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Due to COVID-19, there's been a burst in online examinations. The main integrity safeguard so far is human proctoring, which requires trained supervisors to constantly monitor all test-Takers' videos and audios through webcams. To overcome such costliness and ineffectiveness, we have designed an automated online proctoring system that is effective, nonintrusive, and adaptable to different testing scenarios. Our approach presents a novel combination of (a) a gaze view tracking module using a mathematical 3D gaze motion formula and (b) a configurable cheating classification using a custom-Trained object detection model. Our gaze view tracking leverages two cameras (a webcam and a follower-cam) working in tandem, and facial landmark detection to follow the test-Taker's gaze in a real-Time and non-intrusive manner. It then feeds to our AI-based cheating classifier, which leverages TensorFlow object detection algorithm with a custom-Trained object detection model to identify preconfigured cheating targets. Our end-To-end prototype and trials show effectiveness in tracking the test-Taker's gaze and autodetecting cheating targets. Our system can serve as a great complement to the current online proctor suite and will influence online learning even after pandemics by reducing human toil. © 2022 IEEE.
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The World Health Organization (WHO) declared COVID-19 as a pandemic in early 2020. As a result, the organization has decided to close all educational institutions, and thereby, conventional classroom learning has become obsolete. And as a consequence, exams taken online have become an essential part of an online assessment. The critical issue that arises is how to maintain online exam credibility and student honesty during online exams. In this work, we study the acceptance of online exams by Kuwait University students exposed to online proctoring during the lockdown. We proposed an acceptance model based on the TAM framework but with twelve constructs applied to three proctoring methods: AI proctoring, live human proctoring, and blended proctoring. The data is collected using an online survey from 478 college students. The partial least square structural equation modeling (PLS-SEM) method is used to process the collected data. The findings indicate that live-human and mixed proctoring provide a greater level of satisfaction than AI proctoring alone.
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Online examinations gradually become popular due to Covid 19 pandemic. Environmentally friendly, saving money, and convenient,.. are some of the advantages when taking exams online. Besides its major benefits, online examinations also have some serious adversities, especially integrity and cheating. There are some existing proctoring systems that support anti-cheating, but most of them have a low probability of predicting fraud based on students' gestures and posture. As a result, our article will introduce an online examination called ExamEdu that supports integrity, in which the accuracy of detecting cheating behaviors is 96.09% using transfer learning and fine-tuning for ResNet50 Convolutional Neural Network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background Purpose Method Findings Conclusions COVID‐19 has led to an unprecedented increase in the use of technology for teaching and learning in higher education institutions (HEIs), including in engineering, computing, and technology programs. Given the urgency of the situation, technologies were often implemented with a short‐term rather than long‐term view.In this study, we investigate students' perceptions of the use of video‐based monitoring (VbM) for proctoring exams to better assess its impact on students. We leverage technological ambivalence as a framing lens to analyze students' experiences and perceptions of using VbM and draw implications for responsible use of educational technology.Qualitative data were collected from students using focus group interviews and discussion board assignments and analyzed inductively to understand students' experiences.We present a framework of how a technological shift of existing practice triggered ambivalence that manifested itself as a sustained negative outlook among students regarding the use of VbM, as well as their institution and instructors. Students accepted the inevitability of the technology but were unconvinced that the benefits of VbM outweighed its risks.As instructors use educational technologies that are inherently driven by user data and algorithms that are not transparent, it is imperative that they are attentive to the responsible use of technology. To educate future engineers who are ethically and morally responsible, engineering educators and engineering institutions need to exhibit that behavior in their own practices, starting with their use of educational technologies. [ FROM AUTHOR]
ABSTRACT
Importance of online education can be seen especially during the ongoing Covid-19 when going to schools or colleges is not possible. So validity of online exams should also be maintained with respect to traditional pen-paper examinations. However, absence of invigilator makes it easy for the examinees to cheat during the exam. Though there are already many systems for online proctoring, not all educational institutes can afford them as the systems are very expensive. In this paper, we have used eye gaze and head pose estimation as the main features to design our online proctoring system. Therefore, the purpose of this paper is to use these features to create an online proctoring system using computer vision and machine learning and stop cheating attempts in exams. © 2021 IEEE.
