MMAP: A Multi-Modal Automated Online Proctor

Gadekar, Aumkar; Oak, Shreya; Revadekar, Abhishek; Nimkar, Anant V.

doi:10.1007/978-3-030-82469-3_28

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The features include authentication, head movement, eye movement tracking, and speech and object recognition. The overall accuracy of the system is 91% [17]. In the article, the authors propose an online proctoring system that ensures confidentiality.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan

et al. 2023

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The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system.

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Section: Literature Reviewmentioning

confidence: 99%

Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan

et al. 2023

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“…In 2011, the United Nations Economic Commission for Europe (UNECE) approved the Strategy in Education for Sustainable Development (ESD) that encourages regional monitoring as a practical management approach used by educators, including school teachers, as well as the heads and specialists of municipal educational departments [29]. Implementing proctored online examinations has been suggested to analyze and discourage academic dishonesty by identifying and addressing malicious activities among participants [30], [31]. A pool of questions can be uploaded, and AI is used to detect any suspicious behaviors and cheating attempts during an online examination so that the questions can be randomized or chosen from different pools of questions.…”

Section: ) Academic Dishonestymentioning

confidence: 99%

College Studentsâ€™ Perception and Concerns regarding Online Examination amid COVID-19

Lee,

Ling,

Alvin Yau

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Growing concerns about online examinations have led to various investigations of techniques for improvement. With most higher education institutions shifting to online learning and examination amid COVID-19, these concerns, including the academic dishonesty, validity, reliability, and anxiety of online examination, are more critical than ever. This paper presents the outcomes of the survey to elicit the perceptions of undergraduate students from two universities in South Korea and Malaysia towards undertaking online exams and the associated concerns. Additionally, the study explores the potential of artificial intelligence (AI) in addressing these concerns. There are three main research questions: 1) How has AI been adopted to tackle the four main concerns in online exams? 2) What are the studentsâ€™ perceptions regarding these concerns? Are there any differences between South Korean and Malaysian students? 3) What is the extent of the stress level when webcam proctoring and timers are implemented during online exams? The survey results show that both South Korean and Malaysian students agree that online exams make cheating more accessible than in-person exams. They also suggest that selecting questions randomly from a question bank could discourage cheating. Moreover, the study highlights that both groups of students experience moderate stress levels when webcam proctoring is used over Zoom during online exams, and they experience a high-stress level when timers are set for each question.

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Automated smart artificial intelligence-based proctoring system using deep learning

et al. 2023

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MMAP: A Multi-Modal Automated Online Proctor

Cited by 3 publications

References 19 publications

Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan

Research on the Development of a Proctoring System for Conducting Online Exams in Kazakhstan

College Studentsâ€™ Perception and Concerns regarding Online Examination amid COVID-19

Automated smart artificial intelligence-based proctoring system using deep learning

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