International Conference on VLSI, Communication and Computer Communication
Detection of Malpractice in Offline Examination Using Deep Learning
Manoj A, Insha Mohammed, Teja Swaroop Naidu, Rohith S R, R. Aruna, Department of Electronics and communication Engineering, AMC Engineering college, Bangalore, India.
Exam proctoring is a hectic task i.e.; the monitoring of students' activities becomes difficult for supervisors in the examination rooms. It is a costly approach that requires much labor and difficult task for supervisors to keep an eye on all students at a time. Automatic exam activities recognition is therefore necessitating and a demanding field of research. In this research work, categorization of students' activities during the exam is performed using a deep learning approach. A deep CNN architecture a kernel size of 7 * 7 and 64 different kernels all with a stride of size 2 giving us 1 layer. After that, the model is validated upon ImageNet. In this paper, we present a multimedia analytics system which performs automatic offline exam proctoring. The system hardware includes one webcam for the purpose of monitoring the visual environment of the testing location. To evaluate our proposed system, we collect multimedia (visual) data from many exam centers performing various types of activities while taking exams. Extensive experimental results demonstrate the accuracy, robustness, and efficiency of our offline exam proctoring system.
A Lam, Phan & Phuong Chi, Le & Tuan, Nguyen & Dat, Nguyen & Nguyen, Trung & Anh, Bui & Aftab, Muhammad Umar & Tran, Van Dinh & Son, Ngo. (2019). “A Computer-Vision Based Application for Student Behavior Monitoring in Classroom”. Applied Sciences. 9. 4729. 10.3390/app9224729.
B. Atoum, Yousef & Chen, Liping & Liu, Alex & Hsu, Stephen & Liu, Xiaoming. (2017). “Automated Online Exam Proctoring”. IEEE Transactions on Multimedia. PP. 1-1. 10.1109/TMM.2017.2656064
T. Saba, A. Rehman, N. S. M. Jamail, S. L. Marie-Sainte, M. Raza and M. Sharif, "Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach," in IEEE Access, vol. 9, pp. 47639-47656, 2021, doi: 10.1109/ACCESS.2021.3068223.
González González, C. S., Infante Moro, A., & Infante Moro, J. C. (2020). Implementation of E-Proctoring in Online Teaching: A Study about Motivational Factors. Sustainability, 12(8), 3488. DOI: https://doi.org/10.3390/su12083488.
A. R. Baig and H. Jabeen, "Big data analytics for behavior monitoring of students," Procedia Comput. Sci., vol. 82, pp. 43_48, Jan. 2016.
F. Rodrigues and P. Oliveira, "A system for formative assessment and monitoring of students' progress," [7] Comput. Educ., vol. 76, pp. 30_41, Jul. 2014.
J. Ramberg and B. Modin, "School effectiveness and student cheating: Do students' grades and moral standards matter for this relationship?" Social Psychol. Educ., vol. 22, no. 3, pp. 517_538, Jul. 2019.
Z. A. von Jena, ``The cognitive conditions associated with academic dishonesty in university students and its effect on society,'' UC Merced Undergraduate Res. J., vol. 12, no. 1, pp. 1_21, 2020.
M. Ghizlane, B. Hicham, and F. H. Reda, ``A new model of automatic and continuous online exam monitoring,'' in Proc. Int. Conf. Syst. Collaboration Big Data, Internet Things Secur. (SysCoBIoTS), Dec. 2019, pp. 1_5.
I. Blau and Y. Eshet-Alkalai, “The ethical dissonance in digital and nondigital learning environments: Does technology promotes cheating among middle school students'' Comput. Hum. Behav., vol. 73, pp. 629_637, Aug. 2017.
A. Asrifan, A. Ghofur, and N. Azizah, ``Cheating behavior in EFL classroom (a case study at elementary school in Sidenreng Rappang Regency),'' OKARA, J. Bahasa dan Sastra, vol. 14, no. 2, pp. 279_297, 2020.
P. M. Newton, ``How common is commercial contract cheating in higher education and is it increasing? A systematic review,'' Frontiers Educ., vol. 3, p. 67, Aug. 2018.
A. Bushway andW. R. Nash, ``School cheating behavior,'' Rev. Educ. Res., vol. 47, no. 4, pp. 623_632, Dec. 1977.
Brown, M. T. (2017). Automated Grading of Handwritten Numerical Answers. In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 279-284).
IEEE Cupic, M., Brkic, K., Hrkac, T., Mihajlovic, Z., & Kalafatic, Z. (2014, May). Automatic recognition of handwritten corrections for multiple choice exam answer sheets. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on (pp. 1136-1141).
Cite this article
Manoj A, Insha Mohammed, Teja Swaroop Naidu, Rohith S R, R. Aruna, “Detection of Malpractice in Offline Examination Using Deep Learning”, Advances in Intelligent Systems and Technologies, pp. 158-165, December. 2022. doi: 10.53759/aist/978-9914-9946-1-2_29
