Breskina A. A. / Herald of Advanced Information Technology 2023; Vol.6 No.2: 163–173 ISSN 2663-0176 (Print) ISSN 2663-7731 (Online) Information technology in socio-economic, organizational and technical systems 163 DOI: https://doi.org/10.15276/hait.06.2023.11 UDС 004.032 Development of an automated online proctoring system Anastasiia A. Breskina ORCID: https://orcid.org/0000-0002-3165-6788; anastasia.breskina@gmail.com Odessа Polytechnic National University, 1, Shevchenko Ave. Odessa, 65044, Ukraine ABSTRACT The rapid development of machine learning technologies, the increasing availability of devices and widespread access to the Internet have significantly contributed to the growth of distance learning. Alongside distance learning systems, proctoring systems have emerged to assess student performance by simulating the work of a teacher. However, despite the development of image processing and machine learning technologies, modern proctoring systems still have limited functionality: some systems have not implemented computer vision methods and algorithms satisfactorily enough (false positives when working with students of different ancestry, racial background and nationalities) and classification of student actions (very strict requirements for student behaviour), so that some software products have even refused to use modules that use elements of artificial intelligence. It is also a problem that current systems are mainly focused on tracking students' faces and gaze and do not track their postures, actions , and emotional state. However, it is the assessment of actions and emotional state that is crucial not only for the learning process itself, but also for the well-being of students, as they spend long periods of time at computers or other devices during distance learning, which has a great impact on both their physical health and stress levels. Currently, control over these indicators lies solely with teachers or even students themselves, who have to work through test materials and independent work on their own. An additional problem is the quality of processing and storage of students' personal data, as most systems require students to be identified using their identity documents and store full, unanonymised video of students' work on their servers. Based on the analysis of all these problems that impede the learning process and potentially affect students' health in the long run, this article presents additional functional requirements for modern automated online proctoring systems, including the need to analyse human actions to assess physical activity and monitor hygiene practices when using computers in the learning process, as well as requirements for maximum protection of students' personal data. A prototype of the main components of an automated online proctoring system that meets the proposed requirements has been developed. Keywords: Distance learning; automated online proctoring systems; personal data protection; analysis of people's emotions; analysis of people's actions For citation: Breskina A. A. “Development of an automated online proctoring system”. Herald of Advanced Information Technology. 2023; Vol.6 No.2: 163–173. DOI: https://doi.org/10.15276/hait.06.2023.11 INTRODUCTION Distance learning technologies are not a new field of application that began its development with the de- velopment and spread of computer technologies and the Internet. The problem of assessing students' be- havior in the process of passing exams and complet- ing individual work has always existed. Proctoring systems were proposed to solve this problem. Online proctoring systems are information systems designed to supervise the process of completing test or exami- nation tasks and to monitor and evaluate student in- tegrity. These systems mimic the role of a teacher by observing and evaluating student behavior. Initially, these were synchronous proctoring systems, where students were observed by people (teachers them- selves or hired employees) [1]. To automate the pro- cess and reduce costly expenses, asynchronous proc- toring systems were introduced [2, 3], where the en- tire process of passing test tasks was recorded and © Breskina A., 2023 analysed by the teacher after the exam itself after the fact. However, the development of artificial intelli- gence methods and models has given hope for the automation of this process of assessing students' in- tegrity. It is these information systems, automated online proctoring systems [2, 3], that this paper is de- voted to. LITERATURE REVIEW There is a large number of automated online proctoring systems based on artificial intelligence [4, 5], [6, 7]. According to the peculiarities of these systems implementation and their use on various dis- tance learning platforms, it was proposed to systemize them and divide them into three groups (Fig. 1, Table 1). The first one is plug-ins. Plug-ins that can easily integrate into existing systems. These programs have access to limited functionality within the main plat- form: most run-in browsers and do not have access to the student's entire desktop. However, they have ac- cess to a microphone and camera. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/deed.uk)