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Online exams have become increasingly popular in recent years for assessing students' content knowledge, especially during the COVID-19 outbreak. Proctoring for online tests is, however, challenging due to the lack of a face-to-face connection. Furthermore, according to a prior study, online assessments are more vulnerable to various types of cheating, putting their validity at risk. Suspicious student head and mouse movements are identified and depicted in three degrees of detail, allowing course instructors and professors to proctor online tests in a rapid, fast, and trustworthy manner. Our thorough evaluations, which include usage scenarios, well-designed user research, and expert interviews, indicate that our method is effective and practical. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Online proctoring has become a necessity in online teaching. Video-based crowd-sourced online proctoring solutions are being used, where an exam-taking student's video is moni-tored by third-parties, leading to privacy concerns. In this paper, we propose a privacy-preserving online proctoring system. The proposed image-hashing-based system can detect the student's excessive face and body movement (i.e., anomalies) that is resulted when the student tries to cheat in the exam. The detection can be done even if the student's face is blurred or masked in video frames. Experiment with an in-house dataset shows the usability of the proposed system. © 2022 IEEE.
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Internet-based client authentication protocols must be strengthened to reduce attacks and security vulnerabilities that threaten the performance of apps in fast internet distribution and cloud computing. Due to a multitude of benefits such as effectiveness, convenience, simplicity, and usability, distance and digital training (called e-learning) seems to have become the mainstream in skills and retraining. Secondly, because just like the COVID-19 pandemic's physical isolation rules, online learning has now become the exclusive possibility. Due to the lack of physical existence, however, online systems are a major issue in monitoring attendees and students over sessions, particularly during tests. It is necessary to establish technological tools that deliver survey clearly for monitoring unfair, unethical, and unauthorized behavior in classes and examinations. In this dissertation, we develop a modern online proctoring system based on machine learning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background Objectives Methods Results and Conclusions The Covid‐19 pandemic disrupted higher education in many ways, such as the move to Emergency Remote Online Teaching and Learning (EROTL), often including a move to online assessments and examinations. With evidence of increased academic dishonesty in unproctored online assessment, institutions sought ways to ensure academic and institutional integrity and reputation. In doing this, many institutions selected and implemented online proctoring solutions.This article maps considerations of online proctoring solutions in the nexus between ensuring academic and institutional integrity and reputation, and addressing stakeholder concerns regarding invasive surveillance and the impacts on student privacy.The study involved a PRISMA‐informed systematic review of three digital libraries, namely Clarivate's Web of Science, Elsevier's Scopus, and Springer's SpringerLink, for peer‐reviewed journal articles and conference proceedings. After screening, a final corpus of 27 articles was analysed.The findings include evidence that, in the midst of the Covid‐19 pandemic, higher education institutions were largely influenced by cost, usability and efficiency in choosing online proctoring solutions to ensure academic and institutional integrity. Student privacy was either considered in terms of data protection and transparency, or not at all. This article aims to provide valuable insight into the criteria used to select online proctoring solutions to ensure academic and institutional integrity in online examination environments. Student privacy appears not to have the consideration it warrants. [ FROM AUTHOR] Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Examination malpractice is a deliberate wrong doing contrary to official examina-tion rules designed to place a candidate at unfair advantage or disadvantage. The proposed system depicts a new use of technology to identify malpractice in E-Exams which is essential due to growth of online education. The current solu-tions for such a problem either require complete manual labor or have various vulnerabilities that can be exploited by an examinee. The proposed application en-compasses an end-to-end system that assists an examiner/evaluator in deciding whether a student passes an online exam without any probable attempts of mal-practice or cheating in e-exams with the help of visual aids. The system works by categorizing the student's VFOA (visual focus of attention) data by capturing the head pose estimates and eye gaze estimates using state-of-the-art machine learn-ing techniques. The system only requires the student (test-taker) to have a func-tioning internet connection along with a webcam to transmit the feed. The exam-iner is alerted when the student wavers in his VFOA, from the screen greater than X, a predefined threshold of times. If this threshold X is crossed, the appli-cation will save the data of the person when his VFOA is off the screen and send it to the examiner to be manually checked and marked whether the action per-formed by the student was an attempt at malpractice or just momentary lapse in concentration. The system use a hybrid classifier approach where two different classifiers are used, one when gaze values are being read successfully (which may fail due to various reasons like transmission quality or glare from his specta-cles), the model falls back to the default classifier which only reads the head pose values to classify the attention metric, which is used to map the student's VFOA to check the likelihood of malpractice. The model has achieved an accuracy of 96.04 percent in classifying the attention metric.
