, D.W., Ji, Q.: In the Eye of the Beholder: A Survey of Models for Eyes and Gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)
Pappusetty, D., Kalva, H., Hock, H.S.: Pupil response to quality and content transitions in videos. IEEE Trans. Consum. Electron. 63(4), 410–418 (2017)
Bacivarov, I., Ionita, M., Corcoran, P.: Statistical models of appearance for eye tracking and eye-blink detection and measurement. IEEE Trans. Consum. Electron. 54(3), 1312–1320 (2008)
Zhang, X., Yuan, S., Chen, M., Liu, X.: A Complete system for analysis of video lecture based on eye tracking. IEEE Access 6, 49056–49066 (2018)
Tan, J., et al.: Face detection and verification using lens less cameras. IEEE Trans. Comput. Imaging 5(2), 180–194 (2019). https://doi.org/10.1109/TCI.2018.2889933
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gopane, S., Kotecha, R. (2022). Enhancing Monitoring in Online Exams Using Artificial Intelligence. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_14
Download citation
DOI: https://doi.org/10.1007/978-981-16-5348-3_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5347-6
Online ISBN: 978-981-16-5348-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)