ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 9. Vol. 30. 2024

DOI: 10.17587/it.30.474-479

M. V. Guner^1,2, Graduate Student, Senior Teacher,A. N. Safonova¹, Ph.D. Tech. Sciences, Associate Professor, Yu. A. Maglynets¹, Ph.D. Tech. Sciences, Professor,
¹ Federal State Autonomous Educational Institution of Higher Education "Siberian Federal University", Krasnoyarsk, Russian Federation,
² Federal State Budgetary Educational Institution of Higher Education "Altai State Technical University named after. I. I. Polzunov", Barnaul, Russian Federation

Assessment of Independence in Passing Entrance Online Testing by Analyzing the Applicant's Head Rotation Angle Based on Neural Network Image Recognition Methods

The work is devoted to the construction of a model for assessing the independence of applicants in passing an online entrance test based on an analysis of the direction of their gaze using neural network image recognition methods. The normalized deviation in the rating of this person before and after enrollment was taken as a metric for assessing the applicant's independence in passing the online entrance test in mathematics. All experiments presented in the work were carried out on real data. Our collection of images was an array of frames from web cameras of553 applicants while they were taking an online entrance exam in mathematics. As part of data preprocessing, images with poor quality were removed from the collection, as well as images where there were two or more people in the frame, or the applicant's head did not fit completely into the frame (less than 90 %). The final size of the collection was 3828 examples. The RealHePoNet convolutional neural network (CNN), proposed by Spanish researchers, was used as a detector of the incoming person's head and a method for determining the direction of his gaze. The accuracy of detecting the head of incoming images from our collection was 82.89 %. By analyzing the area and aspect ratio of the rectangles that, according to the RealHePoNet CNN, limit the incoming head, we managed to increase the detection accuracy to 97.81 %. When the average value of the incoming head rotation angle exceeds the threshold of 6.8°, the average normalized deviation in the rating before and after enrollment becomes negative, and when it exceeds 10.2°, this deviation is less than —2 points, which indicates an increased likelihood of being recruited to take the exam strangers. The average angle of the applicant's head tilt is over 46°, just like the head lift over 18°, may also indicate fraudulent actions on the part of the person taking the exam.

Keywords: image recognition, neural networks, information technology in education, admissions campaign, online testing, assessment of applicants' knowledge

P. 474-479

Acknowlegements: The research was carried out with the support of the Krasnoyarsk Regional Fund for Support of Scientific and Scientific-Technical Activities within the framework of the project "Digital import-substituting technology for detecting forest fires based on the use of the unique Russian satellite constellation Arctic-M with subsequent integration with the ISDM-Rosleskhoz system" No. 2022021409612. Works supported from grant funds in the form of a subsidy from the REC "Yenisei Siberia" No. 075-15-2022-620 dated August 29, 2022

References

1. Berral-Soler R., Madrid-Cuevas F. J., Munoz-Salinas R., Marin-Jimenez M. J. RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild, Arxiv , 2020, available at: https://arxiv.org/abs/2011.01890v1, https://doi.org/10.48550/arXiv.2011.01890.

2. Safonova A., Guner M. Factor Analysis of Students’ Knowledge Assessment Based on the Results of Online Entrance Testing in Mathematics to the University under the Conditions of COVID-19, MDPI Education Science, 2023, vol. 13, no. 1, pp. 46, available at: https://www.mdpi.com/2227-7102/13/1/46, https://doi.org/10.3390/educsci13010046.

3. Noorbehbahani F., Mohammadi A., Aminazadeh M. A systematic review of research on cheating in online exams from 2010 to 2021, Education and Information Technologies, 2022, vol. 27, available at: https://doi.org/10.1007/s10639-022-10927-7.

4. Gorgani H. H., Shabani S. Online exams and the COVID-19 pandemic: a hybrid modified FMEA, QFD, and k-means approach to enhance fairness, SN Applied Sciences, 2021, vol. 3, pp. 818, available at: https://doi.org/10.1007/s42452-021-04805-z.

5. Hylton K., Levy Y., Laurie P. Dringus Utilizing webcambased proctoring to deter misconduct in online exams, Computers & Education , Volumes 92—93, January—February 2016, pp. 53—63, available at: https://doi.org/10.1016/j.compedu.2015.10.002.

6. Dendir S., Maxwell R. S. Cheating in online courses: Evidence from online proctoring, Computers in Human Behavior Reports , August—December 2020, vol. 2, pp. 100033, available at: https://doi.org/10.1016/j.chbr.2020.100033.

7. Susithra V., Reshma A., Bishruti Gope, Sankar S. Detection of Anomalous Behaviour in Online Exam towards utomated Proctoring, 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) , Puducherry, India, 30-31 July 2021, available at: https://doi.org/10.1109/ICSCAN53069.2021.9526448.

8. Hossain Z. T., Roy P., Nasir R., Nawsheen S., Hossain M. I. Automated Online Exam Proctoring System Using Computer Vision and Hybrid ML Classifier, 2021 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON) , Dhaka, Bangladesh, 03-04 December 2021, available at: https://doi.org/10.1109/RAAICON54709.2021.9929456.

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