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ISSUE: Technology is pervasive in medicine, but we too rarely examine how it shapes assessment, learning, knowledge, and performance. Cultures of assessment also shape identities, social relations, and the knowledge and behavior recognized as legitimate by a profession. Therefore, the combination of technology and assessment within medical education is worthy of review. Online proctoring services have become more prevalent during the Covid-19 pandemic, as a means of continuing high-stakes invigilated examinations online. With criticisms about increased surveillance, discrimination, and the outsourcing of control to commercial vendors, is this simply "moving exams online", or are there more serious implications? What can this extreme example tell us about how our technologies of assessment influence relationships between trainees and medical education institutions? EVIDENCE: We combine postdigital and postphenomenology approaches to analyze the written component of the 2020 online proctored United Kingdom Royal College of Physicians (MRCP) membership exam. We examine the scripts, norms, and trust relations produced through this example of online proctoring, and then locate them in historical and economic contexts. We find that the proctoring service projects a false objectivity that is undermined by the tight script with which examinees must comply in an intensified norm of surveillance, and by the interpretation of digital data by unseen human proctors. Nonetheless, such proctoring services are promoted by an image of data-driven innovation, a rhetoric of necessity in response to a growing problem of online cheating, and an aversion, within medical education institutions, to changing assessment formats (and thus the need to accept different forms of knowledge as legitimate). IMPLICATIONS: The use of online proctoring technology by medical education institutions intensifies established norms, already present within examinations, of surveillance and distrust. Moreover, it exacerbates tensions between conflicting agendas of commercialization, accountability, and the education of trustworthy professionals. Our analysis provides an example of why it is important to stop and consider the holistic implications of introducing technological "solutions", and to interrogate the intersection of technology and assessment practices in relation to the wider goals of medical education.
Subject(s)
COVID-19 , Education, Medical , COVID-19/diagnosis , Humans , Pandemics , Technology , TrustABSTRACT
The COVID-19 pandemic caused many educational institutions in the world to switch to the distance education process, and this process was called "Emergency Remote Teaching". This urgent transition process has caused many problems in educational environments. One of the problems is the subject of measurement and evaluation. Along with the pandemic, many institutions have used various online assessment systems to make measurements and evaluations online, and researchers have conducted research on these online assessment systems. This research focus on the features of the online assessment systems and aims to examine the trends towards the prominent features of the online assessment systems in the Emergency Remote Teaching period. For this purpose, the prominent online assessment systems have been determined by systematically analyzing academic studies published in 2020, and answers have been sought to the following research questions: (1) which platforms they support, (2) which security features they have, and (3) what common features they have. Identifying trends in the characteristics of online assessment systems is expected to guide practitioners, decision-makers, researchers, and system developers in the process of selecting and/or developing an online assessment system for use in online measurement and evaluation.
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Online exams have become widely used to evaluate students' performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.
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Critics of artificial intelligence have suggested that the principles of fairness, accountability and transparency (FATE) have been used for ‘ethics washing’, in order to appease industrial interests. In this article, we develop this relational and context-dependent analysis, arguing that ethics should not be understood as values or design decisions, but as socio-technical achievements, enacted in the practices of students, teachers and corporations. We propose that the ethics of using AI in education are political, involving the distribution of power, privilege and resources. To illustrate this, we trace the controversies that followed from an incident in which a student was misclassified as a cheat by an online proctoring platform during the Covid-19 lockdown, analysing this incident to reveal the socio-technical arrangements of academic integrity. We then show how Joan Tronto’s work on the ethics of care can help think about the politics of these socio-technical arrangements — that is, about historically constituted power relations and the delegation of responsibilities within these institutions. The paper concludes by setting the immediate need for restorative justice against the slower temporality of systemic failure, and inviting speculation that could create new relationships between universities, students, businesses, algorithms and the idea of academic integrity. © 2021, The Author(s).
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Relevance. In the face of the COVID-19 pandemic, universities all over the world had to deal with a major challenge transition from face-to-face to online learning. It was necessary to make this transition without damaging the quality of education and the transparency of examinations, especially entrance examinations taken by international students. The number of the latter fell significantly because of the pandemic and the competition for overseas students became especially fierce. One of the optimal solutions to the problem of conducting entrance exams during the pandemic was the online proctoring system. Research objective. This research aims to assess the economic efficiency of the online proctoring system by looking at the case of the Moscow Institute of Physics and Technology (MIPT). Data and methods. The article compares the most popular online proctoring systems on the market and used by universities in Russia and other country. Furthermore, it analyzes the results of the international admission campaign in 2020 and the economic effect of the in-house proctoring system in comparison with other readymade solutions. Results. The research results showed that the MIPT’s in-house proctoring system is no less efficient than the most popular readymade systems used by the majority of universities in Russia and worldwide, yet the costs of developing and operating the university’s own system are significantly lower. Conclusion. The development of an in-house online proctoring system can increase the economic efficiency of universities in terms of international admission in the forthcoming years. © Oykher, A.D., 2021.
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There have been giant leaps in the field of education in the past 1-2 years.. Schools and colleges are transitioning online to provide more resources to their students. The COVID-19 pandemic has provided students more opportunities to learn and improve themselves at their own pace. Online proctoring services (part of assessment) are also on the rise, and AI-based proctoring systems (henceforth called as AIPS) have taken the market by storm. Online proctoring systems (henceforth called as OPS), in general, makes use of online tools to maintain the sanctity of the examination. While most of this software uses various modules, the sensitive information they collect raises concerns among the student community. There are various psychological, cultural and technological parameters need to be considered while developing AIPS. This paper systematically reviews existing AI and non-AI-based proctoring systems. Through the systematic search on Scopus, Web of Science and ERIC repositories, 43 paper were listed out from the year 2015 to 2021. We addressed 4 primary research questions which were focusing on existing architecture of AIPS, Parameters to be considered for AIPS, trends and Issues in AIPS and Future of AIPS. Our 360-degree analysis on OPS and AIPS reveals that security issues associated with AIPS are multiplying and are a cause of legitimate concern. Major issues include Security and Privacy concerns, ethical concerns, Trust in AI-based technology, lack of training among usage of technology, cost and many more. It is difficult to know whether the benefits of these Online Proctoring technologies outweigh their risks. The most reasonable conclusion we can reach in the present is that the ethical justification of these technologies and their various capabilities requires us to rigorously ensure that a balance is struck between the concerns with the possible benefits to the best of our abilities. To the best of our knowledge, there is no such analysis on AIPS and OPS. Our work further addresses the issues in AIPS in human and technological aspect. It also lists out key points and new technologies that have only recently been introduced but could significantly impact online education and OPS in the years to come.